Climate Change Impacts on Water Resources Systems

Proceedings of National Seminar on

Climate Change Impacts on Water Resources Systems

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Proceedings of National Seminar on

Climate Change Impacts on Water Resources Systems 27th-29th November 2013 Editor

Dr. D.T. Shete

Organized by Civil Engineering Department Parul Institute of Engineering and Technology Limda, Ta. Waghodia, Dist. Vadodara

Supported by (COE TEQIP II) Sardar Vallabhbhai National Institute of Technology Surat

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First Impression: 2013 © Parul Institute of Engineering and Technology, Limda, Vadodara National Seminar on Climate Change Impacts on Water Resources Systems

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PREFACE With all the humbleness at my command, I present here the proceedings of the National Seminar on “Climate Change

Impacts on Water Resources Systems” organized by the Civil Engineering Department, Parul Institute of Engineering and technology–Degree, Limda, Ta Waghodia, dist Vadodara and supported by (CoE–TEQIP II) Sardar Vallabhbhai National Institute of Technology, Surat on 27th–29th November, 2013 at Limda. The idea to organize this Seminar was a result of a shock. The shock came when the International Polar Year, a global consortium studying the Artic, froze a small vessel into the sea ice off eastern Siberia in September 2006. Norwegian explorer Fridrj of Nansen had experienced the same a century ago and his Farm, carried by drifting ice, emerged off eastern Greenland 34 months later. IPY scientists thought their Tara would take 24–36 months. But it reached Greenland in just 14 months, stark evidence that the sea found a more open, ice free, and thus faster path westward thanks to Arctic melting. The loss of Arctic sea ice is well ahead of what the Intergovernmental Panel on Climate Change forecast, largely because emissions of carbon dioxide have topped what the panel projected. “The models just are not keeping up” with the reality of CO2 emissions, says IPY’s David Carlsen. Although policy makers hoped climate models would prove to be alarmists, the opposite is true. Scientists have known that permafrost, if it melted, would release carbon, exacerbating global warming, which would melt more permafrost, which would add more to global warming, on and on in a feedback loop. But estimates of how much carbon is locked into Arctic permafrost were woefully off. “It is about three times as much as was thought, about 1.6 trillion tones” said Edward Schuur of the University of Florida. And he warned that 1–2 billion tones per year would be the rate at which CO2 would be released from permafrost. Compare this with 300 million tones/ year emission of CO2 through cars and light trucks in the USA. The irony is that the more respectable geo engineering option, carbon capture, is also by far the more expensive and less likely to counteract a steep rise in temperatures. Each year approximately 30 billion tones of CO2 are released by the world’s industries and autos. Scientists still think there is enough porous rock deep beneath the earth’s surface to accommodate all the liquid CO2 one can pump, but getting it there would take many years and cost billions. Still there is a hope. David Keith, a physicist at the University of Calgary, is working on designing particles that are more efficient at cooling than sulphates but without side effects. Keith’s engineered particles would absorb the sun’s energy unevenly, causing one side to heat more quickly than the other and to drift upward. Such a particle might be released on the ground and rise up on its own accord, higher than the ozone layer-to the mesosphere, 100km up–where it would reflect light but leave the ozone intact. Wishing to be squirrel in the mammoth endeavour to find always evading solution to the Climate Change Impacts, it was decided to first understand the Climate Change Impacts on Water Resources Systems in the presence and guidance of the eminent experts on the subject, who will enlighten the research scholars, post graduate students, professionals and faculty of Engineering and Agricultural Engineering colleges by their own research first and then join the audience in encouraging the researchers and professionals who will present their research work/ case studies. All the received papers totaling 40, were critically scrutinized by the elite Peer Reviewers from IISc, IITs, NITs, Agricultural Engineering Colleges of very high repute and topmost scientists of NIH. I take this opportunity to put on record my heartfelt thanks to everyone of them. I am very grateful to our Chairman and Managing Trustee, Dr. Jayeshbhai K. Patel, who was the constant source of inspiration to all of us in our every endeavour to better our past performance. I will never forget the generosity of Dr. P.D. Porey, Director, Sardar Vallabhbhai national Institute of Technology, Surat in supporting this Seminar wholeheartedly.

Who will forget our dynamic, enthusiastic and young Trustee, Dr. Devanshubhai J. Patel as he always cooperated with me in organizing this Seminar. Dr. R.H. Pandya, our Adviser, always extended his hand for any help I asked for. I am really impressed by his sense of devotion to the cause when he visualized what else I would need and provided the same on my table. Dr. Vilin Parekh, Principal and Adviser to the Seminar, showed faith in me and entrusted the responsibility of organizing this prestigious Seminar to me. Whenever I needed his advice and help, he was always there. I sincerely thank All India Council for Technical Education, New Delhi and Gujarat Council on Science and Technology & Department of Science and Technology, Gandhinagar for the generous grant provided by them, without which the National Seminar could not be possible. Last but not the least, I will be ungrateful if I would not put on record the assistance rendered by Dr. P.L. Patel, HOD, Civil Engineering Department, SVNIT, Surat in making the Seminar a successful one. I take this opportunity to express my heartfelt thanks to all my colleagues in my Department, the administrative and account staff of Admin, Shri Ashish Andharia and Shri Kamalesh Marathe for designing the Banners, Brochure and the cover page of this proceedings. I wish this Seminar the great success.

Dr. D.T. Shete Convener NSCCIWRS Civil Engineering Department Parul Institute of Engineering and Technology-Degree Limda 27th November, 2013

vi

Preface

FROM THE DESK OF CHAIRMAN Water resources are important to both society and ecosystems. A reliable and clean supply of drinking water sustains the health of living beings. Water is also needed for agriculture, energy production, navigation, recreation, and manufacturing. Many of these uses put pressure on water resources, stresses that are likely to be exacerbated by climate change. In many areas, climate change is likely to increase water demand while shrinking water supplies. This shifting balance would challenge water managers to simultaneously meet the needs of growing communities, sensitive ecosystems, farmers, ranchers, energy producers, and manufacturers. In some areas, water shortages will be less of a problem than increases in runoff, flooding, or sea level rise. These effects can reduce the quality of water and can damage the infrastructure that we use to transport and deliver water. At Parul Arogya Seva Mandal we strive to ensure the rights of “Education, employment and Health” to the most unprivileged strata of the society. When the quality of water is compromised due to climate change impacts, this unprivileged strata of the society becomes the most vulnerable to the health hazards. Therefore, we initiated to organize a prestigious national seminar on this topic. I heartily welcome you to the intensive brain storming sessions of this seminar.

Dr. Jayeshbhai K. Patel Managing Trustee Parul Arogya Seva Mandal Limda 27th November, 2013

FROM THE DESK OF DIRECTOR SVNIT SURAT I am pleased to know that Parul Institute of Engineering and Technology (PIET) in association with Sardar Vallabhbhai National of Technology Surat is organizing a National Seminar on ‘Climate change impacts on water resources systems’ during Nov. 27-29, 2013. The seminar is expected to give important recommendation and on resolving the burning problem of ‘climate change’ for the whole world. The prediction of climate change; and availability of water resources in the river basins has been given the top priority by Ministry of Water Resources, Govt. of India in the recent past. Also, the seminar is expected to resolve the issues of extremes (floods or droughts) due to changing climate conditions across the globe. I convey my best wishes to the organizers for the success of the seminar.

Prof. P.D. Porey Director SVNIT, Surat 27th November, 2013

FROM THE DESK OF MANAGING TRUSTEE Most of the educational Institutes concentrate on the holistic development of the students. We at the Parul Arogya Seva Mandal not only cater to the needs of the students and Faculty but also strive hard to fulfil the aspirations of the society. The burning topic today faced by one and all is the Climate Change Impacts on Water Resources Systems. Changes in the amount of rain fall during storms provide evidence that the hydrological cycle is already changing. Over the past 50 years, the amounts of rain fall during the most intense 1% of storms increased by almost 20%. Warming winter temperatures cause more precipitation to fall as rain rather than snow. Furthermore, rising temperatures cause snow to begin melting earlier in the year. This alters the timing of stream flow in rivers that have their sources in mountainous areas. As temperatures rise, people and animals need more water to maintain their health and thrive. Many important economic activities, like producing energy at power plants, raising livestock, and growing food crops, also require water. The amount of water available for these activities may be reduced as Earth warms, and if competition for water resources increases. It is our small but significant first step towards facilitating academicians and professionals to come together in brain storming on various issues involved in mitigating the challenge posed by the Climate Change Impacts. I welcome you, one and all to be the part of this historic National Seminar.

Dr. Devanshubhai J. Patel Trustee Parul Arogya Seva Mandal Limda 27th November, 2013

FROM THE DESK OF ADVISER When the well is dry, we know the worth of water. —Benjamin Franklin Poor Richard’s Almanac, 1746 One of the major scientific challenges of the latter part of the 20th century and start of the 21st Century is Global Climate Change and its Impacts on Civilization. Climate change will alter the hydrological cycle in many ways. The trigger is the warming of the atmosphere and oceans, which will change major weather systems. This will alter the temporal and spatial patterns of rainfall with on sequences for runoff, surface and groundwater storage, river flow regimes and, it is estimated, greater likelihood of extremes–droughts and floods–in different parts of the world. Climate change is profoundly changing Earth’s hydrology, giving us too much water in some places, but too little water in others; giving us too much water at one time, but not enough water over time; and giving us rain, but not snow. Perhaps the greatest challenge posed by climate change is deciding how to adjust our water resource use to address the “new hydrology.” Alterations in hydrology caused by climate change are complex. Climate and water resources are intimately connected through the hydrologic cycle which defines the transfer of water from the oceans to groundwater and surface water storage. The need of the hour is to respond to the needs of communities, for livelihoods and cultures alter as a result of climate change and water scarcity. We have to ensure availability of information and learning opportunities for income diversification in the semi-arid climate, and improve the access to education on a broader scale for poor and vulnerable people There is a need to improve understanding and modeling of climate changes related to the hydrological cycle at scales relevant to decision making. Information about the water-related impacts of climate change is inadequate– especially with respect to water quality, aquatic ecosystems and groundwater–including their socio-economic dimensions. As the world continues its current patterns of production and consumption, the future is at great risk. It is no longer possible for us to seek solutions for individual problems in an isolated manner. Meeting challenges in climate and water calls for, among other things, switching to food systems that conserve water and that are net emission mitigators. Today’s leaders have the opportunity to invest in multifunctional agricultural systems and agro-ecological practices that will help mitigate climate change problems, help conserve land and water resources, and simultaneously build up vibrant rural communities for whom agriculture is a rewarding way of life. We know how to chart this path. What is most needed is the collective political will to move in a direction that is sustainable, equitable and fair. I wish this National Seminar a grand success.

Dr. Vilin Parekh Adviser, NSCCIWRS Principal PIET, Limda 27th November, 2013

FROM THE DESK OF COCONVENER It is predicted that in a warmer environment, more precipitation will occur as rain rather than snow. Although more rain than snow may seem like an advantage, it could mean more frequent water shortages. When snow and ice collect on mountain peaks, water is released slowly into reservoirs as it melts throughout the spring and summer. When rain falls, reservoirs fill quickly to the brim and can result in excess runoff that cannot be accommodated. As rain flows faster than the melting snow, higher levels of soil moisture and ground water recharge are less likely to achieve. Areas like Lahul and Spiti in Himachal Pradesh that rely on snowmelt as their primary fresh water source could increasingly experience water shortages. The relationship between climate change and water resources systems does not end here. The systems used to treat and move public water supplies require large amounts of energy, produced mainly by burning coal, natural gas, oil and other fossil fuels. So when one uses water she also uses energy and contributes to climate change. To understand how to break this vicious circle, a National Seminar is organized by Civil Engineering Department of Parul Institute of Engineering and Technology. I welcome all of you to this unique Seminar where there are brain storming sessions, panel discussions along with usual technical sessions.

Prof. Suhasini M. Kulkarni Co-Convener NSCCIWRS and Head, Civil Engineering Department, PIET, Limda 27th November, 2013

National Seminar on

CLIMATE CHANGE IMPACTS ON WATER RESOURCES SYSTEMS 27th–29th November 2013

Organized by Civil Engineering Department Parul Institute of Engineering & Technology-Degree Limda, Ta. Waghodia, Dist. Vadodara–391760

Supported by (COE TEQIP II) Sardar Vallabhbhai National Institute of Technology Surat

Chief Patron Dr. J.K. Patel Chairman, PASM

Patrons Dr. D.J. Patel Managing Trustee, PASM

Dr. P.D. Porey Director, SVNIT

Adviser Dr. Vilin Parekh Principal Parul Institute of Engg. & Tech.-Degree

Convener Dr. D.T. Shete Professor Civil Engineering Department Parul Institute of Engg. & Tech.-Degree

Co-Convener Prof. S.M. Kulkarni Head Civil Engineering Department, Parul Institute of Engg. & Tech.-Degree

Coordinator Prof. S.M. Damodariya

Co-Coordinators Prof. D.A. Shah Prof. Shilpa Pathak

Prof. Mehul Sharma Prof. Parthi Parikh

Organising Committee Prof. Chetan Kulkarni Prof. Arpita Patel Prof. Arpit Parikh

Transportation Committee Prof. Nazim Chinwala Prof. Ashish Meeruti

Registrartion Committee Prof. Chaitali Shah Prof. Ankita Shah

Hospitality Committee Prof. Amit Patel Prof. Rajan Lad Prof. Nidhi Shah

Catering Committee Prof. Ekta Sharma Prof. Dhara Kalsariya

Inauguration Committee Prof. Neha Shah Prof. Meghan Kemkar Prof. Kushal Parmar

Technical Committee Prof. Medha Kemkar Prof. Nehal Shah Prof. Santok Khunti

National Advisory Committee Chairman Dr. M.C. Varshneya Former V.C., AAU, Anand

Co-Chairman Dr. Deepak Kashyap IIT Roorkee, Roorkee Dr. P.P. Mujumdar IISc, Bengaluru Dr. Anil Kulkarni IISc, Bengaluru Dr. D. Nagesh Kumar IISc, Bengaluru Dr. A.K. Gosain IIT Delhi, Delhi Dr. M. Dineshkumar IRAP, Hyderabad

Dr. T.I. Eldho IIT Bombay, Mumbai Dr. M. Behara IIT Bombay, Mumbai Dr. Rajesh Gupta VNIT, Nagpur Dr. Tanweer Deshmukh MANIT, Bhopal Dr. P.L. Patel SVNIT, Surat

Dr. Deepak Khare IIT Roorkee, Roorkee Dr. M.L. Kansal IIT Roorkee, Roorkee Dr. D.S. Arya IIT Roorkee, Roorkee Dr. S.K. Jain IIT Roorkee, Roorkee Dr. Sanjay Jain NIH, Roorkee

Speakers Dr. M.C. Varshneya Former V.C., AAU, Anand Dr. P.P. Mujumdar IISc, Bengaluru Dr. M.L. Kansal IIT Roorkee, Roorkee Dr. D.S. Arya IIT Roorkee, Roorkee Shri N.K. Bhandari SSCAC, Vadodara

Dr. M. Dineshkumar IRAP, Gandhinagar Dr. M.B. Joshi SSNNL, Gandhinagar Dr. J.S. Pandey NEERI, Nagpur

Dr. D.T. Shete PIET, Limda Dr. P.L. Patel SVNIT, Surat Shri R.K. Suryavanshi CWC, Gandhinagar Dr. S. Venkataraman IMD, Pune

Technical Papers Review Committee Chairman Dr. P.P. Mujumdar IISc, Bengaluru Dr. Eldho T.I. IIT Bombay, Mumbai Dr. Manasa Ranjan Behara IIT Bombay, Mumbai Dr. Deepak Kashyap IIT Roorkee, Roorkee Dr. Deepak Khare IIT Roorkee, Roorkee Dr. S.K. Jain IIT Roorkee, Roorkee

Dr. A.K. Gosain IIT Delhi, Delhi Dr. Sanjay Jain NIH, Rorkee Dr. Deepa Chalisgaokar NIH, Roorkee Dr. R.P. Pandey NIH, Roorkee Dr. Rajesh Gupta VNIT, Nagpur

Dr. D. Nageshkumar IISc, Bengaluru Dr. B. Venkateswara Rao JNTU, Hyderabad Dr. N.S. Murty G.B. Pant Uni. of Agri. & Tech., Pantnagar Dr. J.K. Neelakanth WMRC, Dharwad Dr. Tanweer Deshmukh MANIT, Bhopal

Contents Preface Messages Committees Speakers

v vii xii xiv

AGRICULTURE

1. Effect of Climate Change on Agriculture M.C. Varshneya and V.B. Vaidya

3

2. Increasing Gross Crop Yields in Warming Atmosphere by Optimal Irrigation S. Venkataraman

10

3. Impacts of Climate Change on Growth and Yield of Rice in Middle Gujarat Agro-Climatic Region H.R. Patel, S.B. Yadav, M.M. Lunagaria, P.K. Parmar, N.J. Chaudhari, B.I. Karande and V. Pandey

13

4. Climate Change Impact on Crop Water Requirements in South Saurashtra Region of Gujarat State Harji D. Rank, Pankaj J. Rathod and Hiren P. Patel

18

5. Drainage Coefficient using Probablistic Analysis for Patan, Siddhpur and Wagdod Raingauge Stations of Patan District, Gujarat Neha Patel and Dilip Shete

26

6. Impact Assessment of Projected Climate Change on Pearl Millet in Gujarat S.B. Yadav, H.R. Patel, M.M. Lunagaria, P.K. Parmar, N.J. Chaudhari, B.I. Karande and V. Pandey

33

DROUGHT

7. Climate Change Impacts on Drought in North Gujarat Agroclimatic Zone: Special Reference to Ahmedabad District Dilip Shete and Neha Patel

41

8. Characterization of Agricultural Drought in Koppal District of Northeastern Parts of Karnataka, India J.K. Neelakanth, D. Tamilmani and I. Muthuchamy and P. Balakrishnan

50

FLOOD

9. Rainfall based Digital Flood Estimation Techniques R.K. Suryawanshi, S.S. Gedam and R.N. Sankhua

59

10. Development of Flood Inundation Model for Surat City under Changing Climatic Condition in Tapi Basin P.V. Timbadiya, P.L. Patel and P.D. Porey

64

11. Flood Forecasting and Reservoir Operation as an Adaptive Measure for Climate Change in Tapi Basin Shekharendu Jha, Rishi Srivastava and Vikas Barbele

70

HYDEL POWER

12. An Overview of Sardar Sarovar Project with Special Reference to Power Component N.K. Bhandari and N.P. Namdeo 79

Contents

IMPACT OF CLIMATE CHANGE

13. Impact of Climate Change on Water Resources M.L. Kansal and Surendra Kumar Chandniha

89

14. Impacts of Climate Change on the Canal Network of Sardar Sarovar Project M.B. Joshi

93

15. ENSO and its Impact on Monsoon Rainfall in Central India D.S. Arya, A. Greeballa and A. Murumkar

97

16. Climate in India: Key Features of the Variables M. Dinesh Kumar

104

17. Synergistic Impacts of Climate Change and Environmental Pollution: Studies Required for Impact Minimization and Environmental Management J.S. Pandey

112

18. High Resolution Climate Change Projection using PRECIS Model—A Case Study of Coastal District of Tamil Nadu Perumal Thirumurugan, Andimuthu Ramachandran, Muthiah Krishnaveni and Dhanya Praveen

119

19. Impact of Climate Change on Water Resource Systems of Rajasthan State Kuldeep, Ankit Sharma and Rohit Goyal

122

20. Climate Change Analysis for the Coastal Belt Area of Saurashtra Harji D. Rank, Pankaj J. Rathod, Hiren P. Patel, Popat B. Vekariya, Rajni J. Patel and Dhaval M. Paradava

128

21. Rainfall-Runoff Modeling using ANN at Dharoi Sub-Basin Ratan A. Panchal, T.M.V. Suryanarayana and F.P. Parekh

135

22. Rainfall-Runoff Modeling using HEC-HMS, Remote Sensing and Geographical Information System in Middle Gujarat, India Mukesh K. Tiwari, M.L. Gaur, Baria Sonal V. and Nakum Jayesh K.

140

23. Predicting Rainfall using Meteorological Variables at Deesa, Dist. Banaskantha, Gujarat Devang Shah, Dilip Shete and Neha Patel

147

24. Determining Time Scale to Study Climate Change Impacts on Rainfall at Deesa in Banaskantha, Gujarat Rina Chokshi, Dilip Shete, Vandana Pandya and Neha Patel

154

25. Impact of Climate Change on Rainfall and Potential Evapotranspiration of Warangal District Jew Das and N.V. Umamahesh

165

26. Climate Change Impact on Rainfall in Junagadh District of Gujarat State, India Harji D. Rank, Pankaj J. Rathod, Hiren P. Patel and Popat B. Vekariya

170

27. Temporal Variability and Trend in Monthly, Seasonal and Annual Rainfall in Twentieth Century over Gujarat Region Sachin S. Chinchorkar, F.G. Sayyad, V.B. Vaidya and Vyas Pandey

179

28. Temporal Variability Analysis of Rainfall in Ahmedabad District, Gujarat Shilpa C. Parmar and Dilip T. Shete

187

29. Adaptation of Water Sector to Global Warming and Climate Change Vidyanand Mahadeo Ranade

xvi

190

Contents

NSCCIWRS

Contents

WATER SHED

30. Application of Watershed based R&D Voyage in India with a Sketch from Gujarat A.M. Shekh and M.L. Gaur

199

31. Natural Water Springs of Mid-Himalayas: A Watershed Recharge and Release System in Need of Management Neeraj Kumar Bhatnagar and R.K. Nema

206

32. Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds B. Krishna Rao, R.S. Kurothe, Gopal Kumar, V.C. Pande and P.K. Mishra

212

33. Rainfall Analysis to Plan Water Harvesting Structures in Micro Watersheds of NAU Research Farms, Bharuch Sarika Santu Wandre and Prashant Kumar Shrivastava

220

AUTHOR INDEX

225

NSCCIWRS

Contents

xvii

Agriculture

NSCCIWRS

M.C. Varshneya

76

Effect of Climate Change on Agriculture M.C. Varshneya Former Vice-Chancellor, Anand Agricultural University, Anand [email protected]

V.B. Vaidya Assistant Professor, Agricultural Meteorology, Department of Agricultural Meteorology, BACA, AAU, Anand [email protected]

ABSTRACT Impact of climate change on Arctic and Antartica alongwith effect of global warming on the sea level rise are discussed. While discussing impact of climate change in India loss of agricultural biodiversity and effects of climate change on crop growth and yield are explained in this paper.

KEYWORDS: Climate change, Agriculture and Economy, Crop growth and yield

INTRODUCTION The 2007 IPCC report has unequivocally concluded that our climate is warming rapidly, and that we are now at least 90% certain that this is mostly due to human activities. The amount of carbon dioxide in our atmosphere now far exceeds the natural range of the past 650,000 years, and it is rising very quickly due to human activity. The atmospheric concentration of carbon dioxide has been increasing at an alarming rate (1.9 ppm per year) in recent years than the natural growth-rate. The global atmospheric concentration of methane was at 1774 ppb in 2005 and nearly constant for a period of time. Nitrous oxide increased to 319 ppb in 2005 from pre-industrial value of about 270 ppb (Cline, 2008). If this trend is not halted soon, many millions of people will be at risk from extreme events such as heat waves, drought, floods and storms, our coasts and cities will be threatened by rising sea levels, and many ecosystems, plants and animal species will be in serious danger of extinction. The increase in mean air temperature over the globe for the last 100 years (18501899 to 2001-2005) is 0.76°C which is influencing the reduction of snow cover and discharge of river water in addition affecting the agricultural production system. The agricultural sector can reduce its own emissions, offset emissions from other sectors by removing CO2 from the atmosphere (via photosynthesis) and storing the carbon in soils, and reduce emissions in other sectors by displacing fossil fuels with bio-fuels. Every ton of carbon added to, and stored in, plants or soils removes 3.6 ton of CO2 from the atmosphere. Furthermore, biomass from the agricultural sector can be used to produce bio-fuels, which can substitute for a portion of the fossil fuels currently used for energy. Bio-fuel use, particularly substituting energy crops for imported petroleum for transportation, has important energy security benefits. Improvement of crop varieties, through a combination of breeding and biotechnology, will have maximal benefits if combined with improved agronomy. Agronomic advances include conservation agriculture, which involves reduced tillage and NSCCIWRS

permanent soil cover, and rainwater harvesting. Improved yields are likely to benefit farmers; the overall goal of adaptation should be to ensure that livelihoods, not necessarily just yields, are improved. Application of best management practices in agriculture and use of bio-fuels for GHG mitigation can have substantial co-benefits. Increasing the organic matter content of soils (which accompanies soil carbon storage) improves soil quality and fertility, increases water retention, and reduces erosion. More efficient use of nitrogen can reduce nutrient runoff and improve water quality in both surface and ground waters. Climate change is one of the allencompassing global environmental changes likely to have deleterious effects on natural and human systems, economies and infrastructure. The risks associated with it call for a broad spectrum of policy responses and strategies at the local, regional, national and global level. The UNFCCC (United Nations Framework Convention on Climate Change) highlights two fundamental response strategies: mitigation and adaptation. While mitigation seeks to limit climate change by reducing the emissions of GHG (greenhouse gases) and by enhancing ‘sink’ opportunities, adaptation aims to alleviate the adverse impacts through a wide-range of system-specific actions. Albeit both mitigation and adaptation measures must be pursued to tackle the climate change problem and to create an effective and inclusive international climate change regime, more attention has been devoted to mitigation in the past, both in scientific research and policy debate. Sensitivity to the issue of adaptation has grown over the last couple of years, particularly after the IPCC (Intergovernmental Panel on Climate Change) TAR (Third Assessment Report). Adaptation has now emerged as an urgent policy priority, prompting action both within and outside the climate change negotiations. Climate change is now accepted as a fact by the all. This acceptance provides a new climate for

M.C. Varshneya

3

National Seminar on Climate Change Impacts on Water Resources Systems

governments, decision-makers and researchers to assess the impacts and the necessary solutions across many important sectors around the globe. Two climate models were run to the year 2100 for the National Assessment Program in the USA. One showed regional temperature increases of 5°C in winter and 3°C in summer by the year 2060, while the other predicted even greater increases. The models did not agree on specific regional climate changes, e.g. one had precipitation increases and the other decreases in the southeastern USA during the summer. It appears reasonable to conclude that since the concentration of radiative gases is clearly increasing in the atmosphere and the theoretical reasons for causing warming are not disputed, the conditions to induce warming are in place. Many atmospheric scientists agree with this assessment.

IMPACT OF CLIMATE CHANGE ON ARCTIC AND ANTARCTICA The arctic and Antarctica regions are fully snow covered and hence no activities were possible. Arctic region is claimed to be the territorial property of six nations viz., Canada, USA, Norway, Denmark, England and Russia. Eight countries were given status of observer, who accepted the territorial claims of those six countries. Now, twelve more countries including China and India have been accorded the status of observer. Up-till now arctic region was fully snow covered hence no activity was possible. But, now because of climate change arctic has melted and polar region is an ocean in summers. Therefore, now, navigation is possible. The distance via arctic region is almost half of the distance between equatorial or tropical regions. It reduces the fuel consumption and cost. Naturally, region has become commercially active. It will create a wedge between six nations claiming territorial authority and rest of the countries specially observers. Similarly, mining of petroleum is economical. In Antarctic region snow has melted to the tune of 6-8 m, already fifty countries are doing scientific research experiments. Possibility of availability of minerals on sea bed is enormous. Climate change will attract the attention on mining of minerals from that area. Therefore, climate change and its impact on polar region will be having far reaching effect on global economy and politics.

IMPACT OF CLIMATE CHANGE IN INDIA The Global warming had pushed up the temperature of Himalayas by up to 0.6°C in past 30 years. If the temperature continues to rise as it is, there will be no snow and ice in Himalayas in 50 years. Thousands of glaciers are the source of water for nine major rivers whose basins have 1.3 billion people from Pakistan to Myanmar, including parts of India and China. Although the warming processes continue unabated, the rate of 4

rise in temperatures in the Gangotri glacier area has nevertheless demonstrated a marked gradual decline since the last quarter of the past century. However, Samudra Tapu, one of largest glaciers in Chandra Basin in Lahul and Spiti receded by 862 m between 1963 and 2006, at a rate of 18.5 m in a year, with the rapid rate retreat being observed during past six years compared to earlier decades (India Today, 2006). Glaciers in the Himalayan mountain ranges will retreat further, as temperatures increase: they have already retreated by 67% in the last decade (Rao et al., 2008). Glacial melt would lead to increased summer river flow and floods over the next few decades, followed by a serious reduction in flows thereafter. It is apparent that the projected climate change leading to global warming, sea-level rise and melting of glaciers will disturb the water balance in different parts of India and quality of ground water along the coastal track.

EFFECT OF GLOBAL WARMING ON SEA LEVEL RISE The past observations on the mean sea level along the Indian coast show a long-term rising trend of about 1.0 mm/year. However, the recent data suggests a rising trend of 2.5 mm/year in the sea level along Indian Coastline. Model simulation studies, based on an ensemble of four AOGCM outputs, indicate that the oceanic region adjoining the Indian subcontinent is likely to warm at its surface by about 1.5- 2.0°C by the middle of this century and by about 2.5-3.5°C by the end of the century. The corresponding thermal expansion, related sea-level rise is expected to be between 15 cm and 38 cm by the middle of this century and between 46 cm and 59 cm by the end of the century. A one-meter sea level rise is projected to displace approximately 7.1 million people in India, and about 5,764 km2 of land area will be lost, along with 4,200 km of roads (Rao et al., 2008).

STATUS OF AGRICULTURE AND LAND USE The total land area of India is 328.8 Mha and out of this about 180-190 Mha area (gross) is cultivated. India occupies 2.45 % of the worlds land area, 4% water resources but, supports 16.2% of the world’s human population and 15% of livestock. About 61% of the land area is under agriculture. It is endowed with varied climate, biodiversity and ecological regions. The food production increased since 1970-1980 and country became self reliant. Subsequently, increase in food production was low. In 2009-2010 food production was 230 Mt, while in 2010-2011 it is 236 Mt (Varshneya and Vaidya, 2011).

AGRICULTURE AND ECONOMY Agriculture contribution in the gross domestic product is declining in India, which in 2008-09 touched at

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15.7% from about 30% in 1990-91. During the last two decades, the average annual growth of agriculture sector was less than half (around 3%) of the overall average growth of the economy (6-7%) (NAAS, 2009). Industrial and service sectors have outpaced performance of agriculture sector during the last two decades. But the proportion of workforce engaged in agriculture did not commensurate with the decline of its share in the gross domestic product. At present also, agriculture sector provides employment to about 52 % of the workforce that used to be about 61% in 1990-91. These starkly different trends reveal that incomes in non-agriculture sector are growing faster than agriculture sector. And a sizable workforce from agriculture is needed to be shifted to non-agriculture sector for income and livelihood opportunities. Hence, in the country the research and development focus needs to be reoriented in a way to develop and promote those technologies that raise agricultural income and ensure employment opportunities in the agri-supply chain to a vast majority of the workforce (ICAR, 2011).

GROWING FOOD DEMAND The demand for food and processed commodities is increasing due to growing population and rising per capita income. There are projections that demand for food grains would increase from 192 million tonnes in 2000 to 345 million tonnes in 2030. Hence in the next 20 years, production of food grains needs to be increased at the rate of 5.5 million tonnes annually. The demand for high-value commodities (such as horticulture, dairy, livestock and fish) is increasing faster than food grains for most of the high-value food commodities demand is expected to increase by more than 100% from 2000 to 2030 (ICAR, 2011). Demand of food in the year 2020 is likely to be to the tune of 280 Mt. The demand for food and processed commodities is increasing due to growing population and rising per capita income.

IMPACT OF IRRIGATION ON CLIMATE CHANGE Today, around 3,800 km3 of fresh water is withdrawn annually from the world’s lakes, rivers and aquifers. This is twice the volume extracted 50 years ago. Agriculture accounts for about 67 per cent of withdrawals, industry uses 19 per cent and municipal and domestic uses account for nine per cent. By the end of the 20th century, there were over 45,000 dams in over 150 countries. About one fifth of the world’s agricultural land is irrigated, and irrigated agriculture accounts for about 40 per cent of the world’s agricultural production. Half the world’s large dams were built exclusively or primarily for irrigation, and an NSCCIWRS

estimated 30 to 40 per cent of the 271 million hectares of irrigated lands worldwide rely on dams. Dams are estimated to contribute between 12–16 per cent of world food production. In India and China together, large dams could have displaced between 26–58 million people between 1950 and 1990. By 2025 there will be approximately 3.5 billion people living in waterstressed countries. Changes in precipitation and evapotranspiration may influence ground water recharge.

LOSS OF AGRICULTURAL BIODIVERSITY The agricultural biodiversity is under threat from changes in production system. More than 90 per cent of crop varieties have been lost from farmers’ fields in the past century. Animal breeds are also disappearing at the rate of five per cent per year. In place of this diversity of farmers’ varieties, consumers are being provided more and more with homogeneous, uniform, food commodities produced from a limited range of varieties developed and owned by plant breeding companies. To survive, humanity will need to make sure that the genes of our crops, livestock, other food species and the agricultural biodiversity of which they are a part, are continuously under development in farmers’ fields. Agricultural biodiversity is the basis of the world’s food supply, farm livelihoods and landscapes and is humanity’s insurance against future threats to food and farming. Rainfall trend observed in Gujarat Meteorological sub-division during twentieth century (1871-2011) studied by Chinchorkar et al., (2013) revealed that there is insignificant decrease in southwest monsoon rainfall while increase in post monsoon season. Rainfall decline is more predominant in June and July but not so in August and September within the monsoon season. There was a major shift in rainfall pattern temporarily during recent years as seasonal rainfall during the southwest monsoon was declining while increasing in postmonsoon season. The decreasing trend in southwest monsoon rainfall over Gujarat subdivision is supported by other researchers (Rupa Kumar et al., 1992) reported that monsoon depression frequency had a strong decreasing trend during last 100 years and the frequency now is less than half of the frequency of depressions at the beginning of the twentieth century. The Premonsoon, southwest and winter rainfall over Gujarat meteorological subdivision shows decreasing trend from 1871 to 2011, while in Post-monsoon and Annual rainfall over meteorological subdivision shows increasing trend from 1871 to 2011 (Chinchorkar et al., (2013).

EFFECT OF GLOBAL WARMING ON AGRICULTURE Globally the overall impact of baseline global warming by the 2080s is a reduction of agriculture productivity

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(output/hectare) of 16% without carbon fertilization, and a reduction of 3% should carbon fertilization benefits. Developing countries suffers losses of about 25%

3.

Since, there is greater probability of increase in temperature in rabi, it is likely that the productivity of wheat and other rabi crops would be significantly reduced.

without carbon fertilization and 10 to 15% if carbon fertilization is included. Losses could reach devastating levels in some of the poorest country (>50% in Senegal & Sudan).Damage will be greater in countries closer to the equator, where temperatures already tend to be close to crop tolerance level. India would record 27% loss (by crop model), 38% without carbon fertilization and 29% with carbon fertilization in crop yields.

4.

Wheat yields in central India are likely to suffer by up to 2 per cent in the pessimistic scenario, but there is also a possibility that these might improve by 6 per cent, if the global change is optimistic

5.

Sorghum, being a C4 plant, does not show any significant response to increase in CO2 and hence the different scenarios do not affect its yield.

6.

However, if the temperature increases are higher, western India may experience some negative effect on productivity due to reduced crop durations.

7.

The impact of warming scenarios becomes apparent at higher levels of fertilizer application from 2030 onwards.

8.

In future, therefore, much higher levels of fertilizer may need to be applied to meet the increasing demand for food.

9.

The production of fruits may be significantly affected if the changes in climate happen to coincide with the critical periods. Global warming will push the snow line higher and dense vegetation will shift upwards. This shift will be selective and species specific due to the differential response of plants to changing environmental conditions.

The Challenge to stabilize agriculture productivity is large due to: 1.

Green revolution has already slowed down. FAO data shows that grain yields which rose at an annual rate of 2.7% in 1960s &1970s have risen at 1.6% in past 25 years.

2.

Global food demand is expected to approx. triple by 2080s because of the higher world population & higher incomes. It is also likely to shift the land from food crops to bio-energy (ethanol fuel).

These studies won’t include losses due to extreme weather events as floods, droughts and insect pest. Also it wouldn’t include agriculture losses associated with sea level rise, major consideration is in Bangladesh & Egypt. Areas such as Indonesia, where main crop rice will be more vulnerable to the increased intensity of ENSO effects in future of climate change. Prof. David Battisti of Univ. of Washington researched the effects of future ENSO patterns on Indonesian rice agriculture using IPCC’s 2007 annual report and 20 different logistical models mapping out climatic factors such as wind, pressure, sea level and humidity and found that rice harvest will experience a decrease in yield. Bali and Java, which holds 55% rice yields in Indonesia, will be likely to experience 9-10% probability of delayed monsoon patterns, which prolongs the dry season (Varshneya et al., 2010).

DIRECT EFFECTS OF CLIMATE CHANGE ON CROP GROWTH AND YIELD Some of the simulation model studies explain the effect of climate change on crop growth and yield.

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1.

Most of the simulation studies have shown a decrease in the duration and yield of crops as temperature increased in different parts of India.

2.

Yields of both kharif and rabi crops decreased as temperature increased; a 2°C increase resulted in 15-17 per cent decrease in the grain yield of both crops, but beyond that the decrease was very high in wheat (Rao, 2007).

10. The nutritional quality of cereals and pulses may also be moderately affected which, in turn, will have consequences for our nutritional security. 11. The loss in farm-level net revenue may range between 9 per cent and 25 per cent for a temperature rise of 2-3.5°C. 12. Changes in rainfall, temperature and wind speed pattern may influence the migratory behavior of the locust.

IMPACT OF ANIMAL AGRICULTURE As a result of the steady rise in animal-product promotion and demand, traditional farming practices in the latter half of the last century was replaced in the U.S. largely by immense, intensive animal operations; in the developing world, they are being replaced at a rate of more than 4% per year. The industrialization of animal agriculture is an important contributor to global environmental degradation and climate change. Animal agriculture accounts for 37%, 65%, and 64% of

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anthropogenic methane, nitrous oxide, and ammonia emissions, respectively, from ruminant fermentation, livestock waste, fertilizer use and other factors (FAO, 2006). Methane and nitrous oxide have 21 and 296 times, respectively, the global warming potential of CO2. In 2006, the UN Food and Agriculture Organization (FAO) declared that animal agriculture contributes 18% of annual anthropogenic greenhouse gas emissions, measured in CO2-equivalents, more than that of the worldwide transportation sector. Animal agriculture constitutes 30% of the total land surface, the largest use of land by humans. Thirty three percent of total arable land is used to produce feed crops, 20 with energy input that far outweighs the output. Approximately 70% of previously forested land in the Latin American Amazon is used as grazing pastures, with the remainder being used largely for feed crop production. Animal agriculture consumes 70% of the fresh water and contributes extensively to land, air, and water pollution. Pesticides and fertilizers, including manure, may contaminate waterways. In the U.S., animal agriculture is responsible for 37% of pesticide use and 32% and 33%, respectively, of the nitrogen and phosphorus loads found in fresh water sources (Akhtar, 2009). Given the animal agriculture sector’s considerable role in environmental degradation, zoonotic disease emergence, and chronic disease promotion, reducing livestock production and promoting healthy plant based diets should be a global health priority. Health care providers can, individually and collectively, play a significant role in ensuring healthy and environmentally sustainable nutrition policies and practices.

CONTRIBUTION OF AGRICULTURE IN GHG’S EMISSIONS

The main sources of nitrous oxide are nitrogen fertilizers and manure applied to cropland and pastures, leguminous crops, and crop residues. Some nitrous oxide emissions also occur from stored manure. Annual U.S. agricultural methane emissions are approximately 44 MMT carbon-equivalents per year (2004 estimate) and stem mainly from livestock, animal waste, and rice cultivation. In aggregate agricultural GHG emission on a carbon-equivalent basis accounts for roughly 8 percent of total U.S. emissions from all sources (USEPA, 2006). Improved agricultural practices over the next 10 to 30 years is substantial, estimated at approximately 102 to 270 MMT carbon-equivalent per year. This estimate derives from a combination of carbon sequestration (70 to 221 MMT carbon), nitrous oxide reductions (23 to 31 MMT carbon-equivalent), and methane reductions (9 to 18 MMT carbonequivalent). This mitigation potential equals or exceeds present emissions from U.S. agricultural sources (about 160 MMT carbon-equivalent in 2004) and represents 5 to14 percent of year-2004 U.S. GHG emissions from all sources and for all gases. In addition, energy produced from agricultural biomass sources, if substituted for fossil fuels, represents a mitigation potential of 510 to 1,710 MMT CO2 equivalents per year (140 to 470 MMT carbon-equivalent per year) or 7 to 24 percent of total 2004 U.S. GHG emissions.

MITIGATION BY AGRICULTURAL SECTOR

The agricultural sector can reduce its own emissions, offset emissions from other sectors by removing CO2 from the atmosphere (via photosynthesis) and storing the carbon in soils, and reduce emissions in other sectors by displacing fossil fuels with bio-fuels. Through adoption of agricultural best management practices, U.S. farmers can reduce emissions of nitrous oxide from agricultural soils, methane from livestock production and manure, and CO2 from on-farm energy use. Improved management practices can also increase the uptake and storage of carbon in plants and soil. Every tonne of carbon added to, and stored in, plants or soils removes 3.6 tonnes of CO2 from the atmosphere. Carbon stocks in agricultural soils are currently increasing by 12 million metric tonnes (MMT) of carbon annually. If farmers widely adopt the best management techniques now available, an estimated 70 to 220 MMT of carbon could be stored in U.S. agricultural soils annually. Together with attainable nitrous oxide and methane reductions, these mitigation NSCCIWRS

options represent 5 to 14 percent of total U.S. GHG emissions. The relevant management technologies and practices can be deployed quickly and at costs that are low relative to many other GHG-reduction options.

For agriculture to be sustainable for betterment of present six billion population and nine billion of future population, we need to protect and even improve the environment, so future generations can meet the challenges of their day. For sustainable agriculture we must 1.

Meet the needs of everybody.

2.

Protecting and environment.

3.

Providing opportunity for social wellbeing of one billion farmers.

even

improving

the

All three are essential and none are optional. For this agriculture needs dramatic productivity improvements due to deployment of numerous innovations, better genetics and better informed farming practices.

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Agricultural carbon sequestration has the potential to substantially mitigate global warming impacts. When using biologically based regenerative practices, this dramatic benefit can be accomplished with no decrease in yields or farmer profits. Even though climate and soil type affect sequestration capacities, these multiple research efforts verify that practical organic agriculture, if practiced on the planet’s 3.5 billion tillable acres, could sequester nearly 40 percent of current CO2 emissions.

in print and electronic media. The following actions may reduce the bad effect of climate change. 

All plant components viz., straw, dry leaves, leaf drop and husk should be utilised fully.



Practice of rabbing forthwith.



Plant residue should be utilised for energy production instead of burning.



Major source of Nitrous oxide in atmosphere is chemical Nitrogenous fertilisers, hence organic fertilisers and manures should be used. It will help in controlling Nitrous oxide.



Organic farming should be encouraged.



Use of organic food, vegetables and fruits in diet should be encouraged.



Use of water saving technologies in giving irrigation to agricultural crops like sprinkler and/or Drip irrigation should be popularised.



Use of SRI method of paddy cultivation should be encouraged to save water and emission of methane is reduced.



The dairy animals should be fed with feed which have less methane emission from animals.



Plantation on hill slopes and wastelands.



Growing of mangroves on coastal areas to reduce tidal velocity and thus soil erosion.

CONSERVATION AGRICULTURE (CA) TECHNOLOGIES TO MITIGATE EFFECT OF CLIMATE CHANGE CA involves practices such as minimum or zero mechanical disturbance, crop residues retention, permanent organic soil cover, diversified crop rotations, precise placement of agro chemicals, in field traffic control and application of animal manure and crop residues. The benefits of CA are lower farm traffic, reduction in use of mechanical power, labour inputs thus resulting in timely field operations, lower risk of crop failure and ultimately resulting in higher yields, lower costs and reduction in environmental pollution. A good number of machines such as no-till drill, strip till drill, raised bed planter, laser land leveller, straw cutter cum incorporator, straw baler, farm residue collector, straw combine have been developed and are being propagated. In rice also to reduce water requirement direct seeding of rice on raised beds and System of Rice Intensification (SRI) method of paddy cultivation is being experimented. To produce 1 kg of rice about 3000-5000 litre of water is required. To apply water irrigation pumps are used and they are operated by diesel engines which emit CO2. The amount of carbon sequestered by plants is almost equal to the amount lost in the atmosphere. In No-till plots, fuel consumption was found to be 11.30 l/ha as compared to 34.62 l/ha by conventional method resulting in fuel saving of 24 l/ha. There was 67 % saving in fuel due to no-tillage as compared to conventional method. In another study conducted by Rautray (2003), conservation tillage as compared to conventional practice showed higher performance in terms of increased benefit cost ratio (2.47-2.17) and lower operational energy (5.1-26.1%). the maximum water productivity (kg per cu.m. of water) was recorded under permanent raised bed (1.59) followed by no-tillage (1.37) and conventional till flat bed sown crops (1.19).

ACTION PLAN FOR MITIGATION OF CLIMATE CHANGE The understanding of regarding impact of climate change is increasing day by day due to lot of discussion

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should

be

stopped

The above points, though it is few steps, but it can reduce the challenge of Global warming to our environment for present and future generations.

REFERENCES Akhtar, A.Z. (2009). Health Professionals’ Roles in Animal Agriculture: Climate Change, and Human Health. Am. J. Prev. Med. 36 (2), pp. 0749-0797, doi:10.1016/j.amepre.2008.09.043. Chinchorkar, S.S., F.G. Saiyyad, V.B. Vaidya and Vyas Pandey. (2013). Temporal Variability and Trend in Monthly, Seasonal and Annual Rainfall in Twentieth Century over Gujarat Region, A paper acceped for national seminar on Climate Change and its Impact on Agriculture, held during 27th- 29th NOVEMBER, 2013 at Parul Institute of Engineering and Technology, Waghodiya, Distt. Vadodara, Gujarat, p. 1-9. Cline, W. R., (2007), Global Warming and Agriculture: Impact Estimates by Country (Washington: Center for Global Development and Peterson Institute for International Economics).

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FAO (Food and Agriculture Organization of UN). (2006). FAO statistical databases. Available from http://faostat.fao.org.

Rautray, S.K. (2003). Mechanization of rice-wheat cropping system for increasing the productivity, Annual Report (20022003), Rice-Wheat consortium, CIAE, Bhopal.

ICAR (Indian Council of Agricultural Research). (2011). Vision 2030 document, ICAR New Delhi, p. 1-38.

Rupa Kumar, K., Pant, G.B., Parthasarathy, B., Sontakke, N.A., (1992).”Spatial and sub seasonal patterns of the long term trends of Indian summer monsoon rainfall”,” International Journal of Climatology, 12, 257–268.

India Today, 6 Nov., (2006). International Federation of the Red Cross (2002) India: Orissa Cyclone Appeal No. 28/1999 final report. IPCC (2007). Intergovernmental Panel on Climate Change (Working Group I) Climate Change 2007: The Physical Science basis, IPPCC. IPCC (2007). Intergovernmental Panel on Climate Change (Working Group II) Climate Change 2007: Impacts, Adaptation and Vulnerability IPPCC NAAS (National Academy of Agricultural Sciences). (2009). State of Indian Agriculture. New Delhi, India. Rao, G.G.S.N. (2007). Consequences of Climate Change on Agro-ecosystems. Central Research Institute for Dryland Agriculture (CRIDA) compiled lecture notes, Santoshnagar, Hyderabad – 500 059, pp. 15-28

U.S. Environmental Protection Agency (USEPA). (2006). Inventory of U.S. greenhouse gas emissions and sinks: 1990– 2004. Washington, DC: U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation, in press. EPA 430-R-05-003. Varshneya, M. C., V.B. Vaidya, Vyas Pandey, B.I. Karande and H.R. Patel. (2010). Role of Agriculture to Meet Challenges of Global Warming. An invited paper published in CD of International workshop on “Global Warming, sustainable Development, Agriculture and Public Leadership” held during 11th -13th March, at Gujarat Vidyapeeth, Ahmedabad, p. 1-15.

Rao, GGSN, AVSM Rao, M. Vanaja, VUM Rao and Y.S. Ramakrishna. (2008). Impact of regional climate change over India. In “Climate change and Agriculture over India, eds. GSLHV Prasad Rao, GGSN Rao, VUM Rao and YS Ramakrishna, Kerala Agriculture University, Thrissur pub., pp. 13-48.

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Increasing Gross Crop Yields in Warming Atmosphere by Optimal Irrigation S. Venkataraman Former Director, India Meteorological Department and Former WMO/FAO Agromet Expert [email protected]

ABSTRACT Availability of quantum of water for crop culture in future is examined. Likely variations in extent of reduction in unit area yield of crops due to higher temperatures, effects of elevated levels of Carbon Dioxide in reducing water needs and increasing yields of crops and a decreasing trend in Evaporative Power of Air under global warming are set out. Thus, the need for adoption of a holistic approach in impact assessment studies on crop water use-efficiencies is stressed. Increase in gross crop yields with increasing temperatures for same quantum of irrigation water used is brought out

KEYWORDS: Water Availability, Evaporation Paradox, Yield Per Day, Water use Efficiency, Irrigation Strategy

INTRODUCTION In recent years there have been conflicting reports about the status of Himalayan Glaciers, In 2013 the Intergovernmental Panel on Climate Change had announced their disappearance by 2035. Prior to that there were reports of radar-based studies indicating an increase in snow cover in the Asian mountain ranges. It had also been reported that the Himalayas have lost no ice in the last ten years. However, for us in India this is much ado about nothing as glacier contribution is a minor factor, much less than 10%., in the annual river water budgets in the Ganga and Brahmaputra basins. A review of work on the effects of climate change on Indian Summer monsoon rainfall (Venkataraman, 2003) show that global warming is beneficial in offsetting magnitude of monsoon failures and will be lead to less frequent failures but greater variability in monsoonal rains. In other words there will be no enhancement in incidence of hydrological droughts. There is also an apprehension that global warming will lead to increase in crop water needs. However. reduction in observed values of Pan Evaporation and computed values Reference Crop Evapotranspiration in the recent decades have been reported in many places like India, China, Australia, New Zealand, USA and in many parts of Russia. The above phenomenon is called the " Evaporation Paradox" (Golubev et al 2001) and is attributed to increased cloudiness and/or higher concentration of aerosols of anthropogenic origin. Thus, water needs of crops per unit area will only decrease and not increase. The objective of the above discussion is to emphasise that what we should worry about is (i) not reduction in quantum of water availability for cropping but on optimal irrigation to maximise land-occupancy of crops in time and space and (ii) offsetting the effects

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of higher temperatures in reduction of crop yields. Again, as the increase in temperature in global warming is due to an increase in concentration of Carbon Dioxide. a holistic view involving the effects of both temperature and carbon dioxide on water useefficiencies of crops needs to be adopted to arrive at feasible solutions. The discussions below have been tailored towards meeting the above stated objective

MATERIAL AND METHODS Effects of Higher Temperature Unit area yields of a crop cultivar in a season, when expressed as yield per day per unit area becomes a conservative parameter (Swaminathan. 1968). Accumulated temperatures above a specified base value determine life-duration of crop cultivars. Thus, increase in air temperatures in the ongoing scenario of climate change will reduce field-life duration of crop cultivars and hence lead to an equivalent reduction in unit area yield of crops. The percentage reduction will vary with the base temperature for growth of the crop and with ambient mean crop season temperature (Venkataraman, 2004) i.e. from place to place and in the same place with seasons. Thus, assessment of percentage reduction in field-life duration for given levels of increase in temperature has to be done on a crop-wise, season wise basis. The latter aspect can be taken care of by the use of mean crop seasonal ambient temperatures of 15, 20, 25 and to 30°C. (Venkataraman, 2004). Effects of Increase in Carbon Dioxide Concentration Elevated levels of Carbon Dioxide will reduce transpiration need of plants due to decrease in stomatal aperture (Goudrian and Unsworth, 1990). The consequent reduction in stomatal conductance does not interfere with gas exchange between leaf and air (Drake et al.1997) and hence photosynthesis increases under elevated Carbon Dioxide(Allen,1990).Thus, elevated CO2 will increase water use efficiency. Doubling of levels of CO2 will (i) reduce transpiration to the extent

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of 23% (Cure and Acock, 1986) to 40% (Morison, 1987) and (ii) increase yields to the tune of 30% for many crops ((Kimball, 1983). For a doubling in concentration of CO2, the temperature rise will be 4 °C (Gigori et al, 1989). Due to cooling effects of aerosols, the rise in temperature for a doubling of CO2 level will be 3°C (McCarthy et al. 2010). Mitigation Measures The reductions in field life of crops in various ambient levels of air temperature are not sufficient to warrant raising of another crop. Breeding of crops with higher day-degree requirements or with significantly higher yields per day appear daunting and not possible. The argument that reduction in field-life of a crop must lead to a considerably lesser water needs of crops can be contested on the ground that factors leading to crop-life reduction can lead to an equivalent increase in unit are crop water needs. As mentioned above, despite an increase in temperature the evaporative power of air decreases. The above influences mean that optimal use of available irrigation potential offers the main and practical means to help overcome the yield-reducing influence of higher temperatures. The same is examined in sime detail below.

RESULTS AND DISCUSSON Doubling of levels of Carbon Dioxide from the 2000 level of 370 ppm is expected to occur by 2100 (IPCC, 1990). Thus, in view of the above, temperature will rise by 3°C by the end of the century. So an assessment covering increases of 1,2 and 3 °C should do for the present. The indications are that even on a safe conservative basis, evaporative power of air by 2100 will easily be lower than 3% of the present level. From the forgoing it is evident that for every one degree rise in temperature, yield will increase by 10% and water need will go down by 11%..The base values above which temperatures have to be cumulated varies amongst crops and range from 14ºC for Muskmelon to 2.2ºC for Spinach. For agricultural crops the range is

from 4.5ºC for Wheat to 10ºC for Maize. It was thus decided to carry out the assessment for two base temperatures of 4.5 and 10ºC. In view of the above, it was decided to proceed as detailed below to arrive at the percentage increase in gross yields of crops for same quantum of water used for temperature increases of 1, 2 and 3°C. at ambient seasonal temperatures of 15, 20, 25 and 30°C. The percentage reduction in crop life duration for increase in temperature of 1, 2 and 3°C for crops with base temperatures of 10°C and 4.5ºC presented by Venkataraman (2004) for ambient mean seasonal temperatures of 15, 20, 25 and 30°C were taken. The reduced durations were divided by 0.89. 0.78 and 0.67 for increase in temperatures of 1, 2 and 3°C respectively to get the percentage increase in irrigable area for same quantum of water used. From the increased irrigable area figure so obtained, yields were increased by 10%. 20% and 30% for increased temperatures of 1, 2 and 3 ° C respectively. Results of computations as detailed above are presented in table below. The above shows that for crops with low base temperatures growing in colder regime the increase in gross yield will be higher. For normal crop season temperatures of 20 to 30° C, the increase will be about 15, 35 and 60 % for increase in mean air temperatures of 1, 2 and 3° C respectively. Water needs of all crops and cultivars of same field-life duration and growthrhythm will be the same irrespective of their yield level. Thus, optimal irrigation emerges as the key to increase gross crop yields with available irrigation potential under global warming.

CONCLUSION It is possible to not only ensure sustainability of crop production but increase gross production of all crops under all likely global warming scenario through optimal use of available irrigation resources. Savings in irrigation water through drip and sprinkler irrigation, switch over to SRI rice from puddled paddies and minimisation of peak water consumption of crops in Summer would be additional mitigating measures.

Table 1: Percentage Increase in Gross yields for same quantum of Water Used of Crops: A and B with Base Temperatures of 10 and 4.5°C Respectively Mean Crop Season Temp, °C 1 °C 15 20 25 30

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A 102 112 116 117

B 112 116 117 118

Increase in Mean Air Temperature 2°C A B 124 129 127 137 135 140 140 143

S. Venkataraman

3°C A 122 149 161 169

B 151 163 169 172

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REFERENCES Allen, L.H. Jr. (1990). Plant responses to rising CO2 and potential interactions with air pollutants. Jl. Environ. Qual. 19: 15-34. Cure, J. D. and Acock, B. (1986). Crop responses to carbon dioxide doubling: A literature survey. Agricl. and Forest Meteorol. 38: 127-145. Drake, B.G.; Gonzalez-Meler, M.A. and Long, S.P. (1997). More efficient plants: A consequence of rising atmospheric CO2. Ann. Rev. Plant Physiol. Plant Molecular Biol. 48: pp 609-639. Goudrian, J, and Unsworth, M.H. 1990. Implications of increasing carbon dioxide and climate change for agricultural productivity and water resources. Amer. Soc. Agron. Spl. Publication No, 23: 111-130 Intergovernmental Panel on Climate Change (IPCC) (1990). Climate Change. The IPCC Scientific Assessment. Houghton,J.T., Jenkins,G.j. and Ephraums, J,J, (Eds). Cambridge University Press, Cambridge, U.K. 410pp.

McCarthy, M.P.; Best, M.J. and Betts, R.A. ( 2010). Climate change in cities due to global warming and urban effects. Geophys. Res, Lett. 37: L09705 doi:10.1029/2010 G10 2845. Morison, J.I.L. (1987). Intercellular CO2 concentration and stomatal response. In “Stomatal Functionˮ . Stanford Univ. Press. Pp 229-251. Swaminathan. M.S. (1968). Genetic manipulation of productivity per day. Special Lecture, Symposium on Cropping Patterns in India. Indian Council of Agricultural Research. Venkataraman, S, (2003). An insight into climate change and future crop prospects in India. Ind. Jl. Environ. & Ecoplan. 7: 483-490. Venkataraman, S, ( 2004). On possible reduction in yields of grain crops in future climate. Jl. of Agrometeorol. 6: 213-219.

Kimball, B. A. ( 1983). Carbon dioxide and agricultural yield. An assemblage and analysis of 430 prior observations. Agronomy Jl. 75: 779-788.

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S. Venkataraman

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Impacts of Climate Change on Growth and Yield of Rice in Middle Gujarat Agro-Climatic Region H.R. Patel, S.B. Yadav, M.M. Lunagaria, P.K. Parmar, N.J. Chaudhari, B.I. Karande and V. Pandey Department of Agricultural Meteorology, B.A. College of Agriculture, Anand Agricultural University, Anand, Gujarat–88110 [email protected]

ABSTRACT The impact of projected climate change (2071-2100) of A2 scenario and its likely impact on rice yield of middle Gujarat agroclimatic region of Gujarat using PRECIS output of A2 and Baseline (1960-1990) were studied. Baseline and A2 scenario yields were simulated using InfoCrop-rice model. The rice experiment data of kharif seasons 2009 to 2012 were used of Main Rice Research Station, Navagam, Anand Agricultural University, Anand, Gujarat to calibrate and validate the model. Secondly the study was extended to district level for Anand, Vadodara, Ahmedabad, Panchmahal and Dahod Districts to simulate rice yield using InfoCrop-rice model under A2 scenario. Popular cv. GR-17 and Gurjari with two dates of transplanting (D 1– 15th July and D2 – 30th July) were considered for the study. The overall calibration and validation of InfoCrop-rice model simulation results found satisfactory and under acceptable range (mean error < ±15%). The PRECIS model output showed that there will be mean rise of both maximum and minimum temperature of Anand, Ahmedabad, Vadodara, Dahod , Panchmahal to the tune of 6.0, 4.1, 4.0, 5.0, 4.5 and 5.1, 3.5, 4.3, 3.8, 4.10C, respectively against the base line periods (1961-90). While total rainfall will be rise of Anand, Ahmedabad, Vadodara, Dahod and Panchmahal districts to the 42.0, 28.14, 20.95, 70.0 and 49.7 % respectively as compare to baseline period.. The model simulated results shows that (irrespective of cultivars and dates of sowing) mean grain yield will be reduced at Anand, Ahmedabad, Vadodara, Dahod and Panchmahal are 27.8, 29.7, 32.9, 33.8 and 35.4 respectively. The adaptation strategies viz. fifteen days early transplanting from normal, change in variety, better water management with additional fertilizer and an early transplanting change in variety and better water management with additional fertilizer, the crop yield benefitted 6 to 14 %.

KEYWORDS: Climate Change, PRECIS, A2 Scenario, Simulation, Info Crop-Rice

INTRODUCTION The changes in climate parameters are being felt globally in the form of changes in temperature and rainfall pattern. The global atmospheric concentration of carbon dioxide, a greenhouse gas (GHG) largely responsible for global warming, has increased from a pre-industrial value of about 280 ppm to 387 ppm in 2010. Similarly, the global atmospheric concentration of methane and nitrous oxides, other important GHGs, has also increased considerably resulting in the warming of the climate system by 0.74°C between 1906 and 2005 (IPCC, 2007 a). The global average sea level rose at an average rate of 1.8 mm per year over 1961 to 2003. This rate was faster over 1993 to 2003, about 3.1 mm per year (IPCC, 2007 a). There is also a global trend of an increased frequency of droughts as well as heavy precipitation events over many regions. Cold days, cold nights and frost events have become less frequent, while hot days, hot nights and heat waves have become more frequent. It is also likely that future tropical cyclones will become more intense with larger peak wind speeds and heavier precipitation. The IPCC (2007a) projected that temperature increase by the end NSCCIWRS

of this century is expected to be in the range 1.8 to 4.0°C. For the Indian region (South Asia), the IPCC projected 0.5 to 1.2°C rise in temperature by 2020, 0.88 to 3.16°C by 2050 and 1.56 to 5.44°C by 2080,depending on the future development scenario (IPCC 2007b). Increasing temperatures and changes in rainfall pattern are also impacting the agricultural sector. Although there are ongoing studies to understand the impacts, some studies have shown certain trends. Researchers use several methods to assess the impact of climatic variability ranging from the traditional approach of historical data analyses by various statistical tools to controlled environment studies and Crop Growth Simulation models in order to understand the impact of temperature, rainfall and CO2 on crop growth and yield (Aggarwal, 2008). Rice is grown in an area of 7.5 lakh ha in Gujarat with production of 14.24 Lakh tones. Valsad district has maximum area (14%) and production (29%) middle Gujarat agro-climatic region, while Ahmedabad district has lowest area (9.3%) and production (19.1%) under rice cultivation. The highest productivity of rice in Kheda district (2270 kg/ha) and Ahmedabad district (2180 kg/ha),while lowest productivity was recorded in Vadodara districts (1146 kg/ha) of middle Gujarat region (Annon, 2011).

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National Seminar on Climate Change Impacts on Water Resources Systems

Table 1: Trend Analysis of Weather Condition over Different Locations of Middle Gujarat Region

Rainfall

Tmax

Tmin

1600

40

1400 35 1200 30

1000 800

25

600 20 400

RESULTS AND DISCUSSION Anand VarodaraAhmedabad Panchmahal Dahod

Temp.(0C)

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

15 Projected

200 Base line

The Info Crop model requires daily weather data of maximum and minimum air temperature, solar radiation and rainfall. For calibration and validation of the model, observed weather data (2009-2012) were obtained from Main Rice Research Station, Navagam, Anand Agricultural University, Anand, Gujarat. Top layer soil data file of similar texture were modified in Master using actual soil data of respective experimental site. The field experiment was carriedout on rice for kharif with cv. GR17 and Gurjari with two transplanting of sowing (D1 – 15th July and D2 – 30th July)) were used to calibrate and validate the InfoCrop-rice model. Secondly the study was extended to district level for Anand, Vadodara, Ahmedabad, Panchmahal and Dahod Districts to simulate rice yield using InfoCrop model under A2 scenario.

The maximum and minimum temperature of Anand, Ahmedabad, Vadodara, Dahod , Panchmahal to the tune of 6.0, 4.1, 4.0, 5.0, 4.5 and 5.1, 3.5, 4.3, 3.8, 4.10C, respectively against the base line periods (1961-90). While rainfall will be recorded at Anand, Ahmedabad, Vadodara, Dahod and Panchmahal districts to the 42.0, 28.14, 20.95, 70.0 and 49.7% respectively as compare to baseline period. The PRECIS model output showed that highest temperature (6.0 0C) will be rise in Anand district while it was lowest (4.0 0C) at Vadodara district. In case of rainfall the highest (70%) average rainfall of projected period under A2 scenario will be rise in Dahod and lowest (20%) in Vadodara district in comparison to their respective baseline rainfall of all the districts, (Fig. 1).

Projected

Data Requirement for Info Crop Simulation Model

Vado- dara 0.036 0.033 0.018 0.039 0.031 0.021 0.040 0.017 0.026 0.022 2.11

Projected Weather Over Base Line

Base line

For climate change impact study, weather data for A2 scenario was derived from PRECIS downscaled model output prepared by IITM Pune in a grid size of 0.4 degree. Two period of 30 years each, one for base line i.e., 1961-1990 (base line period) and another for A2 projected scenario i.e., 2071-2100 (projected scenario) were considered for climate change impact study. There are gross difference between PRECIS base line daily weather data and actual weather data for the same period (Taylor et al, 2007). Thirty year monthly average of daily weather parameters of base line data was subtracted from corresponding projected A2 scenario data and the difference obtained were used for computing weather data for projected period using actual observed data. In case of rainfall percentage difference on monthly sum of 30 years average data, between projected output and base line output were used as correction factor.

Projected

Climate Change Study

Panch- mahal 0.02 0.01 0.01 0.01 0.01 -0.03 -0.01 0 -0.02 -0.01 1.73

long term data i.e. baseline (1980-2011) of Anand, Ahmedabad, Vadodara, Dahod, Panchmahal. The results of analysis are presented in Table 1. showed that all the weather parameters i.e. maximum temperature, minimum temperature and rainfall were not showing any specific significant trend except Ahmedabad. In Ahmedabad district positive significant trend for minimum temperature under winter, monsoon and post monsoon season was observed, while during summer season minimum temperature trend was found significant negative.

Base line

MATERIALAND METHOD

Projected

Rainfall (mm)

Slope Dahod -0.131 0.111 -0.106 -0.091 -0.054 0.049 0.172 0.001 -0.046 0.044 1.732

Ahmeda- bad 0.041* 0.023 0.048* 0.062* 0.039 -0.032 -0.035* 0.024 -0.029 -0.014 -0.432

Base line

Tmin. (0C)

Winter Summer Monsoon Post-onsoon Annual Winter Summer Monsoon Post-monsoon Annual Annual

Anand 0.033 0.043 0.019 0.049 0.033 0.020 0.043 0.019 0.029 0.024 2.14

Projected

Tmax. (0C)

Period/ Season

Rainfall (mm)

Para-meter

Anand VarodaraAhmedabad Panchmahal Dahod

Trend Analysis of Weather Conditions over Different Locations The linear regression analysis was carried out to analyze the trends of temperature and rainfall using 14

Fig. 1: Comparison of Base line and Projected Weather at Different Locations

H.R. Patel

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Impacts of Climate Change on Growth and Yield of Rice in Middle Gujarat Agro-Climatic Region

Table 2: Percent Reduction in Different Parameters under Projected Climate over Base line Climate on Rice Across Various Locations Place Anand

Date of Sowing 15th July 30th July

Ahmeda-bad

15th July 30th July

Dahod

15th July 30th July

Panch-mahal

15th July 30th July

Vado-dara

15th July 30th July

Cultivars GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari GR-17 Gurjari

Lai -32 -39 -28 -30 -33 -28 -32 -23 -23 -41 -33 -47 -40 -48 -41 -51 -31 -36 -38 -34

Trends of Rice Yield, Yield Attributing Characters and Phenological Variation during Projected Period Impact on Anthesis Date The results indicated that rice crop showed advancement in anthesis date was seen at all study districts of Gujarat. Higher advancement in anthesis date was noted in Gurjari at Dahod, while it was lowest in Anand district. On and average mean anthesis date reduction advancement (irrespective of cultivars and dates of sowing) was 23, 25, 26, 33 and 35 % of base line at Anand, Vadodara, Ahmedabad, Panchmahal and Dahod respectively (Table 2). Impact on Maturity Date

Mat -12 -10 -19 -17 -18 -23 -22 -21 -15 -27 -18 -32 -19 -17 -14 -31 -15 -10 -22 -27

Bio-mass -16 -13 -21 -29 -18 -21 -20 -25 -31 -37 -33 -40 -25 -33 -38 -29 -27 -25 -28 -24

Grain Yield -20 -30 -24 -37 -28 -25 -31 -34 -30 -34 -32 -38 -31 -38 -32 -40 -32 -28 -34 -36

presented in Table 2. The model simulated results shows that (irrespective of cultivars and dates of sowing) mean grain yield will be reduced at Anand, Ahmedabad, Vadodara, Dahod and Panchmahal were 27.8, 29.7, 32.9, 33.8 and 35.4 respectively. The highest yield reduction (40.4%) was noted at Panchmahal district in late transplanting (30 July) and Gurjari cultivar, while it was lowest (20.2%) at Anand district under timely transplanting (15 July) in GR-17. It might be due to Anand district have more irrigated area as compare to Panchmahal and Dahod districts. Similar results were also reported by Mohandass et al,(1995). and Hundal et al,(1997) Impact on Biomass Yield

The simulation result showed that the crop duration of rice was reduced in all five districts ranged between 10% to 32% in different transplanting dates and varieties. Less reduction in maturity days was noted at onset of monsoon transplanting at all the location as compared to 15 days later transplanting. Similarly lower reduction in maturity days was noted in cv. Gurjari as compared to GR-17 at all location. On and average mean maturity days reduction (irrespective of date and cultivars) was 14, 18, 21, 20 and 23 % at Anand, Vadodra, Ahmedabad, Panchmahal and Dahod respectively (Table 2). Impact on Grain Yield The climate change impact on projected period of rice yield at Anand, Ahmedabad, Vadodara, Dahod and Panchmahal with % reduction from base line is

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Anth -33 -22 -23 -19 -26 -27 -23 -29 -29 -38 -31 -41 -27 -37 -29 -40 -19 -28 -22 -34

The simulation analysis indicated that rice is likely to lose the biomass yields ranged between 13.1 to 39.9 % at different districts in study area. The highest reduction was noticed at Dahod district and lowest was in Anand district. On an average mean biomass reduction (irrespective of cultivars and dates of sowing) was 20,26,21,33 &35 % under A2 Scenario as compare to baseline biomass yield at Anand, Vadodara, Ahmedabad, Panchmahal and Dahod respectively. (Table 2) Strategies for Adapting Rice to Climate Change The district-wise impacts, adaptation and net vulnerability are worked out and presented in Table 3. The adaptation strategies viz. fifteen days early transplanting from normal, change in variety, and better water management with additional fertilizer and early transplanting were tried for study.

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National Seminar on Climate Change Impacts on Water Resources Systems

Table 3: Adaptation and Vulnerability Analysis of Rice in Middle Gujarat Agro-Climate Region

-22.6

-20.6

-18.0

-26.5 -21.3 -28.2 -27.0

-28.6 -22.6 -29.8 -25.6

-22.6 -21.8 -25.5 -23.0

-20.3 -17.9 -21.8 -19.6

Early Transplanting from Normal The shifting of transplanting windows fifteen days early from the normal transplanting (15 July) adaptation benefited 6.3 to 8.8 % in different districts of study area highest benefited district was (8.8) Anand, while lowest was (6.3) in Vadodara district. The yield gain by adaptation of Anand, Vadodara, Ahmedabad, Panchmahal and Dahod was 8.8, 6.3, 8.4, 7.2 and 6.8% respectively. Change in Variety The analysis showed that change in variety alone can make farmers better equipped to face the climate change impacts on rice crop. Yield gain by change in variety (GR-417) in place of traditional variety in all districts of study areas was significantly increased. The yield gain by this adaptation strategy of Anand, Vadodara, Ahmedabad, Panchmahal and Dahod was 5.2, 4.2, 7.1, 5.6 and 8.2 % respectively. Better Water Management with Additional Fertilizer Nitrogen application and irrigation should be provided to suit the changed phenology of the crop in a changed environment. In this situation, the adaptation gains are projected to be up to 10.8% underA2 scenario in projected period (2071-2100). The highest yield gain (10.8%) by this adaptation was recorded at Dahod district, while it was lowest (7.9%) in Ahmedabad district. It might be due to Dahod district has lowest irrigated area and Ahmedabad has highest irrigated area. It may be noted that the application of additional nitrogen is particularly in the context of farmers who are applying less than the recommended dose of fertilizer.

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Even after Early Trans Planting Change in Variety Better Water Management with Additional Fertilizer

-19.0

12.5 11.8 13.6 14.2

Even after Change in Better Water Management with Additional Fertilizer

9.8

10.2 7.9 9.9 10.8

Even after Change in Change In variety

7.2

4.2 7.12 5.6 8.2

Even after Change in Planting date (A2 2071-2100)

5.2

6.3 8.4 7.21 6.8

Early Trans Planting Change in variety Better Water Management with Additional Fertilizer

8.8

-32.8 -29.7 -35.4 -33.8

Better Water Management with Additional Fertilizer

-27.8

Ahmedabd Dahod Panch-mahal Vado-dara

% Yield gain by Change in Variety

% Yield Gain by Adaptation 15 Days Early Trans Planting from Normal Transplanting Date (15 july)

Anand

Districts

% Yield Change in Projected Period (2071-2100) Under A2 Scenario from Baseline Yield (1960-90)

Net Vulnerability under A2 2071-2100 (Yield Reduction even after Adaptation)

Combine Effect of Early Trans Planting, Change In Variety, Better Water Management With Additional Fertilizer This is the combined above three adaptation strategies. The model simulated output showed highest benefited rice yield up to 14.2 %. The yield benefited by this adaptation at Anand, Vadodara, Ahmedabad, Panchmahal and Dahod was 9.8, 12.5, 11.8, 13.6 and 14.2 respectively. Similar result were observed by Aggarwal et al, (2002). Vulnerability Analysis The vulnerability showed that in Anand, Vadodara, Ahmedabad, Panchmahal and Dahod, the rice crop is projected to be vulnerable to climate change with a net vulnerability of up to 21.8 %. The highest (-21.8 %) vulnerable district for rice yield from current yield to climate change was Panchmahal, while lowest (-18.0 %) vulnerability was found in Anand district even after following all adaptation strategies like early transplanting, Change in variety, Better water management with additional fertilizer.

CONCLUSION The maximum and minimum temperature will be high under A2 scenario in 2071-2100 at Anand, Ahmedabad, Vadodara, Dahod, and Panchmahal to the tune of 6.0, 4.1, 4.0, 5.0, 4.5 and 5.1, 3.5, 4.3, 3.8, 4.10C, respectively against the base line periods (1961-90). While rainfall will be higher at of Anand, Ahmedabad, Vadodara, Dahod and Panchmahal districts to the 42.0, 28.14, 20.95, 70.0 and 49.7% respectively as compare to baseline period. The model simulated results shows that (irrespective of cultivars and dates of sowing) mean grain yield will be reduced at Anand, Ahmedabad,

H.R. Patel

NSCCIWRS

Impacts of Climate Change on Growth and Yield of Rice in Middle Gujarat Agro-Climatic Region

Vadodara, Dahod and Panchmahal are 27.8, 29.7, 32.9, 33.8 and 35.4 respectively. The adaptation strategies viz. fifteen days early transplanting from normal, change in variety, better water management with additional fertilizer and early transplanting change in variety and better water management with additional fertilizer, the crop yield benefitted 6 to 14%.

REFERENCES Agarwal, P.K. and Mall, R.K.(2002), Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climate Change, 52(3), 331–343. Aggarwal, P.K., (2008). Global Climate Change and Indian Agriculture: Impacts, adaptation and mitigation. Indian Journal of Agricultural Sciences 78(10): 911 -19 Anonymous (2011). District wise area, production and yield of important cereals crops, in Gujarat state, Krishi Bhawan, Sector 10-A, Gandhinagar. Data book. pp 230-245.

IPCC, (2007a): Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom & New York, NY, USA. IPCC, (2007b): Summary for Policymakers. In: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 7-22 Mohandass, S., Kareem, A.A., Ranganathan, T.B. and Jeyaraman, S.(1995) , Rice production in India under current and future climates. In Modeling the Impact of Climate Change on Rice Production in Asia, CAB International, UK, 1995, pp. 165–181.

Hundal, S.S. and Prabhjyot-Kaur (1997). Application of the CERES-ricet model to yield prediction inthe irrigated plains of the Indian Punjab, J. Agric.Science (Cambridge, UK) 129, 13-18.

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Climate Change Impact on Crop Water Requirements in South Saurashtra Region of Gujarat State Harji D. Rank Principal Investigator, National Initiatives on Climate Resilient Agriculture (NICRA), Research Engineer, AICRP on Groundwater Utilizations, Soil and Water Engineering Department, Junagadh Agricultural University, Junagadh [email protected]

Pankaj J. Rathod National Initiatives on Climate Resilient Agriculture (NICRA), Soil and Water Engineering Department, Junagadh Agricultural University, Junagadh [email protected]

Hiren P. Patel National Initiatives on Climate Resilient Agriculture (NICRA), Soil and Water Engineering Department, Junagadh Agricultural University, Junagadh [email protected]

ABSTRACT The climate change is not a hypothesis but a planet wide observation fact, and one of the main concerns is climatic variations impact on hydrological cycle. The crop water requirements depend on Reference evapotranspiration (ETo). The reference evapotranspiration depends on various climatic parameters. Therefore, the effort has been made to assess the climate change impact on reference evapotranspiration (ETo). The annual average weekly ETo of the region was found 33 mm/week. The minimum relative humidity(RHmin), mean relative humidity(RHmean), minimum temperature(Tmin), and mean temperature(Tmean) were found increasing and wind velocity(Wv), Pan evaporation(PE), and daily ETo were found deceasing significantly while the maximum temperature(Tmax), bright sunshine hours(BSS), maximum relative humidity (RHmax) and annual rainfall time series data could not show any significant trend. The expected decrease in annual ETo of this region may be 54.51 mm/10 years. During the standard week no. 14, 20, 21, 23, 24, 25 27, 28, 29, 34, 37, 38, and 39, no significant trends were found. During the 35th standard week, the weekly ETo was found increased at the rate of 0.051 mm/week per year while for the rest of the weeks, the ETo were found decreased at significant level. The Tmax was the weather parameter most influencing(r=0.88) the ETo.

KEYWORDS: Crop Water Requirements, Climate Change, Reference Evapotranspiration (ETo)

INTRODUCTION The Intergovernmental Panel on Climate Change (IPCC) has predicted that the global temperature would rise by 1.4 to 5.8 oC by the year 2100. Various studies showed that the observed warming trend during past decades occurred mainly due to the increase in the minimum temperatures rather than the maximum temperatures (Folland et al., 1992, Karl et al., 1991, Smith and Reynolds, 2005). The observed warming as well as the precipitation anomalies is not uniform on the globe. Regional differences make it reasonable to consider climate variability, in particular long-term series of meteorological variables in regions with different climatic conditions in the order to detect their

18

impact on hydrological parameters. The evapotranspiration is one of the important phases of hydrological cycle. The reference evaporation (ET0) has a great concern with agricultural production and crop water requirement. Therefore, the present study was planned to evaluate climate change and its impact on reference evapotranspiration (ETo).

METHODOLOGY Data Collection The historical records (1965-2010) of daily data of hydro meteorological parameters like maximum/ minimum temperature, maximum/ minimum relative humidity, wind velocity, bright sunshine hours, pan evaporation, rainfall etc. for Junagadh station (21.5oN latitude and 70.58o E longitude with 60m MSL) were collected from the JAU observatory, Junagadh.

Harji Rank

NSCCIWRS

Climate Change Impact on Crop Water Requirements in South Saurashtra Region of Gujarat State

Crop Water Requirements The water requirement of any crop for the particular duration is the multiplication of the reference evapotranspiration and its crop coefficient, both of that respective duration. The crop coefficient is more a genetic driven characteristic of crop plants. It may be somewhat influenced by soil types and by weather to very little extent. Therefore, the climate change impacts on reference evapotranspiration can be directly reflected on water demands in agriculture sector for the irrigation. So, in this paper an attempt is made to determine the reference evapo-transpiration on daily basis using historical daily weather data and the climate change impacts on it is assessed. Reference Evapotranspiration (ETo) The reference crop evapotranspiration (ETo) was calculated according to the FAO Penman-Monteith equation (Allen et al. 1998). ETo is the evapotranspiration rate from a hypothetical reference crop with an assumed crop height (12 cm), a fixed crop surface resistance (70 s m-1) and albedo (0.23), closely resembling the ET from an extensive surface of green grass cover that is in uniform height, actively growing, completely shading the ground and with adequate water supply. Thus the Penman-Monteinth equation as per Eq.(1) was used for calculation of ETo. ET = [0.418(Rn– G) + (900/(T + 273)) U2 (es − ea)] / [ +  (1 + 0.34U2)]

higher than a data value from an earlier time period, S was incremented by 1. On the other hand, if the data value from a later time period was lower than a data value sampled earlier, S wa s decremented by 1. The net result of all such increments and decrements gave the final value of S. Mann-Kendall statistic (S) was calculated by Eq.(2) =∑

∑

(

−

)

(2)

Sign (xj – xk)=1, 0 and -1, if xj>xk, xj = xk and xj < xk respectively. n is the number of data points in time series. A very high positive value of S was an indicator of an increasing trend, and a very low negative value indicated a decreasing trend. However, it was necessary to compute the probability associated with S and the sample size, n, to statistically quantify the significance of the trend. The variance of S, i. e. VAR(S) for each data series was estimated by the following Eq. (3).

∑

( )= [ (n (n − 1) (2n + 5) − (tp (tp − 1)(2tp 5)]

(3)

Where n was the number of data points, m was the number of tied groups (a tied group was a set of sample data having the same value), and t p was the number of data points in the p group. The normalized test statistic Z was computed as Eq.(4). = {[S − 1]/ [VAR(S)]½} if S > 0

(1)

= = == {[S + 1]/ [VAR(S)]½} if S < 0

Where, Rn is the net radiation, at the crop surface (MJ m-2 per day), G is the soil heat flux (MJ m-2 per day), T is the average air temperature (oC), U2 is the wind speed at 2 m height (ms-1), (es-ea) is the vapor pressure deficit (KPa),  is the slope of the vapor pressure curve (KPa oC-1),  is the psychrometric constant (Kpa oC-1), and 900 is the conversion factor.

If Zcal >0 and Zcal > Ztab, where, Ztab = 3.090, 2.326, 1.645, .282, the trend was considered as increasing and if Zcal < 0 and -Zcal > Ztab the trend was considered as decreasing at 0.1%, 1%, 0.5 and 10 % respectively.

Mann-Kendall Analysis

Sen’s Slope Method

The Mann-Kendall test is a non-parametric test for identifying trends in time series data. The test compares the relative magnitudes of sample data rather than the data values themselves (Gilbert, 1987). One benefit of this test is that the data need not conform to any particular distribution. Moreover, data reported as non-detects can be included by assigning them a common value that is smaller than the smallest measured value in the data set. The procedure that will be described in the subsequent paragraphs assumes that there exists only one data value per time period.

The true slope of an existing trend (as change per year) was determined using the Sen's nonparametric method. The Sen’s method could be used in cases where the trend could be assumed to be linear as Eq.(5) below. ( )=

(5)

Where m is the slope and c is a constant. The slope mi within between two values of pair of all data value was estimated using the following Eq.(6).

The data values w e r e evaluated as an ordered time series. Each data value was compared to all subsequent data values. The initial value of the MannKendall statistic, S, wa s assumed to be 0 (e.g., no trend). If a data value from a later time period was NSCCIWRS

+

(4)

Harji Rank

=(

−

)

/( − )

(6)

Where, k=1,2, 3, ........(n-1). j= k+1=2, 3, ......n. i= 1 to N [N= n (n-1)/2] 19

National Seminar on Climate Change Impacts on Water Resources Systems

Table 1: Mann-Kenall and Sens Statistic for Annual Mean Weekly Climatic Parameters Weekly Kendall “S" Mann-Kendell Z Sens Parameter slope RH min 235 2.248476** 0.737637 RH mean 225 2.120866** 1.240385 Tmin 374 3.536856**** 0.581957 Tmean 263 2.480878*** 0.319575 Wind velocity -324 -4.25549**** -1.02633 PE -167 -2.20064** -0.18643 ETo -392 -5.14**** -1.94648 Tmax -103 -0.98503NS -0.0926 BSS 44 0.578673NS 0 RHmax 110 1.032075NS 0.968407 Annual total 145 1.363414* 108.76 Rainfall ****, ***, ** and * - Significant at 0.1%, 1%, 5% and 10% probability.

The Sen’s estimator of slope was estimated using the following Eq.(7)

(7)

Best Fit Trend Line Analysis The each time series data of weather parameter and its 3, 5, and 10 years moving average were analyzed for the best fit trend line. The slope of the best fit trend line was taken as slope of the change per year.

RESULTS AND DISCUSSION The trend analysis of annual mean temperature, relative humidity, rainfall, wind velocity, bright sunshine hours and ETo were carried out. The daily calculated ET0 were summed up for the respective 7 days of each standard week (1 to 52) to get weekly ETo. Each of 52 time series data of weekly ET0 was analyzed using MannKendall and Sens statistics as well as for best fit trend line. Intra-Year Variation of ETo The annual average ETo of the region was found 33 mm/week. The intra- year variation of ET0 was found as shown in Fig. 1. The highest ETo (51.28 mm/week) was found during the 17th standard week. The lowest of mean weekly ETo was found as 19.77 mm/week during 32nd week. The ETO was higher than annual mean (33 mm/week) during 8 to 26 and 43th standard week while it was lower than the annual mean during 1 to 7, 27 to 42 and 44 to 52 standard weeks. The ETO higher than the mean might be due to the dry warm climate during standard weeks of 8-26(March-June) and due to warm climate during 43th standard week(October). The ETo was lower than the annual mean during standard week 1 to 7 (Jan-Feb) and 44-52 week (Nov-Dec) due

20

Mean E T o (mm/week)

+

Trend Increasing Increasing Increasing Increasing Decreasing Decreasing Decreasing No Trend No Trend No Trend No Trend

Trend Slope 0.0659 0.0649 0.0612 0.0261 -0.0614 -0.0476 -0.2591 -0.0088 0.0001 0.0691 5.3015

R2 0.0836 0.0722 0.1533 0.1529 0.499 0.1954 0.1213 0.0174 0.0001 0.0473 0.0261

to cold weather and during 27 to 42(July-October) due to higher humidity.

= = ½

Confidence Level (%) 98.77 98.30 99.98 99.34 100.00 98.61 100.00 16.23 71.86 84.90 91.36

52 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 S ta na drd We e k

Fig. 1: The Variation of Mean ETo (mm/ Week) during the Year

Climate Change Trend Analysis The time series data of annual mean of weekly average maximum temperature, minimum temperature, mean temperature, maximum humidity, minimum humidity, pan evaporation, wind velocity, bright sunshine hours and ETo were obtained by averaging 52 values of the respective parameters. The time series data of annual rainfall was obtained by summing up the 365 daily values of the respective year and was analyzed. The Mann-Kendall and Sens statistics with best fit trend line results were found as shown in (Table 1). The data (Table 1) showed that the RHmin, RHmean, Tmin, and Tmean were found increasing at 5%, 5%, 0.1%, and 1% significant level respectively. The respective confidence level in increasing trend was found as 98.77, 98.30, 99.98 and 99.34%. The Wind velocity, Pan evaporation (PE), and daily ETo were found deceasing at 0.1%, 5%, and 0.1%, significant level respectively. The respective confidence level in decreasing trend was found as 100, 98.61, and 100%. The Tmax, BSS, RHmax and annual rainfall time series data could not show any sign cant trend below 5 % significant level.

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Table 2: Mann-Kenall and Sens Statistic for Annual ETo Parameter Kendall “S" Mann-Kendell Z Sens Slope Annual ETo -395 -5.17968**** -101.217 3 yma ETo -754 -7.63627**** -73.9112 5 yma ETo -715 -7.75957**** -69.0072 10 yma ETo -594 -7.78193**** -57.5514 yma- year moving average, **** - Significant at 0.1%, probability.

Confidence Level (%) 100 100 100 100

Based on the best fit trend line slope, the annual mean of weekly RHmin, RHmean, Tmin and Tmean will be increased by 0.659%, 0.649%, 0.612 oC and 0.261 oC in next the 10 years. However, annual average of wind velocity, PE, and ETo will be decreased by 0.614kmph, 0.476 mm/week and 2.591mm/week in the next 10 years.

2000

Trend Decreasing Decreasing Decreasing Decreasing

Annual Eto

3-Yma

R2 0.72 0.85 0.88 0.93

Trend Slope -5.8598 -5.7167 -5.7088 -5.4513

5 Yma

10 Yma

Annual ETo(mm/year)

1900

1800

1700

1600

Annual ETo

The Table.2 showed that for all the time series data, the values of slope of best fit trend line were negative which indicated that the annual ET0 was in decreasing trend. The Fig. 2 showed th at most of the data points closely hugged the trend line having negative slope. In fact it was also reflected in Mann-Kendell-Z and Sens slope statistics and R2 values shown in Table.2. The trend line of time series data of ET0, 3, 5 and 10 years moving showed that if same trend in climate change will continue, the expected decrease in annual ETo of this region may be 54.51 mm/10 years. The change prediction of ETo can be more reliable using 10 year moving average trend line due to its higher R2 (0.93) than others. Many evidences showed that ET0 decreased over the last decade in the world, such as in India (Chattopadhyay and Hulme,1997), China (Thomas, 2000; Chen et al., 2006, Gao et al., 2006). The same trend of pan evaporation was also found in the USA and across many part of the former Soviet union (Peterson et al., 1995; Golubev et al., 2001), China (Liu et al, 2004; Chen et al, 2005, India (Chattopadhyay and Hulme, 1997), Australia and Newzealand (Roderick and Farquhar, 2004) but a small significant increase in Israel (Cohen et al., 2002). It is not expected that evaporation and evapotranspiration would increase with temperature increasing. This contradiction is the so called “Pan Evaporation paradox”. Golubev et al. (2001) noted that actual evaporation has a increasing trend over Southern Russia and most of the united states during the past 40 years, supporting the explanation by Brutsaert and Parlange (1998) about the Pan evaporation paradox. NSCCIWRS

2010

2005

2000

1995

1990

1985

1980

1975

1970

1500

1965

The annual ET0 was obtained by summing up the weekly ETo of 52 standard week for each year from 1965 to 2010. The Mann-Kendall and Sens slope statistics as well as best fit trend line analysis for the time series data of annual ETo, its 3 , 5 and 10 years moving average were carried out and its significance was tested by R2 and M-K statistics (Table 2).

Year

Fig. 2: Annual ET0, 3,5 and 10 Year Moving Average Trend

The Fig. 2 showed that trend lines were decreasing from the year 1965 to 2010. The trend lines crossed the mean ETo line at year 1990 which indicated that ETo was below annual mean ETo from 1990 onwards. The decreasing trend of ETo indicates that the crop water requirements will be decreased in the future. In this region, the water is a scared resources and this will help to increase the irrigated area. Thus, the decreased trend of ETo is ultimately a good sign for increasing the carbon sequestration through enhanced irrigated agriculture. Weekly ETo Trend The ET0 trend of standard week time series data were analyzed for weekly ETo, its 3-years, 5-years and 10years moving average data series for each of 52 standard week were analyzed. The trend line of ET0, 3yrs, 5yrs, and 10 yrs moving average of 5, 11, 17, 23, 29, 35, 41 and 47 standard week are presented in Fog. 3. During the standard week no. 14, 20, 21, 23-25, 2729, 34, and 37-39, no significant change trends were found. During the 35th standard week, the weekly ETo was found increased at the rate of 0.51 mm/week/10year based on the 10 years moving average trend (R2 =0.5). However, for the rest of the weeks, the ETo were found decreased at significant level. During the standard week 43-52 and 1-8(winter season), the ETo was found decreased at the rate of 2.68, 1.26, 1.36, 1.46, 1.16, 1.07, 2.41, 1.1, 0.5, 1.23, 0.86, 1.9, 2.02, 1.4, 0.81, 1.55 and 0.75 mm/week/10year respectively. The Mann-Kendall and Sens slope test showed that the trends were found significantly decreasing at 0.1% significant level during standard week 44-50, 52, 1, 3, 5, and 6. However, during standard week 43, 51, 2 and 7, the trend of ETo were significantly decreased at 1% significant level.

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Ye ar

40

2005

2010

2005

2010

2005

2010

2000

Ye ar

Ye ar

std.Wk.29 5yrs

3yrs 10 yrs

30 Weekly average ET0

Weekly averageET0

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30

1990

35 1985

35

45

1980

40

50

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45

3yrs 10 yrs

55

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50

std.Wk.23 5yrs

60

1965

55

W eeklyaverageET0

WeeklyaverageET0

65

std.Wk.17 3yrs 5yrs 10 yrs

60

35

2010

Year

65

30

2005

1965

2015

2010

2005

2000

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1990

1985

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1960

12

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17

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22

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27

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32

1980

37

1975

42

std.Wk.11 3yrs 5yrs 10 yrs

60 55 50 45 40 35 30 25 20 15 10 1970

WeeklyaverageET0

47

WeeklyaverageET0

s td.Wk.5 3yrs 5yrs 10 yrs

52

30 25 20 15

25

20 std.Wk.35 3yrs 5yrs 10 yrs

15

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10

10

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15 2005

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25

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35

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45

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std.Wk.47

50

1980

10 yrs

1975

3yrs

5yrs

1970

std.Wk.41

1965

45

1965

Weekly average ET0

50

Ye a r

Y ear

Fig. 3: Trend of Weekly ETo during Different Standard Weeks

During standard week 8 to 26 (Summer season), the significantly decreasing trends were observed except no trend in standard week 14, 20, 21 and 23- 25. The weekly ETo were decreased at the rate of 0.84,

22

0.48, 0.79, 1.02, 0.78, 0.89, 1.34, 0.83, 1.45, 0.77, 1.64, 1.2 and 2.31 mm/week per 10 year in standard week 8 to 13, 15 to 19, 22 and 26, respectively.

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Climate Change Impact on Crop Water Requirements in South Saurashtra Region of Gujarat State

Table 4: Trend Analysis of Weekly ETo Std wk 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

Mann-Kendell Z Value -4.88557**** -2.973*** -4.94238**** -4.39322**** -3.78726**** -3.99556**** -2.95406*** -3.04874*** -2.78364*** -2.59427*** -2.72683*** -2.21555** -2.95406*** -1.26873NS -3.93875**** -2.65108*** -3.12449**** -1.68963** -2.31023** -0.60596NS -0.64383NS -2.25342** -0.66277NS -0.68171NS -1.17405NS -3.23811**** 1.174051NS 0.605962NS 0.605962NS -1.91257** -2.06406** -1.9315** -4.10918**** -1.49597 1.666395** -2.02618** 0.397662NS 0.757452NS -1.55278* -2.61321*** -2.02618** -3.48428**** -2.80257*** -4.52578**** -4.60152**** -3.93875**** -4.63939**** -3.20024**** -4.29854**** -3.42747**** -2.80257*** -4.2228****

NSCCIWRS

Confid-ence Level (%)

Sens slope

Trend

100.0 99.9 100.0 100.0 100.0 100.0 99.8 99.9 99.7 99.5 99.7 98.7 99.8 89.8 100.0 99.6 99.9 95.4 99.0 72.8 74.0 98.8 74.6 75.2 88.0 99.9 88.0 72.8 72.8 97.2 98.0 97.3 100.0 93.3 95.2 97.9 65.5 77.6 94.0 99.6 97.9 100.0 99.7 100.0 100.0 100.0 100.0 99.9 100.0 100.0 99.7 100.0

-3.264 -2.462 -3.729 -2.430 -2.183 -2.523 -1.678 -1.670 -1.695 -2.141 -1.578 -1.638 -2.400 -0.593 -2.757 -1.664 -2.291 -1.477 -2.176 -0.302 -0.302 -1.783 -0.651 -0.678 -1.258 -3.551 0.915 0.334 -0.623 -0.705 -0.986 -0.827 -2.088 -0.502 0.538 -0.775 0.153 0.342 -0.736 -1.589 -1.636 -2.724 -3.549 -2.034 -2.094 -2.581 -1.888 -1.453 -3.806 -1.646 -1.360 -2.486

Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing No Trend Decreasing Decreasing Decreasing Decreasing Decreasing No Trend No Trend Decreasing No Trend No Trend No Trend Decreasing No Trend No Trend No Trend Decreasing Decreasing Decreasing Decreasing Decreasing Increasing Decreasing No Trend No Trend No Trend Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing

ETo Trend slope -0.224 -0.236 -0.231 0.002 -0.131 -0.170 -0.092 -0.109 -0.110 -0.144 0.018 -0.099 -0.130 -0.026 -0.159 -0.097 -0.136 -0.092 -0.140 -0.040 -0.024 -0.106 -0.032 -0.113 -0.087 -0.264 0.068 0.042 -0.032 -0.010 -0.054 -0.045 -0.124 -0.008 0.022 -0.056 -0.021 -0.004 -0.057 -0.096 -0.101 -0.168 -0.228 -0.135 -0.147 -0.169 -0.133 -0.159 -0.246 -0.120 -0.081 -0.154

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R2 0.43 0.28 0.38 0.07 0.18 0.33 0.33 0.16 0.14 0.17 0.01 0.18 0.13 0.01 0.20 0.08 0.14 0.09 0.12 0.02 0.01 0.06 0.01 0.06 0.03 0.24 0.03 0.03 0.02 0.00 0.04 0.06 0.29 0.00 0.01 0.07 0.01 0.00 0.09 0.15 0.11 0.29 0.14 0.29 0.30 0.33 0.41 0.30 0.38 0.20 0.10 0.28

3 yma ETo Trend R2 slope -0.241 0.67 -0.241 0.37 -0.218 0.47 -0.156 0.54 -0.120 0.29 -0.171 0.57 -0.075 0.44 -0.099 0.24 -0.093 0.26 -0.140 0.27 -0.122 0.27 -0.078 0.18 -0.119 0.31 -0.033 0.03 -0.153 0.41 -0.099 0.16 -0.145 0.29 -0.082 0.21 -0.152 0.31 -0.057 0.09 -0.037 0.04 -0.106 0.15 -0.027 0.01 -0.110 0.20 -0.073 0.08 -0.266 0.48 0.079 0.07 0.059 0.13 -0.023 0.02 0.005 0.00 -0.042 0.06 -0.042 0.13 -0.132 0.54 -0.015 0.01 0.038 0.15 -0.036 0.09 -0.005 0.00 -0.016 0.01 -0.056 0.20 -0.103 0.40 -0.104 0.20 -0.166 0.56 -0.235 0.20 -0.147 0.49 -0.143 0.55 -0.152 0.59 -0.120 0.66 -0.139 0.50 -0.230 0.54 -0.100 0.36 -0.076 0.22 -0.138 0.52

5yma ETo Trend R2 slope -0.233 0.74 -0.215 0.40 -0.213 0.52 -0.153 0.59 -0.102 0.29 -0.169 0.66 -0.092 0.44 -0.095 0.29 -0.075 0.28 -0.131 0.28 -0.124 0.29 -0.103 0.22 -0.114 0.37 -0.029 0.03 -0.152 0.53 -0.099 0.20 -0.163 0.45 -0.076 0.31 -0.166 0.51 -0.074 0.24 -0.041 0.08 -0.132 0.30 -0.030 0.02 -0.105 0.23 -0.091 0.19 -0.272 0.59 0.088 0.10 0.075 0.31 -0.021 0.03 0.015 0.03 -0.039 0.08 -0.037 0.12 -0.135 0.68 -0.023 0.05 0.042 0.26 -0.031 0.10 0.001 0.00 -0.012 0.01 0.054 0.30 -0.104 0.53 -0.109 0.28 -0.174 0.70 -0.242 0.27 -0.146 0.55 -0.149 0.65 -0.152 0.62 -0.120 0.70 -0.123 0.64 -0.220 0.62 -0.102 0.55 -0.073 0.25 -0.132 0.68

10yma ETo Trend R2 slope -0.086 0.83 -0.190 0.38 -0.202 0.64 -0.140 0.67 -0.081 0.29 -0.155 0.69 -0.075 0.44 -0.084 0.35 -0.048 0.24 -0.079 0.17 -0.102 0.25 -0.078 0.18 -0.089 0.37 -0.003 0.00 -0.134 0.65 -0.083 0.29 -0.145 0.51 -0.077 0.46 -0.164 0.68 -0.076 0.37 -0.035 0.07 -0.120 0.36 -0.035 0.05 -0.116 0.38 -0.098 0.45 -0.231 0.76 0.114 0.17 0.080 0.49 -0.040 0.17 0.031 0.22 -0.017 0.09 -0.024 0.07 -0.123 0.76 -0.022 0.17 0.051 0.50 -0.019 0.13 0.010 0.05 0.018 0.03 -0.044 0.57 -0.106 0.77 -0.128 0.49 -0.188 0.82 -0.268 0.38 -0.126 0.69 -0.136 0.70 -0.146 0.64 -0.116 0.75 -0.107 0.69 -0.241 0.75 -0.110 0.78 -0.057 0.22 -0.123 0.73

23

National Seminar on Climate Change Impacts on Water Resources Systems

Table 5: Correlation Coefficient for Weather Parameters RHmean RHmean RH min RHmax Tmax Tmin Tmean BSS PE Wind Vel Rainfall Mean 57.39 StDev 16.37 * Significant at 0.05

RH min 0.98*

RHmax 0.95* 0.91*

Tmax -0.22 -0.33 -0.05

40.77 21.43

74.27 11.48

34.03 3.03

Tmin 0.69 0.61 0.78* 0.51

Tmean 0.40 0.30 0.54 0.79* 0.93*

Summary 19.68 26.86 5.07 3.55

During monsoon season(std wk 27 to 42), the significant decreasing trend was found at the rate of 0.31, 0.17, 0.24, 1.23, 0.22, 0.19, 1.06, 1.28, 1.88 mm/week per 10 year during 30-34, 36, and 40-42 standard week respectively. However, no significant trend could be found in cases of 27 to 29, and 37 to 39 standard week. The weekly ETo was increased significantly (5% level) by the tune of 0.51 mm/week per 10 year during 35th standard week.

CONCLUSION The following conclusions could be drawn from the present study. 



24

The average weekly ETo of the region was found as 33 mm/week with highest of 51.28 and lowest of 19.77 mm/week during 17th and 32 standard week respectively. The RHmin, RHmean, Tmin, and Tmean were found increasing and wind velocity, Pan evaporation (PE), and daily ETo were found deceasing significantly while the Tmax, BSS, RHmax and annual rainfall time series data could not show any significant trend below 5% significant level.

PE -0.49 -0.55 -0.39 0.77* 0.15 0.44 0.58

Wind 0.36 0.34 0.38 0.32 0.61 0.58 -0.34 0.40

Rainfall 0.76* 0.78* 0.69 -0.26 0.50 0.25 -0.82* -0.39 0.41

ETo -0.41 -0.50 -0.25 0.88* 0.30 0.59 0.59 0.92* 0.42 -0.39

7.1 2.72

6.37 1.54

7.61 2.64

2.55 4.26

4.71 1.42



The annual mean of RHmin, RHmean, Tmin and Tmean will be increased by 0.659%, 0.649%, 0.612 oC and 0.261 oC in next the 10 years.



The annual average of wind velocity, PE, and ETo will be decreased by 0.614kmph, 0.476 mm/week and 2.591mm/week in the next 10 years.



The expected decrease in annual ETo of this region may be 54.51 mm/10 years.



During the standard week no. 14, 20, 21, 23, 24, 25 27, 28, 29, 34, 37, 38, and 39, no significant trends were found. During the 35th standard week, the weekly ETo was found increased at the rate of 0.051 mm/week/year while for the rest of the weeks, the ETo were found decreased at significant level.



The Tmax was the weather parameter most influencing(r=0.88) the ETo.

Correlation among Weather Parameters The extent of intercorrelation among weather parameters were determined by the correlation coefficient which were as presented in Table-5. The correlation coefficient of ETo with RHmean, RHmin, RHmax, T max, Tmin, Tmean, BSS, PE, Wind speed and rainfall were found as 0.41, -0.5, -0.25, 0.88*, 0.3, 0.59, 0.59, 0.92*, 0.42 and -0.39 respectively. However, the Tmax was the most influencing weather parameter(r=0.88) to the ETo. The pan evaporation and ETo were correlated having coefficient of 0.92. The ETo deceased with increase in RHmean, RHmin, RHmax, rainfall and increased with increase in Tmin, Tmean, BSS, and wind speed. However, the correlation coefficient could not be found significant.

BSS -0.88* -0.92* -0.79* 0.46 -0.48 -0.15

ACKNOWLEDGEMENT The authors are thankful to ICAR, New Delhi for funding the research work under NICRA project. Authors are also thankful to Dr. D.D. Sahu, Agrometeorological Department, J.A.U, Junagadh for providing Meteorological data.

REFERENCES Allen R, Pereira L A, Raes D, Smith M (1998). “Crop evapotranspiration,”. FAO Irrigation and Drainage Paper No. 56, FAO, Rome. Brutsaert, WyParlange MB,(1998). “Hydrologic cycle explained the evaporation paradox,” Nature Vol 396, pp 30 Chattopadhyay N, Hulme M. (1997). “Evaporation and potential evaporation in india under conditions of recent and future climate change,” Agric. For Meteoro Vol 87, pp 55-73 Chen D,G., Gao, CY, XU J Guo, Ren G.( 2005). “Comparision of Thornthwaite method and pan data with standard Penman-Monteinth estimates of refrence evaporation in China,” Clim.Re, Vol 28, pp 123-132

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Chen SB, Liu Y, FyAxel T. (2006). “Climate change on Tibetian Plateau: Potential Evapotranspiration Trends from 1961-2000,” Climate changer, Vol 76, pp 291-319. Cohen S, Lanetz A, Stanhil G. (2002). “ Evaporative climate change at Bet Dagan, Israel:1964-1998,” Agric. For Meteoro Vol 111, pp 83-91. Folland CK, Karl TR, Nicholls N, Nyenzi BS, Parker DE, Vinnikov KY. (1992). “Observed climate variability and change,” Climate Change. The Supplementry Report to the Intergovernmental Panel on Climate Change Scientific Assessment, Cambridge University Press, Cambridge, pp: 135-170.

Karl TR, Kukla G, Razuvayev VN, Changery MJ, Quayle RG, Heim RR,. Easterling DR, Fu CB, (1991). “Global warming evidence for asymmetric diurnal temperature change,” Geophys. Res. Lett, Vol 18, pp 2253-2256. Kendall MG, (1975). Rank correlation methods, 4th ed. Charles Griffin, London. Liu B, Xu M, Henderson M, Gong W. (2004). “ A spatial analysis of pan evaporation trends in China,” J. Geophys.Res., Vol 109. 15102pp. Peterson TC, “Evaporation pp :687-688.

Golubev VS, Groisman PY. (1995). losing its strength. Nature,” Vol 377,

Gao Ge, Chen DL, Ren GY, Chen Y, Liao YM. (2006). “Trend of potential evaporation over china during 1956 to 2000,”. Geophys.Res Vol 25, No 3, pp 378 - 387( in Chinese with English abstarct)

Roderick ML, Farquhar GD. (2004). “Changes in Australian pan evaporation from 1970 to 2002,”. Int.J. Climatol, Vol 24, pp 1077-1090.

Gilbert RO. (1987). “Statistical methods for environmental pollution monitoring,” Van Nostrand Reinhold, New York.

Smith TM, Reynolds WR. (2005). “A global merged land-airsea surface temperature reconstruction based on historical observations (1980-1997),” J.climate, Vol 18, pp 2021-2036.

Golubev VS, Lawrimore JH, Groisman PY, Speranskaya NA, Zhuravin SA, Menne MJ, Peterson T, Maone RW. (2001). “Evaporation changes over contiguous United States and the former USSR: a reassessment,” Geophy.Res.Lett , Vol 28, pp 2665-2668.

Thomas A. (2000). “Spatial and temporal characteristics of potential evapotranspiration trend over china,” Int.J. Climatol. Vol 20, pp 381-396

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Drainage Coefficient using Probablistic Analysis for Patan, Siddhpur and Wagdod Raingauge Stations of Patan District, Gujarat Neha Patel Water Resources Engineering and Management Institute, Faculty of Technology and Engineering, The M.S. University of Baroda, Samiala [email protected]

Dilip Shete Civil Engineering Department, Parul Institute of Engineering & Technology, AT & PO Limbda [email protected]

ABSTRACT The present study aims in determining the best fit probability distribution for maximum one day to consecutive 2 to 7 and 10 days rainfall for the available 3 raingauge station namely Patan, Sidhpur and Wagdod in Patan district. The daily rainfall data varies from 1961 to 2008. The drainage coefficients ranges between 70-72 mm/day, 45-49 mm/day, 33-37 mm/day, 28-31 mm/day, 24-27 mm/day, 21-24 mm/day, 19-21 mm/day and 15-17 mm/day is expected to occur every two years for the crop grown and having tolerance of one day and consecutive 2 to 7 & 10 days respectively.

KEYWORDS: Rainfall Analysis, Probability Distribution; Inverse Gaussian, AIC, BIC, Drainage Coefficient; Return Period

engineering origins of frequency analysis with its present ostensibly ‘‘rigorous theory’’; some myths advanced under the banner of the latter were analyzed in greater detail in Part 2.

INTRODUCTION

According to Vivekanandan (2009) estimation of rainfall for a desired return period is one of the pre– requisite for planning and operation of various hydraulic structures. Frequency analysis approach is the effective and expedient tool for estimation of rainfall. The author studied the use of frequency analysis approach adopting six statistical distributions for estimation of rainfall for different return periods for Kakrapar, Roha and Sudhagad sites. Goodness of fit tests like Chi–square and Kolmogrov–Smirnov were used for checking the adequacy of fitting of the distributions to the recorded annual daily maximum rainfall data.. Similar studies (Barkotulla et al., 2009; Dabral and Pandey, 2008; Kwaku and Duke, 2007; Bhakar et al., 2006), have been carried out using three commonly used probability distributions, viz: Normal, Log Normal and Gamma distribution to determine the best fit probability distribution based on chi-square values for one to consecutive 2 to 7 days maximum rainfall. Deshpande et al. (2008) fitted Extreme Value Type-I (EV1) distribution, to 1-3 day extreme rainfall series and various return period values were estimated.

The climate of Patan district is characterized by a hot summer and general dryness in the major part of the year. Summer is very hot and winter is too cold. The annual rainfall varies between 59 mm to 1,713 mm for a period from 1961 to 2008. The rainy season being short (July–August), the biggest problem in both agriculture and daily life is water. The water is very precious due to scarce and bad quality of ground water. This region is one of the drought prone areas in Gujarat state. The mean daily maximum temperature is about 410C and the minimum is about 80C. Determining past records of hydrologic events in terms of future probabilities of occurrence is the most important problem in hydrology. Frequency analysis is carried out to determine the probability of occurrence of hydrologic event. It involves design of reservoirs, floodways, bridges, culverts, highways, levees, drainage systems, irrigation systems, water supply systems, drought mitigation programs etc requiring frequency analysis. Klemes (2000a and 2000b) in his two companion papers discussed the common frequency analysis techniques for hydrological extremes in particular, the claims that their increasingly refined mathematical structures had increased the accuracy and credibility of the extrapolated upper tails of the fitted distribution models over and above that achieved by the 50–year old empirical methods. Part 1 compared the common sense 26

Patel and Shete (2011; 2008) examined 16 different types of continuous probability distributions using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for goodness of fit for daily rainfall data of Mehsana and Sabarkantha district of

Neha Patel

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Drainage Coefficient using Probablistic Analysis for Patan, Siddhpur and Wagdod Raingauge Stations of Patan District, Gujarat

Gujarat, India respectively. Suhaila and Jemain (2008) presented several types of exponential distributions to describe rainfall distribution in Peninsular Malaysia over a multi-year period. The exponential, gamma, mixed exponential and mixed gamma distributions were compared to identify the optimal model for daily rainfall amount based on data recorded at raingauge stations in Peninsular Malaysia. The models were evaluated based on the Akaike Information criterion (AIC). Kullar et al. (1999) carried out the goodness of fit of three frequency distribution models viz. Lognormal 3-parameters, Lognormal 2-parameters and Extreme Value type 1 and concluded that Lognormal 3 -parameters distribution model was the best distribution model for all the three duration of rainfall (viz maximum 1-day, maximum 2-day and maximum 3-day) for Punjab. For the present study 3 raingauge stations having varying daily rainfall data from 1961 to 2008, situated in Patan district of Gujarat, India are analyzed. Sixteen different probability distributions are tested to determine the best fit probability distribution for maximum one day to consecutive 2 to 7 and 10 days rainfall. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) is used for the selection of best probability distribution function.

STUDY AREA The study area comprises of raingauge stations situated in Patan district of Gujarat state in western India. The Patan District has an area of 5,742.59 km2. The forest area is of 438.36 km2, the irrigated area is of 1,012.62 km2 and unirrigated area accounts for 2,966.41 km2. The district is surrounded by Banaskantha district to the north, Mehsana district to the east, Surendranagar district to the south and Kachchh district to the west. The study area is presented in Figure 1. The climate of this district is characterized by a hot summer and general dryness in the major part of the year. Summer is very hot and winter is too cold. The mean daily maximum temperature is about 410 C and the minimum is about 80 C. The annual rainfall varies between 59 mm to 1,713 mm for a period from 1961 to 2008.The data for the analysis is obtained from State Water Data Centre, Gandhinagar, India. There are 3 raingauge stations having daily rainfall data availability varying from 1961 to 2008. The details of the raingauge stations are given in Table 1.

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Fig. 1: Location Map of Study Area Table 1: Details of the Raingauge Stations in Patan District Sr. Station Latitude Longitude Data Available No. Name N E From To 1 Patan 23°51'21" 72°06'58" 1961 2008 2 Sidhpur 23°54'35" 72°21'30" 1961 2008 3 Wagdod 23°59'16" 72°09'15" 1971 2005

Years 48 48 45

METHODOLOGY The daily rainfall data of 3 raingauge stations contains missing records. The missing data are filled using artificial neural network. The methodology for determing missing records is dealt as per Patel et al. (2010). The complete daily point rainfall dataset thus obtained is used for the analysis by converting it to consecutive 2, 3, 4, 5, 6, 7 and 10 days rainfall. Fig. 2 shows the illustration for determining consecutive 2 days maximum rainfall total. Similar procedure is adopted for determining consecutive 4, 5, 6, 7 and 10 days maximum rainfall total. Sixteen different probabilities namely Birnbaum– Saunders (BS), Exponential (EX), Extreme value (EV), Gamma (GA), Generalized extreme value (GE), Generalized Pareto (GP), Inverse Gaussian (IG), Logistic (LO), Log–Logistic (LL), Lognormal (LN), Nakagami (NA), Normal (NO), Rayleigh (RA), Rician (RI), t location–scale (TL) and Weibull (WE) are applied to the dataset of one, consecutive 2,3,4,5,6,7 and 10 days rainfall. The conventional goodness of fit tests such as Kolmogrov- Smirnov, Anderson darling and Chi-square are based on hypothesis testing and has its own limitations. Measures of information, such as the Akaike information criteria (AIC) and Bayesian’s Information Criterion (BIC) were developed for selecting the optimum model (Patel and Shete 2011).

Fig. 2: Illustration for Computing Consecutive 2 Days Maximum Rainfall

Neha Patel

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National Seminar on Climate Change Impacts on Water Resources Systems

The AIC/ BIC is not a test on the model in the sense of hypothesis testing; rather it is a tool for model selection. Many researchers have shown the importance of using AIC and BIC for model selection (Stone, 1979; Atkinson, 1980; Bozdogan, 1987; Thomas et al., 1994; Buckland et al., 1997; Zucchini, 2000; Pan, 2001; Burnham Anderson, 2002; Dayton, 2003; Wang and Liu, 2006). The AIC and BIC are given by Eqs. (1) and (2)

AIC  2k  2In( L)

(1)

where k is the number of parameters in the statistical model, and L is the maximized value of the likelihood function for the estimated model.

BIC  2  ln L  k ln(n)

(2)

where n is the number of observations, or equivalently, the sample size; k is the number of free parameters to be estimated and L is the maximized value of the likelihood function for the estimated model.

RESULTS AND ANALYSIS The statistical measures of the dataset for one and consecutive 2,3,4,5,6,7 & 10 days maximum rainfall (1D, 2CD, 3CD, 4CD, 5CD, 6CD, 7CD & 10CD) i.e. mean, minimum, maximum, range, skewness, kurtosis, etc. are calculated and the results are presented in Tables 2 to 4.

Type of Data 1D 2CD 3CD 4CD 5CD 6CD 7CD 10CD

Min Max Mean

15 19 19 26 26 26 26 26

436 470 485 486 487 488 488 596

106.03 135.71 148.31 159.84 169.46 179.41 189.53 218.75

for

Patan

Skewness Value Std. Error 2.30 0.34 2.07 0.34 1.88 0.34 1.73 0.34 1.59 0.34 1.53 0.34 1.31 0.34 1.49 0.34

Raingauge

Kurtosis Value Std Error 7.32 0.67 4.78 0.67 4.15 0.67 3.56 0.67 3.18 0.67 2.94 0.67 2.26 0.67 2.54 0.67

From the Tables 2 to 4 it is observed that Patan raingauge station observed the highest maximum one day rainfall. Wagdod observed the highest maximum consecutive 2 to 7 & 10 days rainfall. It is also seen that Patan raingauge station observed the lowest minimum one day to consecutive 2 to 7 & 10 days rainfall. The mean of 1 day and consecutive 2 to 7 & 10 days rainfall for Wagdod raingauge station is the highest among the 3 raingauge stations. Lowest mean rainfall is observed, at Patan for one day and consecutive 2 to 7 & 10 days rainfall. The standard deviation varies from 75.94 mm

28

Table 3: Descriptive Statistics Station, mm Type of Data 1D 2CD 3CD 4CD 5CD 6CD 7CD 10CD

Min Max Mean

20 33 33 36 41 41 41 41

348 436 458 460 485 495 499 624

110.40 145.54 166.72 177.90 191.01 204.51 214.26 247.32

Type of Data 1D 2CD 3CD 4CD 5CD 6CD 7CD 10CD

Min Max Mean

20 20 27 27 27 27 27 27

300 475 548 568 601 621 649 819

114.80 156.24 180.24 189.37 198.10 210.00 220.95 260.30

for

Sidhpur

Skewness Value Std. Error 1.36 0.35 1.46 0.35 1.30 0.35 1.20 0.35 1.07 0.35 0.88 0.35 0.79 0.35 1.03 0.35

Table 4: Descriptive Statistics Station, mm

The BIC is expressed as

Table 2: Descriptive Statistics Station, mm

at Patan to 194.65mm at Wagdod considering all the raingauge stations for all 24 datasets.

for

Raingauge

Kurtosis Value Std Error 1.20 0.68 1.75 0.68 1.50 0.68 1.31 0.68 1.08 0.68 0.55 0.68 0.44 0.68 0.80 0.68

Wagdod Raingauge

Skewness Value Std. Error 0.92 0.38 1.15 0.38 1.19 0.38 1.14 0.38 1.13 0.38 1.04 0.38 0.98 0.38 1.27 0.38

Kurtosis Value Std Error -0.23 0.75 0.55 0.75 0.41 0.75 0.38 0.75 0.52 0.75 0.38 0.75 0.35 0.75 1.18 0.75

Skewness characterizes the degree of asymmetry of a distribution around its mean. All the stations are positively skewed. The values show significant skeweness since two times the standard error values are lower than the skew statistic. Similarly the non-shaded kurtosis values are significant and are positive indicating relatively peaked distribution. Only one day maximum rainfall value at Wagdod shows negative Kurtosis, which is insignificant with respect to the standard error value. Kurtosis characterizes the relative peakedness or flatness of a distribution compared to the normal distribution. The Patan raingauge station has highest kurtosis for one day and consecutive 2 to 7 & 10 days rainfall. For the stations which show significant positive skewness indicates a distribution with an asymmetric tail extending towards more positive values i.e. towards right and positive kurtosis indicates a relatively peaked distribution. Thus skewness and kurtosis values obtained indicates violations of the assumption of normality that underlies many of the other statistics like correlation coefficients, t-tests, etc. used to study test validity. Hence non parameteric indicators were considered for the best distribution selection viz. AIC and BIC. It is observed that all distribution functions significantly fitted the dataset. Tables 5 and 6 presents the ranking of fitted distributions according to AIC and BIC respectively for Patan raingauge station. Similar results are obtained for Sidhpur and Wagdod raingauge stations.

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Drainage Coefficient using Probablistic Analysis for Patan, Siddhpur and Wagdod Raingauge Stations of Patan District, Gujarat

Table 5: Ranking of Sixteen Probability Distributions based on AIC for Patan Raingauge Station Probability Distribution BS EX EV GA GE GP IG LL LO LN NA NO RA RI TL WE

1 D

6 13 16 5 4 14 1 2 12 3 9 15 10 11 8 7

2 CD

5 13 16 6 4 14 1 2 12 3 9 15 10 11 8 7

3 CD

6 14 16 5 4 13 1 2 12 3 9 15 8 11 10 7

4 CD

6 15 16 3 4 14 1 2 12 5 9 13 7 10 11 8

5 CD

10 15 16 3 4 14 1 2 12 6 8 13 5 9 11 7

6 CD

11 15 16 3 4 14 1 2 12 9 7 13 5 8 10 6

7 CD

12 15 16 3 5 14 1 4 11 10 7 13 2 8 9 6

10 CD

12 15 16 3 4 13 1 2 11 10 7 14 5 8 9 6

in Tables 7 & 8. From Table 7 the value of µ ranges from 106.034 to 260.300 and from Table 8 value of  ranges from 159.676 to 511.664 for 24 datasets. Table 7: Scale Parameter, µ for One Day and Consecutive 2, 3, 4, 5, 6, 7 & 10 Days Type of Data 1D C2D C3D C4D C5D C6D C7D C 10 D

1 D

2 CD

3 CD

4 CD

5 CD

6 CD

7 CD

10 CD

5 13 16 4 6 14 1 2 12 3 9 15 8 11 10 7

4 12 16 5 6 14 1 2 13 3 9 15 8 11 10 7

5 13 16 4 6 14 1 2 12 3 9 15 8 10 11 7

6 14 16 3 7 15 1 2 12 4 9 13 5 10 11 8

10 15 16 4 6 14 1 2 12 5 8 13 3 9 11 7

11 15 16 4 5 14 1 2 12 9 7 13 3 8 10 6

12 15 16 3 8 14 1 4 10 9 6 13 2 7 11 5

12 15 16 4 6 14 1 3 11 9 7 13 2 8 10 5

From Tables 5 and 6, one can say that Inverse Gaussian is the best fitted distribution for all the stations for one day and consecutive 2 to7 & 10 days according to AIC and BIC. Each station has eight dataset (one day and consecutive 2 to 7 &10 days). Thus 24 different datasets have been analyzed for three raingauge stations. One can say that though there are different datasets for different raingauge stations the best fitted distribution at the first place is unique i.e. Inverse Gaussian according to both AIC and BIC. For the same distribution, there are varying populations described by the 24 different datasets having different values of the distribution parameters, µ and  presented

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Sidhpur 110.402 145.540 166.725 177.903 191.007 204.511 214.261 247.316

Wagdod 114.798 156.244 180.238 189.371 198.098 209.997 220.948 260.300

Table 8: Shape parameter,  for One Day and Consecutive 2, 3, 4, 5, 6, 7 & 10 Days Type of Data 1D C2D C3D C4D C5D C6D C7D C 10 D

Table 6: Ranking of Sixteen Probability Distributions based on BIC for Patan Raingauge Station Probability Distribution BS EX EV GA GE GP IG LL LO LN NA NO RA RI TL WE

Patan 106.034 135.714 148.306 159.845 169.465 179.409 189.535 218.752

Patan 183.183 243.605 281.090 348.914 383.784 396.909 402.460 398.623

Sidhpur 177.895 268.553 318.628 385.486 434.116 444.915 505.607 511.664

Wagdod 159.676 180.055 202.765 224.187 246.843 285.959 296.379 316.414

Hence based on the sample data of 24 raingauge stations, the 16 different distribution functions describing the population were determined. Thus it can be concluded that all the 16 distributions fitted the dataset of 73 raingauge stations and a unique distribution was identified at the first place as the best fit according to AIC and BIC. The commonly used extreme value and exponential distributions were the least ranked amongst the 16 distributions. For the design of the hydraulic and water conservation structures the required amount of rainfall depth (design rainfall) for the intended design return period can be obtained by considering 15 % runoff for economical considerations. Hence the rainfall depth–duration– return periods for all the 73 raingauge stations developed can be used as a guideline for planning the water resources in the area. In particular, these values could be very beneficial during the construction of drainage systems in the area as poor drainage has been identified as one of the major factors causing flooding. Bhakar et al. (2006), recommended that 2 - 100 years is a sufficient return period for soil and water conservation measures, construction of dams, irrigation and drainage works. Table 9 presents the one day and consecutive 2, 3, 4, 5, 6, 7 and 10 days rainfall for 1.01, 2, 5, 10, 20, 50, 75 and 100 years return period.

Neha Patel

29

National Seminar on Climate Change Impacts on Water Resources Systems

Table 9: Return Period and Rainfall for One and Consecutive 2, 3, 4, 5, 6, 7 & 10 Days Return Period, Years

1.01

Patan Sidhpur Wagdod

19 19 17

Patan Sidhpur Wagdod

25 27 20

Patan Sidhpur Wagdod

28 32 23

Patan Sidhpur Wagdod

34 37 25

Patan Sidhpur Wagdod

36 41 28

Patan Sidhpur Wagdod

38 43 31

Patan Sidhpur Wagdod

39 48 33

Patan Sidhpur Wagdod

41 50 36

2

5

10

20

50

One Day, mm 82 151 205 263 343 84 157 217 279 367 85 165 231 303 405 Consecutive Two Days, mm 106 192 261 333 432 115 206 278 354 458 110 225 326 437 597 Consecutive Three Days, mm 118 210 282 358 462 132 236 317 401 518 126 260 378 508 696 Consecutive Four Days, mm 130 224 296 370 471 145 249 330 413 526 134 273 394 526 715 Consecutive Five Days, mm 139 237 312 388 492 157 267 351 437 554 142 285 408 542 733 Consecutive Six Days, mm 147 251 331 414 526 167 287 379 474 604 155 302 426 559 748 Consecutive Seven Days, mm 154 266 353 442 565 177 299 391 485 613 162 318 449 591 792 Consecutive Ten Days, mm 172 310 420 534 692 200 348 463 582 745 186 375 539 717 973

75

100

379 407 453

406 436 487

477 506 672

510 540 726

510 571 783

544 609 847

517 577 804

550 614 868

540 607 821

573 645 886

577 662 836

613 705 900

620 671 886

660 712 954

765 818 1092

817 872 1178

For the raingauge stations situated in Patan district the maximum rainfall of 82 mm, 106, 118 mm, 130 mm, 139 mm, 147 mm, 154 mm and 172 mm is expected to occur every two years for one day and consecutive 2 to 7 & 10 days respectively at Patan raingauge station. It is observed that this rainfall depth is the lowest amongst the three raingauge stations in the Patan district. The highest maximum rainfall of 85 mm, 115 mm, 132 mm, 145 mm, 157 mm, 167 mm, 177 mm and 200 mm is expected to occur every two years for one day & consecutive 2 to 7 & 10 days respectively at Sidhpur raingauge station. The maximum rainfall of 406 mm, 510 mm, 544 mm, 550 mm, 573 mm, 613 mm, 660 mm and 817 mm is expected to occur every 100 years for one day and consecutive 2 to 7 & 10 days respectively at Patan raingauge station. These rainfall depths are the lowest amongst the three available raingauge stations in the Patan district. The highest maximum rainfall of 487 mm, 726 mm, 847 mm, 868 mm, 886 mm, 900 mm, 954 mm and 1178 mm is expected to occur every 100 years for one day and consecutive 2 to 7 & 10 days respectively at Wagdod raingauge station. Based on the best fitted probability distribution figs. 3 to 5 presents the rainfall depth–duration–return period curves for 30

Patan, Sidhpur and Wagdod raingauge stations in Patan district.

Fig. 3: Rainfall depth–Duration–Return Period Curves for Patan, Raingauge Stations

Fig. 4: Rainfall depth–duration–Return Period Curves for SIDHPUR Raingauge Stations

It is assumed that 15 % of the rainfall infiltrates into the soil and the remaining 85 % constitutes the runoff (Source: Technical Advisory Report 2009, Sardar Krushinagar Dantiwada Agriculture University). Thus the drainage coefficient for Patan raingauge station is 70 mm/day (=82 x0.85), 45 mm/day (=106x0.85/2), 33 mm/day (=118x0.85/3), 28 mm/day (=130x0.85/4), 24 mm/day (=139x0.85/5), 21 mm/day (=147x0.85/6), 19 mm/day (=154x0.85/7) and 15 mm/day (=172x0.85/10) is expected to occur every two years for the crop grown and having tolerance of one day and consecutive 2 to 7 & 10 days respectively.

Fig. 5: Rainfall Depth–Duration–Return Period Curves for Wagdod Raingauge Stations

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Drainage Coefficient using Probablistic Analysis for Patan, Siddhpur and Wagdod Raingauge Stations of Patan District, Gujarat

Table 10: Drainage Coefficients for one and consecutive 2, 3, 4, 5, 6, 7 & 10 days at Patan, Sidhpur and Wagdod Raingauge Stations Return Period, Years Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod Patan Sidhpur Wagdod

1.01

2

5

10

20

50

One Day, mm/day 16 70 128 174 224 292 16 71 133 184 237 312 14 72 140 196 258 344 Consecutive Two Days, mm/day 11 45 82 111 142 184 11 49 88 118 150 195 9 47 96 139 186 254 Consecutive Three Days, mm/day 8 33 60 80 101 131 9 37 67 90 114 147 7 36 74 107 144 197 Consecutive Four Days, mm/day 7 28 48 63 79 100 8 31 53 70 88 112 5 28 58 84 112 152 Consecutive Five Days, mm/day 6 24 40 53 66 84 7 27 45 60 74 94 5 24 48 69 92 125 Consecutive Six Days, mm/day 5 21 36 47 59 75 6 24 41 54 67 86 4 22 43 60 79 106 Consecutive Seven Days, mm/day 5 19 32 43 54 69 6 21 36 47 59 74 4 20 39 55 72 96 Consecutive Ten Days, mm/day 3 15 26 36 45 59 4 17 30 39 49 63 3 16 32 46 61 83

75

100

322 346 385

345 371 414

203 215 286

217 230 309

145 162 222

154 173 240

110 123 171

117 130 184

92 103 140

97 110 151

82 94 118

87 100 128

75 81 108

80 86 116

65 70 93

69 74 100

It is observed that the drainage coefficients are the lowest amongst the 3 raingauge stations. Similarly the highest drainage coefficient obtained are 72 mm/day at Wagdod and 49 mm/day, 37 mm/day, 31 mm/day, 27 mm/day, 24 mm/day, 21 mm/day and 17 mm/day is expected to occur every two years for one day & consecutive 2 to 7 & 10 days respectively at Sidhpur. For a recurrence interval of 100 years, the lowest drainage coefficient of 345 mm/day, 217 mm/day, 154 mm/day, 117 mm/day, 97 mm/day, 87 mm/day, 80 mm/day and 69 mm/day for the crop grown and having tolerance of one day and consecutive 2 to 7 & 10 days respectively is determined. The highest drainage coefficient of 414 mm/day, 309 mm/day, 240 mm/day, 184 mm/day, 151 mm/day, 128 mm/day, 116 mm/day and 100 mm/day is expected to occur at interval of 100 years for the crop grown and having tolerance of one day and consecutive 2 to 7 and 10 days respectively.Table 10 presents the drainage coefficients for one and consecutive 2, 3, 4, 5, 6, 7 & 10 days at Patan, Sidhpur and Wagdod raingauge stations at different return periods.

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CONCLUSION Based on descriptive statistics the skewness and kurtosis values obtained indicates violations of the assumption of normality that underlies many of the other statistics like correlation coefficients, t-tests, etc. used to study test validity. Hence non parameteric indicators were considered for the best distribution selection viz. AIC and BIC. It is observed that all distribution functions significantly fitted the dataset. As per AIC and BIC, Inverse Gaussian distribution is found to be the best fitted for one day and consecutive 2 to 7 & 10 days of annual maximum rainfall for all the three raingauge stations of Patan District, Gujarat, India. The commonly used extreme value and exponential distributions are the least ranked amongst the 16 distributions according to AIC and BIC values. Based on the design parameters of the hydraulic and water conservation structures the required amount of rainfall depth/ drainage coefficient for the intended design return period were obtained for economical considerations.

ACKNOWLEDGEMENT Authors acknowledge with thanks the help rendered by the state water data centre, Gandhinagar for providing the required data of raingauge stations of Patan district of north Gujarat region for the analysis.

REFERENCES Atkinson, AC (1980). “Note on the Generalized Information Criterion for Choice of a Model,” Biometrika, Biometrika Trust 67(2), pp 413-418. Barkotulla, MAB, Rahman MS and Rahman MM (2009). “Characterization and Frequency Analysis of Consecutive Days Maximum Rainfall at Boalia, Rajshahi and Bangladesh,” J. of Development and Agricultural Economics, 1(5), pp 121-126. Bhakar, SR, Bansal, AK, Chhajed, N and Purohit, RC (2006). “Frequency Analysis of Consecutive Days Maximum Rainfall at Banswara, Rajasthan, India,” ARPN J. of Engineering and Applied Sciences 1 (3), Asian Research Publishing Network, ISSN 1819-6608, pp 64-67. Bozdogan , H (1987). “Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions,” Psychometrika, Springer New York , 52 (3), pp 345-370 Buckland, ST, Burnham, KP and Augustin, NH (1997). “Model Selection: An Integral Part of Inference,” Biometrics, International Biometric Society, 53(2), pp 603-618. Burnham, KP, and Anderson, DR (2002). “Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach,” 2nd ed. New York: Springer-Verlag.

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Dabral, PP and Pandey, A. (2008) “Frequency Analysis for One Day to Seven Consecutive Days of Annual Maximum Rainfall for the District of North Lakhimpur, Assam,” IE(I) Journal-AG 89, pp 29-34

Patel, NR and Shete, DT (2011). “Selection of Optimum Order of Markov Chain for Sabarkantha District, Gujarat, India,” International Journal of Water Resources and Environmental Management, 2(1), pp 103-115.

Dayton, CM (2003). “Model Comparisons using Information Measures,” J. of Modern Applied Statistical Methods, 2(2), pp 281-292.

Patel, NR, Suryanarayana, TMV and Shete, DT (2010). “Evaluating Best Method for Filling Missing Daily Rainfall for Banaskantha District, Gujarat, India,” Proceedings of Water 2010 : Hydrology, Hydraulics and Water Resources in An Uncertain Environment, ISSH-2010, ICWRER-2010, Canada

Deshpande, NR, Kulkarni, BD, Verma, AK and Mandal, BN (2008). “Extreme Rainfall Analysis and Estimation of Probable Maximum Precipitation (PMP) by Statistical Methods over the Indus River Basin in India,” J. of Spatial Hydrology. 8 (1), pp 22-36. Klemes, V (2000a). “Tall Tales about Tails of Hydrological Distributions I.” J. of Hydrologic Engineering, 5(3), pp 227–231. Klemes, V (2000b). “Tall Tales about Tails of Hydrological Distributions II,” J. of Hydrologic Engineering, 5(3), pp 232–239. Kwaku, XS and Duke, O (2007) “Characterization and Frequency Analysis of One Day Annual Maximum and Two to Five Consecutive Days’ Maximum Rainfall of Accra, Ghana,” J. of Engineering and Applied Science, Asian Research Publishing Network, ISSN 1819-6608, 2 (5), pp 27-31. Thomas, LM, Stuart, BL and Lewis, SB (1994). “Comparison of the Akaike Information Criterion, the Schwarz criterion and the F Test as Guides to Model Selection,” J. of Pharmacokinetics and Pharmacodynamics, Springer Netherlands, 22(5), pp 431-445 Pan, W (2001). “Akaike's Information Criterion in Generalized Estimating Equations,” Biometrics, International Biometric Society, 57(1), pp 120-125.

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Patel, NR and Shete, DT (2008). “Probability Distribution Analysis of Consecutive Days Rainfall Data for Sabarkantha District of North Gujarat Region, India,” ISH J. of Hydraulics, Indian Society of Hydraulics 14 (3), pp 43-55. Stone, M (1979). “Comments on Model Selection Criteria of Akaike and Schwarz,” J. of the Royal Statistical Society Series B (Methodological) 41(2), pp 276-278. Suhaila, J and Jemain, AA (2008). “Fitting the Statistical Distribution for Daily Rainfall in Peninsular Malaysia based on AIC Criterion,” J. of Applied Sciences Research, 4 (12), pp 1846-1857. Wang, Y and Liu, Q (2006) Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock–recruitment relationships,” Fisheries Research, 77(2), pp 220-225. Vivekananadan, N (2009). “Comparison of T-year Rainfall using Frequency Analysis Approach,” J. of Indian Water Resources Society, 29(1), pp 22–27.Zucchini, W (2000). “An Introduction to Model Selection,” J. of Mathematical Psychology, 44(1), pp. 41-61.

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Impact Assessment of Projected Climate Change on Pearl Millet in Gujarat S.B. Yadav [email protected]

H.R. Patel, M.M. Lunagaria, P.K. Parmar, N.J. Chaudhari, B.I. Karande and V. Pandey Department of Agricultural Meteorology, B.A. college of Agriculture, Anand Agricultural University, Anand–388110, Gujarat

ABSTRACT The impact of projected climate change (2071-2100) of A2 scenario and its likely impact on pearl mille yield of Saurashtra region of Gujarat using PRECIS output of A2 and Baseline (1960-1990) were studied. Baseline and A2 scenario yields were simulated using DSSAT (v4.5) model. The pearl millet field experiment data of kharif and summer seasons 2009 to 2011 were used of Millet Research Station Jamnagar of Junagadh agricultural University, Gujarat to calibrate and validate the model. The study was extended to the regional level for Rajkot, Bhavanagar, Junagadh and Bhuj districts to simulate pearl millet yields. Widely cultivated pearl millet cv. MH-1655 and GHB-732 during kharif with two dates of sowing (D1 – Onset of monsoon and D2 – 15 days after D1) and for summer GHB-558, and MSH-226 with two dates of sowing (D1 – 15 February and D2 – 30 February) were considered for the study. The overall calibration and validation of CERES-millet model simulation results found satisfactory and under acceptable range (mean error < ±15%). The PRECIS model output showed the mean rainfall will be higher under A2 scenario (2071-2100) of 93, 74, 69 and 54 % of mean base line (1961-90) rainfall 389, 836,660 and 627 mm of Bhuj, Junagadh, Rajkot and Bhavanagar respectively. Similarly there will be rise in maximum temperature of 4, 3.8, 3.7 and 2.6 0C during projected period as compared to base line period of Bhavanagar, Rajkot, Junagadh and Bhuj respectively. In case of minimum temperature there will be also rise of 4.3, 4.1, 3.7 and 3.4 0C during projected period as compared to base line period of Bhavanagar, Junagadh, Bhuj and Rajkot respectively. The average yield reduction was found in kharif pearl millet to the tune of 12, 15,10 and 15 % at Junagadh, Bhavnagr, Bhuj and Rajkot respectively. While projected yield reduction in summer season was 4, 8, 6 and 9 % for Junagadh, Bhavnagr, Bhuj and Rajkot respectively. The adaptation strategies viz. fifteen days early transplanting from normal, change in variety, better water management with additional fertilizer and early transplanting change in variety and better water management with additional fertilizer, the crop yield benefitted 4 to 9.5 %

KEYWORDS: Climate change, PRECIS, A2 Scenario, Simulation, CERES-Millet (DSSAT v4.5)

INTRODUCTION Millets is traditional ‘coarse cereals’ whose importance is more in terms of their role as a staple food consumed by the poor. In terms of food grain production millets ranked fourth in India behind rice, wheat and maize (FAO 2011).Post Green Revolution, millets have lost ground to other food crops, especially wheat and rice-the production of millets has more or less remained constant between 1966-2006 whereas that of rice and wheat has increased by 125% and 285%, respectively (MNI et al., 2009). Millet is grown in an area of 8.7 lakh ha and total production of Gujarat state is 15.09 Lakh tones. The major millet growing districts are Kheda, Mehsana, Patan, Kutch, Bhavnagar and Sabarkantha. Kheda district has maximum area (11.4%) and production (11.9%) in middle Gujarat agro-climatic region, while Gandhinagar district has lowest area (1.3%) and production (1.0%) under millet cultivation. The highest productivity of millet in Bhavnagar district (2509 kg/ha) followed by Sabarkantha(2108 kg/ha), Junagadh(1927 kg/ha) and Amreli (1841 kg/ha) district (2180 kg/ha) while lowest productivity was recorded in NSCCIWRS

Kutch districts (1056 kg/ha).(Anon, 2011). The area and total production of millet was found decreasing trend on basis of (1961-2009) data. But productivity was found increasing trend due to improvement in agricultural technology and managements like improve in variety, fertilizer application, irrigation facilities etc. Meteorological data compiled over the past century suggest the earth is warming. In keeping with this, for India as a whole mean annual temperature shows a significant warming trend of 0.51 degrees Celsius per 100 years during the period 1901-2007 (Kothawale et al., 2010).Crop production is largely affected biophysically by meteorological variables, including rising temperature, changing precipitation regimes and increased atmospheric carbon dioxide level. The biophysical effects of climate change on agricultural production will be positive in some agricultural systems and regions, whereas it is negative in others and these effects will vary through time. Various crop models are being used in optimizing natural resources to assess the impact of future potential climate on crop production (Rosenzwieg and Iglesias, 1998; Rosenzwieg and Parry, 1994). Assessment of impact of climate change on crop production using simulation approaches usually associated with two uncertainties. One deals with

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Table 1: Trend Analysis of Weather Condition over Different Locations of Saurashtra Region Parameter Tmax.(0C)

Tmin.(0C)

RF (mm)

Period/ Season Winter Summer Monsoon Post-monsoon Annual Winter Summer Monsoon Post-monsoon Annual Winter Summer Monsoon Post-monsoon Annual

Slope Bhavanagar -0.006 0.004 0.001 -0.003 -0.001 0.007 0.018 0.016 -0.012 0.007 0.075 -0.180 -0.296 2.344 0.362

Bhuj 0.031* 0.033 0.044* 0.052* 0.039 -0.032 -0.025* 0.014 -0.019 -0.016 -0.032 -0.045 -1.508 -0.216 -0.449

Junagadh -0.002 -0.012 0.022 0.001 0.021 0.009 0.007 0.021* 0.021 -0.467 0.098 -0.019 -1.414 -0.237 0.002

Rajkot -0.012 -0.001 0.015 0.012 0.005 0.082 0.045* 0.037 0.092* 0.064 -3.289* -0.158 0.548 0.443 -0.613

predicting climate change scenarios using General Circulation Model (GCM). The other relates to simulation models themselves. In this paper an attempt was made to evaluate the likely impact of climate change on perlmillet yield of Saurashtra region of Gujarat under different realistic hypothetical situations and for this, user friendly version of DSSAT 4.5, a generic simulation model was used.

days after D1) and for summer GHB-558, and MSH226 with two dates of sowing (D1–15 February and D2–30 February) have been used to calibrate and validate the CERES-millet model

MATERIALAND METHOD

The trend analysis based on historical data (1960-90) as presented in Table 1 showed non-significant result for Bhavanagar. For Bhuj maximum temperature during winter, monsoon and post monsoon season showed positive and significant trend, while minimum temperature during summer showed negative trend. Minimum temperature was found significant positive at Junagadh during monsoon season. Trend of maximum temperature during summer season and minimum temperature during summer and post monsoon season at Rajkot was found positive significant, while the rainfall trend was found negative.

Climate Change Study For climate change impact study, weather data for A2 scenario was derived from PRECIS downscaled model output prepared by IITM Pune in a grid size of 0.4 degree. Two period of 30 years each, one for base line i.e., 1961-1990 (base line period) and another for A2 projected scenario i.e., 2071-2100 (projected scenario) were considered for climate change impact study. There are gross difference between PRECIS base line daily weather data and actual weather data for the same period. Thirty year monthly average of daily weather parameters of base line data was subtracted from corresponding projected A2 scenario data and the difference obtained were used for computing weather data for projected period using actual observed data. In case of rainfall percentage difference on monthly sum of 30 years average data, between projected output and base line output were used as correction factor. Data Requirement for DSSAT4.5 (CERES-Millet) Simulation Model The DSSAT model requires daily weather data of maximum and minimum air temperature, solar radiation peed and rainfall. For calibration and validation of the model, observed weather data were obtained from Agro.met observatory, Millet Research Station Junagadh agricultural University Jamnagar, Gujarat. Top layer soil data file of similar texture were modified in Master using actual soil data of respective experimental site. The field experiment data on pearl millet for kharif cv. MH-1655 and GHB-732 with two dates of sowing (D1–Onset of monsoon and D2–15 34

RESULTS AND DISCUSSION Trend Analysis of Weather Conditions over Different Locations

The impact of climate change during the projected period (2071-2100) on pearl millet were simulated using DSSAT model for growth, development and yield and compared with base line (1960-90) simulated results. Projected Weather over base Line The projected weather (2070-2100) under A2 scenario as presented in fig.1 showed that there will be rise in the mean rainfall (2071-2100) of 93, 74, 69 and 54 % of mean base line (1961-90) rainfall 389, 836, 660 and 627 mm of Bhuj, Junagadh, Rajkot and Bhavanagar respectively. Similarly there will be rise in maximum temperature of 4, 3.8, 3.7 and 2.6 deg. C during projected period as compared to base line period of Bhavanagar, Rajkot, Junagadh and Bhuj respectively. In case of minimum temperature there will be also rise of 4.3, 4.1, 3.7 and 3.4 deg. C during projected period as compared to base line period of Bhavanagar, Junagadh, Bhuj and Rajkot respectively. There will be higher rise in Tmax and Tmin with lowest rise of rainfall at Bhavanagar. Bhuj will receive highest rainfall as compared to other stations.

S.B. Yadav

NSCCIWRS

Impact Assessment of Projected Climate Change on Pearl Millet in Gujarat

1600

40

Rainfall (mm)

Tmax

Tmin

35

1200 1000

30

800

25

600

Temp.(0C)

Rainfall

1400

20

400

Rajkot

Bhavanagar

Bhuj

Junagadh

Rajkot

Bhavanagar

Bhuj

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

Base line

Projected

15 Base line

200

Junagadh

Fig. 1: Comparison of Base Line and Projected Weather at Different Locations Table 2: Percent Reduction in Different Parameters under Projected Climate over Base Line Climate on Kharif Pearl Millet Across Various Locations Place Rajkot

Dates of Sowing Onset of monsoon

Bhavanagar

15 Days after onset of monsoon Onset of monsoon

Junagadh

15 Days after onset of monsoon Onset of monsoon

Bhuj

15 Days after onset of monsoon Onset of monsoon 15 Days after onset of monsoon

Cultivars

LAI

ANTH

MAT

MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744 MH-1617 GHB-744

-14.5 -15.0 -17.2 -18.5 -15.1 -10.6 -18.6 -12.2 -12.3 -13.5 -14.2 -16.2 -15.3 -10.4 -14.1 -13.6

-7.2 -10.7 -9.3 -5.5 -8.2 -6.2 -14.4 -10.0 -13.1 -10.2 -15.1 -12.2 -8.0 -8.1 -11.1 -12.1

-11.8 -14.1 -13.7 -16.2 -8.7 -4.6 -12.9 -9.4 -.36 -5.2 -9.1 -7.1 -6.7 -9.4 -8.8 -11.7

IMPACT OF CLIMATE CHANGE ON KHARIF PEARL MILLET Impact on Anthesis Date The results indicated that millet crop showed advancement in anthesis date at all study districts of Gujarat. Higher advancement in anthesis date was noted in MH-1617 (late sown condition) at Junagadh, while it was lowest in Rajkot district in GHB-744 under timely sown condition. On and average mean anthesis date reduction advancement (irrespective of cultivars and dates of sowing) was 12.6, 9.6, 9.8, and 8.2 % of base line at Junagadh, Bhavnagar, Bhuj, and Rajkot respectively. (Table.2) Impact on Maturity Date The simulation result showed that the crop duration of kharif pearl millet was reduced in all four districts ranged between 5 % to 16 % in different sowing dates and varieties. Less reduction in maturity days was noted at onset of monsoon sowing at all the location as NSCCIWRS

Fodder Yield -10.5 -14.5 -13.5 -11.9 -14.9 -9.1 -12.2 -14.1 -5.2 -8.6 -9.2 -14.3 -5.3 -6.2 -8.4 -9.8

Grain Yield -12.4 -13.3 -14.8 -16.2 -16.9 -12.1 -18.2 -15.0 -10.2 -11.1 -13.1 -14.1 -9.2 -7.1 -11.5 -13.2

compared to 15 days later sowing. Similarly lower reduction in maturity days was noted in cv.MH-1617 as compared to GHB-744 at all location. On and average mean maturity days reduction (irrespective of date and cultivars) was 6, 8, 9, and 14 % at Junagadh, Bhavnagar, Bhuj, and Rajkot respectively (Table 2). Impact on Grain Yield The climate change impact on projected period of kharif pearl millet yield, fodder yield and phenology at Junagadh, Bhavnagar, Bhuj, and Rajkot with % change from base line is presented in (Table 2). The model simulated results showed that (irrespective of cultivars and dates of sowing) mean grain yield will be reduced at Junagadh, Bhavnagar, Bhuj, and Rajkot are 12.1, 15.5, 10.2 and 14.1 respectively. The highest yield reduction (18.2%) was noted at Bhavnagar district in late sowing(15 days after onset of monsoon) and MH1617 cultivar, while it was lowest (7.1%) at Bhuj district under timely sowing (onset of monsoon) in GHB-744. Similar result was recorded by Ben

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National Seminar on Climate Change Impacts on Water Resources Systems

Mohamed et al, (2002) the reported that millet yields are predicted to decline by 11% in the two western regions (Dosso and Maradi) of Africa in 2025. Impact on Fodder Yield The simulation analysis indicated that kharif millet is likely to lose the fodder yields ranged between 5.3 to 14.9 % at different districts in study area. The highest reduction was noticed at Bhavnagar district and lowest was in Bhuj district. On an average mean fodder yield reduction (irrespective of cultivars and dates of sowing) was 9.3, 12.5, 7.4, and 12.6 % under A2 scenario as compare to baseline biomass yield. At Junagadh, Bhavnagar, Bhuj, and Rajkot respectively (Table 2).

IMPACT OF CLIMATE CHANGE ON SUMMER PEARL MILLET Impact on Anthesis Date The results indicated that summer pearl millet crop advancement in anthesis date was seen at all study districts of Gujarat. Higher advancement in anthesis

date (14%) was noted in GHB-558 under late sown condition (30 Feb) at Junagadh, while it was lowest in Bhavnagar district in GHB-558 under timely sown (15 Feb) condition. On and average mean anthesis date reduction advancement (irrespective of cultivars and dates of sowing) was 7.6, 5.3, 5.8, and 10.2 % of base line at Junagadh, Bhavnagar, Bhuj, and Rajkot respectively (Table 2). Impact on Maturity Date The simulation result showed that the crop duration of kharif pearl millet was reduced in all four districts ranged between 3 % to 9 % in different sowing dates and varieties. Less reduction in maturity days was noted at timely sowing (15 Feb) at all the location as compared to 15 days later sowing. Similarly lower reduction in maturity days was noted in cv.MSH-226 as compared to GHB-558 at all location. On and average mean maturity days reduction (irrespective of date and cultivars) was 7, 5, 6, and 10 % at Junagadh, Bhavnagar, Bhuj, and Rajkot respectively. (Table. 3)

Table 3: Percent Reduction in Different Parameters under projected Climate over Base Line Climate on Summer Pearl Millet Across Various Locations Place

Dates of Sowing

Cultivars

LAI

ANTH

MAT

Fodder Yield

Grain Yield

Feb-15th

MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558 MSH-226 GHB-558

-13.5 -11.8 -17.2 -14.7 -8.1 -3.5 -11.6 -5.1 -12.0 -15.2 -16.1 -9.2 -11.3 -6.4 -10.1 -9.6

-7.2 -8.2 -5.5 -3.2 -9.8 -4.4 -7.3 -8.8 -8.1 -5.2 -10.1 -7.2 -4.0 -4.1 -7.1 -8.1

-7.2 -8.2 -5.5 -3.2 -5.3 -6.5 -7.5 -8.1 -5.3 -5.2 -9.1 -7.1 -2.7 -3.5 -4.8 -4.2

-6.5 -7.5 -8.4 -9.6 -7.9 -2.1 -5.2 -7.1 -3.2 -6.6 -7.3 -12.3 -10.0 -9.01 -12.0 -11.1

-7.2 -8.2 -5.5 -3.2 -5.3 -6.5 -7.5 -8.1 -5.3 -5.2 -9.1 -7.1 -2.7 -3.5 -4.8 -4.2

Rajkot

Feb-30th Feb-15th

Bhavanagar

Feb-30th Feb-15th

Junagadh

Feb-30th Feb-15th

Bhuj

Feb-30th

Table 4: Different Adaptation Strategies for Kharif Pearl Millet Various Locations of Gujarat Districts

Junagadh Bhavnagar Bhuj Rajkot

36

% Yield Change in Projected Period (20712100) Under A2 Scenario from Baseline Yield (1960-90) 12.1 15.6 10.3 12.9

% Yield Gain % Yield Better Water by Adaptation Gain by Management 15 Days Early Change with Trans Planting in Additional from Normal Variety Fertilizer Transplanting Date (15 July)

6.3 7.3 4.0 5.4

3.2 4.2 2.9 4.6

Early Trans Planting Change in Variety Better Water Management with Additional Fertilizer

4.2 5.2 4.8 3.6

9.5 8.3 9.3 8.9

S.B. Yadav

Net Vulnerability under A2 2071-2100 (Yield Reduction Even after Adaptation) Even Even Even after Even after after after Change in Early Trans Change change Better Water Planting in In Management Change in Planting Change with Variety Date in Additional Better Water (A2 Variety Fertilizer Management 2071with 2100) Additional Fertilizer 5.8 8.9 7.9 2.6 8.3 11.4 10.4 7.3 6.3 7.4 5.5 1.0 7.5 8.3 9.3 4.0

NSCCIWRS

Impact Assessment of Projected Climate Change on Pearl Millet in Gujarat

Impact on Grain Yield The climate change impact on projected period of summer pearl millet yield, fodder yield and phenology at Junagadh, Bhavnagar, Bhuj, and Rajkot with % change from base line is presented in (Table 2). The model simulated results showed that (irrespective of cultivars and dates of sowing) mean grain yield will be reduced at Junagadh, Bhavnagar, Bhuj, and Rajkot are 4.7, 8.5, 6.2 and 9.9 respectively. The highest yield reduction (11.5%) was noted at Rajkot district in late sowing (30 February) and MSH-226 cultivar, while it was lowest (3.1%) at Bhuj district under timely sowing (15 February) in GHB-744. Result supported to findings of Van Duivenbooden et al, (2002). Impact on Fodder Yield The simulation analysis indicated that kharif millet is likely to lose the fodder yields ranged between 3.2 to 12.9 % at different districts in study area. The highest reduction was noticed at Bhuj district and lowest was in Junagadh district. On an average mean fodder yield reduction (irrespective of cultivars and dates of sowing) was 7.3, 5.5, 10.5, and 8.0 % under A2 scenario as compare to baseline fodder yield at Junagadh, Bhavnagar, Bhuj, and Rajkot respectively (Table 2).

STRATEGIES FOR ADAPTING KHARIF PEARL MILLET TO CLIMATE CHANGE

Better Water Management with Additional Fertilizer Nitrogen application and irrigation should be provided to suit the changed phenology of the crop in a changed environment. In this situation, the adaptation gains are projected to be up to 5.2 % under A2 scenario in projected period (2071-2100). The highest yield gain (5.2%) by this adaptation in Bhavnagar district, while it was lowest (3.6 %) in Rajkot district. It might be due to Bhavnagar district has lowest irrigated area and Rajkot has highest irrigated area. It may be noted that the application of additional nitrogen is particularly in the context of farmers who are applying less than the recommended dose of fertilizer. Early Transplanting, Change in Variety, Better Water Management with Additional Fertilizer This is the combined adaptation strategies. The model simulated output showed that if this combine strategies benefited kharif pearl millet yield up to 9.5 %. The yield benefited by this adaptation of Junagadh, Bhavnagar, Bhuj and Rajko was 9.5, 8.3, 9.3, and 8.9 respectively. Vulnerability Analysis

The district-wise impacts, adaptation and net vulnerability was worked out and presented in Table 4. For reducing of climate change impact the adaptation strategies viz. fifteen days early transplanting from normal, change in variety, better water management with additional fertilizer and early transplanting change in variety and better water management with additional fertilizer were attempted for study. Early Transplanting from Normal The shifting of sowing windows fifteen days early from the normal transplanting (15 July) adaptation benefits may be from 4.0 to 7.3% in different districts of study area highest(8.8%) benighted district was Bhavnagar, while lowest was ( 4.0) in Bhuj district. The yield gain by adaptation of Junagadh, Bhavnagar, Bhuj and Rajkot was 6.3, 7.3, 4.0, and 5.4% respectively. It might be due to maximum effective rainfall met to crop growth and grain filling period of millet. Change in Variety The analysis projected that change in variety alone can make farmers better equipped to face the climate change impacts on pearl millet crop. Yield gain by change in variety (GHB-719) in place of traditional variety in all districts of study areas. The yield gain by NSCCIWRS

this adaptation strategy at Junagadh, Bhavnagar, Bhuj, and Rajkot was 3.2, 4.2, 2.9 and 4.6 % respectively.

The vulnerability showed that Junagadh, Bhavnagar, Bhuj, and Rajkot, the kharif pearl millet crop is projected to be vulnerable to climate change with a net vulnerability of up to -7.3 %. The highest (-7.3%) vulnerable district for millet yield from current yield to climate change was Bhavnagar while lowest (-1.0 %) Vulnerability was found at Bhuj district even after following all adaptation strategies like early transplanting, Change in variety and better water management with additional fertilizer. (Table.4)

CONCLUSION The average yield reduction was found in kharif pearl millet 12, 15,10 and 15 % in Junagadh, Bhavnagr, Bhuj and Rajkot respectively. While projected yield reduction in summer season was 4, 8, 6 and 9 % for Junagadh, Bhavnagr, Bhuj and Rajkot respectively. The adaptation strategies viz. fifteen days early transplanting from normal, change in variety, better water management with additional fertilizer and early transplanting Change in variety better water management with additional fertilizer, the crop benefitted 4 to 9.5 %. The kharif pearl millet is more vulnerable as compare to summer peal millet it might be due to rainfall variability in kharif season.

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REFERENCES Anonymous (2011). District wise area, production and yield of important cereals crops, in Gujarat state, Krishi Bhawan, Sector 10-A, Gandhinagar. Data book. pp. 230–245. Ben Mohamed, A., Van Duivenbooden, N. and Abdoussallam, S. (2002). Impact of Climate Change on Agricultural Production in the Sahel-Part 1: Methodological Approach and Case Study for Groundnut and Cowpea in Niger. Climatic Change 54(3): 327-348. Government of India (GoI). (2010). “Climate change and India: a 4x4 assessment—A sectoral and regional analysis for 2030s.” Indian Network for Climate Change Assessment (INCCA), Ministry of Environment and Forests. November.

Rosenzweig, C. and Iglesias, A. (1998). The use of crop models for international climate change impact assessment. In Understanding Options for Agricultural Production, Rosenzweig, G. Y., C., Strzepek, K. D.,Major, A. Iglesias., Yates, D., Holt, A. and Hillel, D.2005. Water availability for agriculture under climate change: Five International studies. Global Environmental Change. Rosenzweig, C., and Parry, M. L. (1994). Potential impact of climate change on world food supply. Nature,367:133-138. Van Duivenbooden, N., Abdoussallam, S. and Ben Mohamed, A. (2002). Impact of Climate Change on Agricultural Production in the Sahel-Part 2: Methodological Approach and Case Study for Millet in Niger. Climatic Change 54(3): 349-368.

MNI (Millet Network of India), Deccan Development Society and FIAN (Food First Information and Action Network), India. (2009). Millets: future of food and farming.

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NSCCIWRS

Drought

NSCCIWRS

Dilip Shete

203

Climate Change Impacts on Drought in North Gujarat Agroclimatic Zone: Special Reference to Ahmedabad District Dilip Shete Civil Engineering Department, Parul Institute of Engineering and Technology [email protected]

Neha Patel Water Resource Engineering and Management Institute, Samiala [email protected] ABSTRACT Drought indicates water scarcity resulted due to insufficient precipitation, high evapotranspiration, and over–exploitation of water resources or combination of these parameters. Drought indices are important elements of drought monitoring and assessment since they simplify complex interrelationships between many climate and climate related parameters. There are various methods and indices for drought analysis which identify and classify drought based on the parameters used to determine it. The paper discusses a new method to determine the modified standardized precipitation index.

KEYWORDS: Draught, Agroclimatic Zone

Climate

Change,

INTRODUCTION Drought is a normal feature of any climate. It is a temporary, recurring natural disaster, which originates from the lack of precipitation and brings significant economic losses. Drought has many facets in any single region and it always starts with lack of precipitation, but may (or may not depending on how long and severe it is) affect soil moisture, streams, ground water, ecosystems and human beings. This leads to the identification of different types of drought (meteorological, agricultural, hydrological and socioeconomic) which reflect the perspectives of different sectors on water shortages. The definition of drought is always vague, it does not quantify the answers to the questions: When, How long or How severe a drought is. Such definitions are conceptual ones. Other operational ones identify the beginning, end, spatial extent and severity of a drought. Operational definitions are formulated in terms of Drought Indices. (Smakhtin and Hughes, 2004). Mavromatis (2010) presented a methodology for making use of drought indices in climate change impact assessment studies. Karamouz et al. (2009) developed a hybrid index for drought prediction. Moreira et al. (2008) predicted SPI-based drought category using loglinear models. Paulo and Pareira (2007) predicted SPI drought class transitions using Markov chains. Bhuiyan et al. (2006) monitored drought dynamics in the Aravalli region using different indices based on NSCCIWRS

ground and remote sensing data. Moreira et al. (2006) analysed SPI drought class transitions using loglinear models. Tsakiris and Vangelis (2005) established drought index incorporating evapotranspiration. Nain et al. (2005) posed a serious question: Are meteorological parameters based drought indices enough for agricultural drought monitoring?. The effect of the length of record on the standardized precipitation index calculation was analysed by Hong et al. (2005). Bhuiyan (2004) used various drought indices for monitoring drought condition In Aravalli terrain of India. Chaudhary and Dhadwal (2004) assessed the impact of drought on the production of major Kharif and rabi crops using standardized precipitation index. An evaluation of agricultural drought indices for the Canadian prairies was carried out by Quiring and Papakryiakou (2003). The effect of the length of record on the standardized precipitation index calculation was studied by Hote et al. (2002). Droughts are an inevitable consequence of meteorological variability, and the design of water resource infrastructure and management strategies to mitigate their effects, requires assessment of the risk. Crucial characteristics of droughts are related to their peak intensities, durations, and severities. According to Wong et al. (2010) these variables were typically correlated and copulas provided a versatile means to model their dependence structure. Mavromatis (2010) used drought indices in climate change impact assessment studies in Greece. Bacanly et al. (2008) analysed drought in Aegean region. Mishra et al. (2007) forecasted drought using a hybrid stochastic and neural

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National Seminar on Climate Change Impacts on Water Resources Systems

network model Rainfall analysis for drought estimation of Udaipur region was carried out by Bhakar et al. (2006). Drought risk assessment of Gujarat using remote sensing and GIS was presented by Chopra (2006). Agricultural drought in Iiuni, eastern Kenya was analysed using application of a Markov model by Biamah et al. (2004). Gore and Sinha Ray (2002) explored droughts and aridity over districts of Gujarat. Lloyd-Hughes and Saunders (2002) provided a drought climatology for Europe. Pandey and Ramasastry (2002) studied incidence of droughts in different climatic regions. Chung and Salas (2000) studied the drought occurrence probabilities and risks of dependent hydrologic processes. Saleh and Bhuian (1996) analysed agricultural drought in a rainfed lowland rice system in north-west Bangladesh. Chen et al. (2009) investigated historical trends of meteorological drought in Taiwan by means of long term precipitation records. Meteorological droughts of different levels of severity were represented by the standardized precipitation index (SPI) at a three–monthly time scale. Additionally, change–point detection was used to identify meteorological drought trends in the SPI series. Singh et al. (2008) presented rainfall changes and drought assessment over arid western Rajasthan. Patel and Shete (2007) rigorously analysed rainfall for drought estimation of Sabarkantha district of north Gujarat region. Vicente-Serrano et al. (2004) gave drought patterns in the mediterranean area. Sirdas and Sen (2003) made Spatio-temporal drought analysis in the Trakya region, Turkey. Ponce et al. (2000) envisaged characterization of drought across climatic spectrum. Sivasami (2000) gave 122 years rainfall and drought pattern. Demuth and Heinrich (1997) analysed temporal and spatial behaviour of drought in south Germany. Historical analysis of drought in the United States was presented by Edwards et al. (1997). Kaledhonkar et al. (1995) provided probabilities of historic droughts for south western Orissa. Mckee et al. (1995) monitored drought with multiple time scales. The relationship of drought frequency and duration to time scales was studied by Mckee et al. (1993). The objective of the present study is to understand the drought pattern using improved technique of determining standardized precipitation index in order to predict the magnitude of droughts.

There are 48 talukas under the study area. It covers 12 % (196.12 lac ha) geographical and 21 % (94.99 lac ha) cultivated area of the State. Type of soil varies from sandy loam to sandy soils. Major crops grown are Great millet, Oil seeds, Spices and condiments, Tobacco, Vegetables and Wheat.

Fig. 1: State of Gujarat, India

Ahmedabad District Ahmedabad is located at 23.00º N and 72.58º E. Ahmedabad is the largest city in Gujarat. It is the seventh largest city and seventh largest metropolitan area of India. The city spans an area of 205 km2 Ahmedabad experiences extreme climate.

Fig. 2: North Gujarat Agroclimatic Zone

There is great difference between the temperatures of days and nights. Summers start from the month of March and end by June. Winters are cool and dry and period includes the month of November to February. Monsoons are from July to September. The annual rainfall varies between 85 mm to 1, 549 mm for a period from 1961 to 2008.

STUDY AREA Gujarat is divided into eight agroclimatic zones (Fig. 1). North Gujarat agroclimatic zone is selected amongst all the eight agroclimatic zones for the present study (Fig. 2). North Gujarat agroclimatic zone is partly or fully spread over seven districts namely Ahmedabad, Banaskantha, Gandhinagar, Kheda, Mehsana, Patan and Sabarkantha.

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DATA COLLECTION Climate Data Climate data comprises of major five variables namely, minimum & maximum temperatures, relative humidity, bright sunshine hours and wind speed. These five basic climate dataset are the minimum requirement for

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Climate Change Impacts on Drought in North Gujarat Agroclimatic Zone: Special Reference to Ahmedabad District

planning of any water resources or agricultural planning. The above mentioned climate parameters are available from the meteorological stations established in and around the area. Meteorological Data The weekly climate data i.e. minimum and maximum temperatures, average relative humidity, wind speed, sunshine hours and rainfall are collected from IMD, Pune. Details of climate stations are presented in Table 1. Other relavent data were collected from Sardar Krushinagar Agriculture University, Dantiwada, Banaskantha. In order to improve the analysis involving only rainfall data, raingauge networks wherein only daily rainfall data are measured, are considered. These daily rainfall dataset are obtained from State Water Data Centre (SWDC), Gandhinagar.

not sensitive enough for being used in monitoring of drought. While the SPI is using precipitation as the only determinant describing the water deficit and effective for monitoring drought. The SPI is a relatively new drought index based only on precipitation. It is an index based on the probability of precipitation for any time scale. The SPI was formulated by Mckee et al. in 1993. The purpose of SPI is to assign a single numeric value to the precipitation that can be compared across regions with markedly different climates. Many researchers have studied the SPI considering it to be fitted to normal distribution by converting it to standard normal variate. For the present study the best fitted distribution are identified and the data are then converted to the standard normal variable. Fig. 4 shows the diagrammatic representation of the proposed methodology.

The rainfall data for the entire north Gujarat region comprising of 7 districts are collected. There are 167 raingauge stations established in these 7 districts. The maximum and minimum annual rainfall during the study period (1961 to 2008) for the above raingauge stations are determined but presented only for Ahmedabad district in Fig. 3.

Fig. 4: Diagrammatic Representation of Equiprobability Transformation from a Fitted Distribution to the Standard Normal Distribution for Determining SPI

The monthly time scale totals are used. Instead of 3 month time scale a 4 month time scale is used as the south west monsoon in India is for 4 months from June to September. The monthly data are then converted to 4, 12 and 24 months totals for determining SPI4, SPI12 and SPI24 respectively. Fig. 3: Maximum and Minimum Annual Rainfall in North Gujarat Agroclimatic Zone

METHODOLOGY There are various methods and indices for drought analysis which identify and classify drought based on the parameters used to determine it. Along the various indices proposed for characterization of meteorological drought two are widely accepted and used according to Tsakiris and Vangelis (2005) namely the Palmer’s Drought Severity Index (PDSI) and the standardized precipitation index (SPI). The PDSI uses precipitation evaporation and soil moisture conditions as key determinants. PDSI is useful for drought assessment but NSCCIWRS

Thus for the present study SPI4 is considered for short term or seasonal variation of drought for 4 months period as it reflects short–term and medium–term moisture conditions. It is important to compare the 4– month SPI with longer time scales. A relatively normal 4–month period could occur in the middle of a long– term drought that would only be visible at longer time scales. Looking at longer time scales would prevent a misinterpretation that any "drought" might be over. For average and long term drought index duration of 12 months and 24 months are studied respectively. A 12– month SPI is a comparison of the rainfall for 12 consecutive months with the same 12 consecutive months during all the previous years of available data.

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Table 1: Details of Climate Stations Available for the Study Sr. No. 1 2 3 4 5 6 7 8 9 25 26 27 28 . 39 40 . 51 52 . 54 55 . 73

Name of station

Taluka

Aslali Bareja Barejadi Chandola Dehgam Nal Lake Sanand Wasai Ambaji

Dascroi Dascroi Dascroi Dascroi Dehgam Sanand Sanand Dascroi Danta

Wadgam Mansa Raipur weir Balasinor . Vaghroli Tank Ambaliyasan . Visanagar Patan . Wagdod Badoli

Wadgam Mansa Dehgam Balasinor . Thasara Mehsana . Visnagar Patan . Patan Idar

. Virpur

District

Latitude N 22°55'00" 22°50'50" 22°53'40" 22°59'20" 23°10'00" 22°49'10" 22°59'20" 22°51'30" 24°20'10" . 24°04'21" 23°25'30" 23°06'30" 22°57'25" . 22°53'00" 23°27'30" . 23°42'00" 23°51'21" . 23°59'16" 23°49'30" . 23°47'00"

Ahmedabad

Banskatha Banaskantha Gandhinagar Kheda Kheda Mehsana Mehsana Patan

Sabarkantha Sabarkantha

. Idar

Longitude E 72°35'40" 72°35'30" 72°40'30" 72°36'30" 72°49'30" 72°03'50" 72°23'00" 72°32'40" 72°51'00" . 72°29'35" 72°42'00" 72°43'45" 73°15'13" . 73°17'30" 72°27'00" . 72°33'15" 72°06'58" . 72°09'15" 73°04'20" . 72°56'30"

Data available From To 1961 2008 1971 2008 1971 2005 1971 2008 1962 2006 1970 2008 1967 2008 1971 2008 1971 2008 . . 1961 2008 1967 2008 1971 2008 1961 2008 1973 1970 . 1961 1961 . 1971 1968 . 1970

2008 2008 . 2008 2008 . 2005 2008 . 2005

Table 2: Modified SPI Classifications by Agnew (2000) for Drought SPI Less than–2.00 Less than–1.65 Less than–1.50 Less than–1.28 Less than–1.00 Less than–0.84 Less than–0.50 Less than 0.00 Less than–0.50 Less than–0.84 Less than–1.00 Less than–1.28 Less than–1.50 Less than–1.65 Less than–2.00

Probability of Occurrence 0.023 0.050 0.067 0.100 0.159 0.201 0.309 0.500 0.309 0.201 0.159 0.100 0.067 0.050 0.023

Modified Drought Classes by Agnew (2000) Extremely wet

Severely wet Severely wet Moderately wet

Mild drought

Moderately wet No drought No drought No drought Moderate drought

Moderate drought Severe drought Severe drought Extreme drought Extreme drought

The SPI at these time scales reflect long–term rainfall patterns. Because these time scales are the cumulative result of shorter periods that may be above or below normal, the longer SPIs tend toward zero unless a specific trend is taking place. SPIs of 24– month are probably tied to streamflows, reservoir levels, and even groundwater levels at the longer time scales. Evaluations for each station are being determined by the same methodology. According to Agnew (2000) as per McKee’s classification for drought all negative indices (SPI) are taken to indicate the occurrence of drought; this means for 50% of the time, drought is occurring which is not correct. The SPI drought threshold recommended by Agnew correspond to 5%, 10%, and 20% probabilities. 44

McKee et al. (1995) Drought Classes Extremely wet

Hence drought is only expected 2 in 10 years and extreme drought only 1 in 20 years. This, it is believed, is a more realistic drought frequency and it corresponds to the employment of the term abnormal occurrence, as used in other branches of environmental science. Therefore the modified SPI classification proposed by Agnew is adopted. Table 2 presents the drought intensity classification according to Agnew. SPI is the ratio of difference between precipitation and mean at a selected period to standard deviation. =

(1)

where xi is the precipitation for ith observation, X is the mean of the data set and σ is the standard deviation.

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Climate Change Impacts on Drought in North Gujarat Agroclimatic Zone: Special Reference to Ahmedabad District

A newer approach for determining the SPI is introduced. With Akaike’s Information Criteria, AIC and Bayesian Information Criteria, BIC using the maximum likelihood method the best probability distribution is fitted to the rainfall dataset of 4, 12 and 24 months time series. The probabilities are then obtained for the respective rainfall values. The SPI given by Eq. (1) is modified as =

[

]

(2)

Where, Xip = fitted probability of rainfall at ith observation; Xp = mean of probability; σp = standard deviation of probability

RESULTS AND ANALYSIS By using the monthly rainfall data of 73 raingauge stations, standardized precipitation index (SPI) values are calculated for 4, 12 and 24 months period. Based on AIC and BIC the best distribution fitted to the dataset of 4 months, 12 months and 24 months rainfall for all the raingauge stations obtained is inverse Gaussian and hence used for further analysis. The drought intensity is classified as discussed in methodology using Table 2 and the results are presented in Table 3. The number of events and percentage out of total number of years of analysis is calculated for all the three types of data viz. original one (O), gamma fitted (G) and best fitted inverse Gaussian (I). From Table 3 it is observed that the total drought events for 4 month time scale using gamma and inverse Gaussian distribution are equal for Bareja, Chandola, Dehgam, Sanand and Nal Lake raingauge stations in Ahmedabad district. For 12 month time scale (SPI12) Aslali, Barejadi, Sanand and Nal Lake have same drought events for gamma and inverse Gaussian while it is more for gamma distribution in case for Bareja, Chandola, Dehgam and Vasai. For 24 months time scale (SPI24) all the raingauges give equal events except Dehgam. The total drought events for SPI4, SPI12 and SPI24 using original dataset are less than or equal to the gamma and inverse Gaussian distributions. Though the total drought events for most of the stations are equal for gamma and inverse Gaussian distribution, the later predicts more extreme drought events. It is observed

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that for smaller time scales the gamma and inverse Gaussian give similar results. But as the time scale increases the results may vary. Thus extreme events are more categorized by the best fitted inverse Gaussian distribution. For shorter period of 4 months the switching of SPI between positive and negative values are more frequent. In longer periods it is seen that the duration of wet or dry periods are longer. Both of these situations can be interpreted in different approaches according to different water users. For example, the soil moisture is influenced much during the 4 months base period and agricultural studies must be carried with more caution. Longer periods of drought affect the ground water and river flows. In the planning of water resources these aspects should also be considered. While analyzing the different time scales of SPI values, it is observed that when the timescale increases from 4 to 24 months the drought intensity events decrease for all the stations. As observed from the results for original data, gamma fitted data and best fitted (inverse Gaussian) data, one can say that the total events observed for most of the raingauge stations is almost same. But for the extreme events the number observed in case for best fitted distribution are more compared to the original and gamma fitted data. The same is observed for all the other districts too. Thus there are differences in number observed for moderate, severe and extreme events for all the three types of data analyzed. As one already knows that any statistical data are rarely following normal distribution and the commonly used gamma distribution is not the best fitted one for the present data analyzed, thus the discussion related to the results obtained based on inverse Gaussian distribution is dealt with. The most obvious characteristics of the drought events observed in all the Figures are that drought category changes as the time scale changes. At longer time scales drought becomes less frequent and of longer duration. For all the stations it is observed that the total drought events are more than 20% for the period under study. Thus based on SPI analysis the area can be categorized as drought affected according to the IMD classification. Observing the moderate, severe and extreme drought events the most frequently affected years observing all the 73 raingauge stations is presented in Fig. 5.

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Fig. 5: Percentage of Raingauge Stations Experiencing Drought in North Gujarat Agroclimatic Zone during the Study Period (1961 to 2008) Table 3: Drought Intensity Classification Events (Percentages) for SPI4, SPI12 and SPI24 in Ahmedabad District RainGauge Station Aslali

Bareja

Barejadi

Chandola

Dehgam

Nal Lake

Sanand

Wasai

46

Type

O G I O G I O G I O G I O G I O G I O G I O G I

Moderate Drought Severe Drought Extreme Drought Total Drought SPI4 SPI12 SPI24 SPI4 SPI12 SPI24 SPI4 SPI12 SPI24 SPI4 SPI12 SPI24 No. % No. % No. % No. % No. % No. % No. % No. % No. % No. % No. % No. % 8 17 7 15 6 13 3 6 3 6 4 9 1 2 1 2 0 0 12 25 11 23 10 21 6 13 6 13 5 11 6 13 5 10 7 15 1 2 1 2 0 0 13 27 12 25 12 26 4 8 5 10 5 11 5 10 3 6 5 11 3 6 4 8 2 4 12 25 12 25 12 26 4 11 3 8 2 5 2 5 3 8 3 8 2 5 2 5 1 3 8 21 8 21 6 16 3 8 3 8 6 16 4 11 3 8 2 5 2 5 3 8 2 5 9 24 9 24 10 27 3 8 2 5 6 16 2 5 1 3 1 3 4 11 5 13 3 8 9 24 8 21 10 27 7 18 5 13 5 14 1 3 3 8 3 8 1 3 1 3 1 3 9 24 9 24 9 24 7 18 4 11 5 14 4 11 4 11 4 11 0 0 1 3 0 0 11 29 9 24 9 24 4 11 4 11 5 14 4 11 4 11 3 8 1 3 1 3 1 3 9 24 9 24 9 24 6 16 7 18 7 19 3 8 3 8 1 3 0 0 0 0 1 3 9 24 10 26 9 24 6 16 7 18 9 24 4 11 1 3 2 5 1 3 3 8 0 0 11 29 11 29 11 30 6 16 5 13 6 16 4 11 2 5 3 8 1 3 3 8 1 3 11 29 10 26 10 27 6 13 5 11 5 11 3 6 4 9 3 7 0 0 0 0 2 4 9 19 9 19 10 22 2 4 6 13 5 11 8 17 6 13 4 9 0 0 0 0 2 4 10 21 12 26 11 24 2 4 5 11 5 11 7 15 4 9 2 4 1 2 2 4 4 9 10 21 11 23 11 24 6 15 4 10 7 18 3 8 4 10 0 0 0 0 0 0 1 3 9 23 8 21 8 21 3 8 0 0 5 13 4 10 6 15 2 5 2 5 2 5 1 3 9 23 8 21 8 21 3 8 0 0 4 11 2 5 4 10 3 8 4 10 4 10 1 3 9 23 8 21 8 21 2 5 4 10 4 10 5 12 3 7 1 2 2 5 2 5 2 5 9 21 9 21 7 17 1 2 1 2 4 10 5 12 5 12 2 5 3 7 3 7 3 7 9 21 9 21 9 22 1 2 1 2 4 10 3 7 3 7 2 5 5 12 5 12 3 7 9 21 9 21 9 22 4 11 2 5 5 14 1 3 3 8 1 3 2 5 1 3 1 3 7 18 6 16 7 19 5 13 6 16 6 16 3 8 1 3 3 8 2 5 3 8 1 3 10 26 10 26 10 27 4 11 5 13 5 14 2 5 0 0 2 5 3 8 4 11 2 5 9 24 9 24 9 24

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Climate Change Impacts on Drought in North Gujarat Agroclimatic Zone: Special Reference to Ahmedabad District

Fig. 6: SPI4, SPI12, SPI24 for Aslali Raingauge Station, Ahmedabad District Table 4: All India and Gujarat State Drought Years Analyzed by Gore and Ponkshe (2004) 1961-1970

1971-1980

1962, 1963, 1965, 1966, 1968

1972, 1973, 1974

1965, 1966, 1968

1972, 1974, 1979

1981-1990 Gujarat State 1982, 1985, 1986, 1987, 1990 All India 1982, 1987

From Fig. 6 one can say that the year 1987 observed extreme drought event for around 80% (59 out of 73) raingauge stations for SPI24 which is the highest. Observing the moderate, severe and extreme drought events for all the 73 raingauge stations, years 1974, 1985 to 1988 and 1999 to 2002 are the most droughts affected one. The frequency of extreme drought is higher for all over north Gujarat agroclimatic zone. One can observe that almost every year with only few exceptions the region experiences drought of different magnitudes and time scales. The study carried out by Gore and Sinha Ray (2002), using aridity index for drought classification for Gujarat state for the period of 1901 to 1999, is in accordance with the findings obtained by using the modified SPI classification. Aridity index used by the authors categorized 27 drought years as worst droughts when the area affected by it exceeded 50% of the total area. For the period from 1961 to 1999, the years 1962, 1966, 1968, 1972, 1974, 1982, 1985, 1986, 1987, 1991, 1995 and 1999 are the worst droughts mentioned. Similar results are obtained using SPI too for the present study region. The above results obtained can also be confirmed by the study carried out by Chopra (2006). The author stated that the drought of 1987 was one of the worst in the century. The monsoon rainfall was normal only in 14 out of 35 meteorological sub– division in the country (India). Also Gujarat was one such state where drought occurred with unfailing regularity. All India and Gujarat state drought years were analysed and presented in Table 4 referring the study by Gore and Ponkshe (2004). In addition, drought index results agree with the historical record for the duration of drought (12–24 month) with some exceptions. NSCCIWRS

1991-2000 1991, 1993, 1995, 1998, 1999

CONCLUSION Overall the drought study concludes that the inverse Gaussian distribution is best fitted to the rainfall data in the study area for different time scales of 4, 12 and 24 months. The total drought events are nearly same for gamma and best fitted distribution. The original data gives less drought events compared to the gamma and inverse Gaussian distributions. It can be concluded that inverse Gaussian can be used for further studies for classifying the drought intensities for the region. Further the area is drought prone as more than 20% years the drought is experienced during the study period from 1961 to 2008. The SPI values show that when time scale increases drought occurs less frequently but has longer duration. Also when the time scale is short the shift between positive and negative values are seen more frequently and when the time scale increases it is observed that the SPI values respond to the varying precipitation conditions slower. In a nutshell drought analysis carried out by the standardized precipitation index is vital for the study area experiencing frequent drought events.

REFERENCES Agnew, C.T. (2000). “Using the SPI to identify drought.” Drought Network News, 12(1), 6–12. Bacanli, U.G., Dikbas, F., and Baran, T. (2008). “Drought analysis and a sample study of Aegean region.” Proc. of Sixth Int. Conference on Ethics and Environmental Policies Ethics and Climate Change. Scenarios for Justice and Sustainability, Padova, 1–10. Bhakar, S. R., Bansal, A. K., Chhajed, N. and. Singh, R.V. (2006b). “Rainfall analysis for drought estimation of Udaipur region.” J. of Agricultural Engineering, 43(3), 40–43.

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Bhuiyan, C. (2004). “Various drought indices for monitoring drought condition In Aravalli terrain of India.” th Proc. of XX ISPRS Congress, 243-248. Bhuiyan, C., Singh, R.P., and Kogan, F.N. (2006). “Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data”, Int. J. of Applied Earth Observation and Geoinformation, 8(4), 289–302. Biamah, E.K., Sterk, G., and Sharma, T.C. (2004). “Analysis of agricultural drought in Iiuni, eastern Kenya: application of a Markov model.”, Hydrological Processes, 19(6), 1307–1322. Chaudhari, K.N., and Dadhwal, V.K. (2004). “Assessment of impact of drought–2002 on the production of major Kharif and rabi crops using standardized precipitation index.” J. of Agrometeorology, 6(Sp. Issue), 10–15. 8 Chen, S.T., Kuo, C.C., Yu, P.S. (2009). “Historical trends and variability of meteorological droughts in Taiwan.” Hydrological Sciences J., 54(3), 430–441. Chopra, P. (2006). “Drought risk assessment using remote sensing and GIS : A case study of Gujarat.” M.S. thesis, Geo–information Science and Earth Observation in Hazard and Risk Analysis, Int. Institute of Geo–information Science and Earth Observation, Netherlands. 10 Chung, C.H., and Salas, J. D. (2000). “Drought occurrence probabilities and risks of dependent hydrologic processes.” J. of Hydrologic Engineering, 5(3), 259–268. Demuth, S., and Heinrich, B. (1997). “Temporal and spatial behaviour of drought in south Germany.” Proc. of the Postojna, Slovenia, IAHS Publ. no.246, 151–157. Edwards, C., Daniel, C., McKee, T.B., Doesken, N.J., and Kleist, J. (1997). “Historical analysis of drought in the th United States.” Proc. of 7 Conference on Climate Variations, Center Long Beach. Gommes, R., and Petrassi, F. (1996). “Rainfall variability and drought in sub-saharan Africa.” FAO agrometeorology series working paper No. 9. Gore, P.G. and Ponkshe, A.S. (2004). “Drought in Gujarat districts and state as key indicators to all India drought.”, J. of Agrometeorology, 6 (1), 47-54.

Kaledhonkar, M.J., Kamra, S.K., Yadav R.K., and Tiwari, K.N. (1996). “Probabilities of historic droughts for south western Orissa in India.” J. of Indian Water Resources Society, 2(4), 19–24. Karamouz M., Rasouli K., and Nazif S. (2009). “Development of a hybrid index for drought prediction: case study.” J. of Hydrologic Engineering, 14(6), 617–627. Lloyd-Hughes, B. and Saunders, M.A. (2002). “A drought climatology for Europe.” Int. J. of Climatology, 22, 1571– 1592. Mavromatis, T. (2010). “Use of drought indices in climate change impact assessment studies: an application to Greece.” Int. J. of Climatology, 30(9), 1336–1348. McKee, T.B., Doesken, N.J., and Kleist, J. (1993). “The relationship of drought frequency and duration to time scales.” Proc. of Eighth Conference on Applied Climatology, Anaheim, California. McKee, T. B.; Doesken; N. J. and Kleist. J. (1995). “Drought monitoring with multiple time scales.” Proc. of the Ninth Conference on Applied Climatology; American Meteorological Society, Boston, 233–236. Mishra, A.K., Desai, V.R., and Singh, V.P. (2007). “Drought forecasting using a hybrid stochastic and neural network model.” J. of Hydrologic Engineering, 12(6), 626–638. Modarres, R. (2007). “Streamflow drought time series forecasting.” J. of Stochastic Environmental Research and Risk Assessment (SERRA), 21(3), 223–233. Moreira, E.E., Coelho, C.A, Paulo, A.A., Pereira, L.S., and Mexia, J.T. (2008). “SPI-based drought category prediction using loglinear models.” J. of Hydrology, 354, 116–130. Moreira E.E., Paulo A.A., Pereira L.S., and Mexia J.T. (2006). “Analysis of SPI drought class transitions using loglinear models.” J. of Hydrology, 331(1–2), 349–359. Nain, A.S., Kersebaum, K.C., Mirschel, W. (2005). “Are meteorological parameters based drought indices enough for agricultural drought monitoring: a comparative study of drought monitoring with SPI and crop simulation model.” st Proc. of ICID 21 European Regional Conference, Frankfurt (Oder) and Slubice-Germany and Poland, 1–9.

Gore, P.G., and Sinha Ray, K.C. (2002). “Droughts and aridity over districts of Gujarat.” J. of Agrometeorology 4 (1), 75–85.

Pandey, R.P., and Ramasastri, K.S. (2002). “Incidence of droughts in different climatic regions.” Hydrological Sciences J., 47(S), S31–S40.

Hong, W., Hayes, M.J., Wilhite, D.A., and Svoboda, M.D. (2005). “The effect of the length of record on the standardized precipitation index calculation.” Int. J. of climatology, 25(4), 505–520.

Parthasarathy, B., Sontakke, N.A., Monot, A.A., and Kothawale, D.R. (1987). “Droughts/floods in the summer monsoon season over different meteorological subdivisions of India for the period 1871–1984.” Int. J. of Climatology, 7(1), 57–70.

Hote, Y.L., Mahe, G., Some, B., and Triboulet, J.P. (2002). “Analysis of a Sahelian annual rainfall index from 1896 to 2000; the drought continues.” Hydrological Sciences J., 47(4), 563–572. Hutchinson, P. (1985). “Rainfall analysis of the Sahelian drought in the Gambia.” Int. J. of Climatology, 5(6), 665–672.

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Patel, N.R. and Shete, D.T. (2007a). “Rainfall analysis for drought estimation of Sabarkantha district of north Gujarat region, India.” Proc. of South Asian Conference on Water in Agriculture: Management Options for Increasing Crop Productivity Per Drop of Water, India.

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Paulo, A.A., and Pereira, L.S. (2007). “Prediction of SPI drought class transitions using Markov chains.” Water Resources Management, Springer Netherlands, 21(10), 1573– 1650. 34 Ponce, V.M., Pandey, R.P., and Ercan, S. (2000). “Characterization of drought across climatic spectrum.” J. of Hydrologic Engineering, 5(2), 222–224. Quiring, S.M., and Papakryiakou, T.N. (2003). “An evaluation of agricultural drought indices for the Canadian prairies.” Agriculture and Forest Meteorology, 118, 49–62. 36 Saleh, A.F.M. and Bhuiyan, S.I. (1996). “Analysis of agricultural drought in a rainfed lowland rice system: A case study in north-west Bangladesh.” J. of Indian Water Resources Society, 2(1), 51–56. Singh, R.S., Rao, A.S., and Mall, R.K. (2008). “Rainfall changes and drought assessment over arid western Rajasthan.” Int. Symposium on Agrometeorology and Food Security, Hyderabad, 33.

Sivasami, K.S. (2000). “Droughts and rainfall pattern, 1877-1999.” Economic and Political Weekly, 1991–1992. Smakhtin, V.U. and Hughes, D.A. ( 2004 ). Working Paper 83 Drought Series Paper 1 Review, Automated Estimation and Analyses of Drought Indices in South Asia, International Water Management Institute Tsakiris, G. and Vangelis, H. (2005). “Establishing drought index incorporating evapotranspiration.” European Water, 9(10), 3–11. Vicente-Serrano, S.M., González-Hidalgo, J.C., Luis M.D., and Raventos, J. (2004). “Drought patterns in the mediterranean area: the Valencia region (eastern Spain).”, Climate Research, 26, 5–15. Wong, G., Lambert, M.F., Leonard, M., and Metcalfe, A.V. (2010).“Drought analysis using trivariate copulas conditional on climatic states.” J. of Hydrologic Engineering, 15(2), 129–141.

Sirdas, S., and Sen, Z. (2003). “Spatio-temporal drought analysis in the Trakya region, Turkey.” Hydrological Sciences J., 48(5), 809–820.

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Characterization of Agricultural Drought in Koppal District of Northeastern Parts of Karnataka, India J.K. Neelakanth Agricultural Engineer, Water Management Research Centre, Belavatigi, UAS, Dharwad [email protected]

D. Tamilmani and I. Muthuchamy Prof., Soil & Water Conservation Engineering, TNAU, Coimbatore

P. Balakrishnan Dean, College of Agril Engineering, UAS, Raichur, Karnataka ABSTRACT Impact of climatic change on Agricultural production and productivity study was done at North-Eastern dry zone of Koppal District Karnataka India. Study area consists of four taluks namely Gangavathi, Koppal, Kustagi and Yelburga comes under Koppal district. Rainfall data of all the rain gauge stations under these 4 taluks of the study area was collected and used for the study to assess and Characterize the Agricultural drought situation and frequency of drought based on 30 years (1975-2010). The maximum agricultural drought severity were found in all the four taluks. Hence, to improve the situation by introducing the practice of growing the short duration crops and low water requirement or green manure or fodder crop etc in order to avoid keeping the land as fallow. To manage drought, Contingency crop planning schedules & dry land technology for drought area, rainwater harvesting and construction of ponds suggested.

KEYWORDS: Agricultural Change, Contingency Crop Management

Drought, Planning,

Climate Drought

INTRODUCTION Global climate change and human activities are placing increasing pressure on water resources around the World. Farming accounts for approximately 70% of water use in the world today. An increase in agricultural production will be needed to meet the growing global demand for food. This in turn will increase the demand for irrigation. Improvement in water use efficiency and crop water productivity are therefore needed to help resolve competing demands on water resources from agriculture development and water use in other sectors. The research community has been studying the role of Earth Observations (EO) in monitoring crop growth processes under the water stress conditions and the early warning and detection of drought. A number of techniques and models have been developed for monitoring agricultural drought and are being used operationally (Loukas and Vasiliades, 2004). Being the first form of water in the hydrologic cycle, rainfall is the primary source of water. However, we depend mainly on secondary sources of water such as rivers, lakes and groundwater, which constitute 0.26 per cent of total global fresh water reserves. On the global water supply scenario, runoff in rivers and storage in lakes or underground aquifers amount to about 40,000 km3 (35 per cent of total rainfall). About 12,500 km3 (30 per cent of total runoff) is reckoned to be annually accessible in the present economic context, of which 50

around 5,000 km3 of accessible runoff is already appropriated. Technically, more water can be mobilized by harnessing remote rivers, capturing floods, melting polar ice or desalinizing sea water, but at a steeply increased cost in terms of finance, energy and environment (Vasudevan and Pathak, 2000). Scenario of Drought in India In India, drought is common and the drought prone areas are mainly confined to the peninsular and western parts of the country, and there are a few pockets in the parts of the country. India experiences localized drought almost every year in some regions or other. In the past independence era major droughts that affected more than 1/3 rd of the country were reported during 1951, 1966-67, 1972, and 1979, 1987-88 and 2002-03. An estimated 260 m ha are subjected to droughts of varying intensities (Gurumel Singh, et al., 2006). By climatic crop growth indices, area subjected to drought has been classified into moderate (7.62 m ha), large (4.70 m ha), Severe (116.8 m ha) and disastrous droughts (19.7 m ha.). Agricultural drought is caused by deficiency of soil moisture during cropping periods. The problem of drought and desertification is alarming in India like any other place in the world. The drought prone areas are confined mainly to the Peninsular and western parts of the country and there are only few pockets in central, eastern, northern and southern parts. They are mostly due to the cumulative effects of precipitation, water utilization etc. the hot semi-arid zones of India follow the north and south temporal climate, with few exceptional arid zones in the central peninsular portion.

J.K. Neelakanth

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Characterization of Agricultural Drought in Koppal District of Northeastern Parts of Karnataka, India

The cold arid zone is confined to the northern hills. It is reported that about 260 million hectares (795 million hectares of geographical area) are subjected to different degrees of water stress and drought conditions. This includes about 38.7 million hectares drought conditions over the South Asia, South East Asia, India etc. over the past century. The hot arid region in India extends to about 32 million hec. Which is about 10% of the country’s geographical area and is found in the states of Rajasthan (19.52 mha), Gujarat (6.40 mha), southern parts of the Punjab, Haryana (2.88 mha) and certain areas of Maharashtra, Andhra Pradesh and Tamilnadu (3.20 mha). The revenue districts of India frequently affected by the event are shown in Table 1. The frequency of drought in India varies between two to more than five years (Appa Rao et. al., 1991) in some parts of India.

characteristic feature of this zone is the lowest rainfall (574 mm) occurring in about 30 to 35 rainy days. Both black and red soils are predominant in this zone with a higher percentage of black soils of varying depths occurring in Bijapur, Bagalkot, Koppal, Raichur, Gadag, Haveri, Dharwad and Belgaum districts. The total irrigable area in the zone is estimated to be around 5.82 lakh ha. or 11.78 per cent of the total cultivated area. The highest number of wells are also found in this zone i.e., in Bijapur district. The rainfall is bi-modal in nature with the highest peak occurring in September followed by another peak in July. Hence, it is predominantly a rabi crop growing tract and variety of crops are grown in this zone. (Anonymous,2008)

Scenario of Drought in Karnataka State

Chaudhari and Dadhwal (2004) carried out a study to quantify the impact of drought on production of four major kharif crops namely groundnut, bajra, cotton and soybean and a rabi crop i.e. wheat using the Standardized Precipitation Index (SPI) which captures cumulative rainfall deviation at various time scales. SPI values were computed for 36 hydrological subdivisions at monthly (SPI-1), bimonthly (SPI-2) and tri monthly (SPI-3) scales using monthly data for the period of 1971-2002. Wu, et al., (2005) illustrated the consistency of dry/ wet event categories between Standardised Precipitation Index (SPI) values derived from different precipitation record lengths. The effect was also illustrated by comparing SPI values derived from different lengths of record of some severe drought and flood years. The gamma distribution is a frequency distribution of climatological precipitation time series and was used as basis for SPI calculation. The results showed that SPI values computed for different lengths of records are highly correlated and consistent when gamma distributions of precipitation over the different time periods are similar. Murthy, C.S et al., (2008) studied the crop condition and assessment of agricultural drought at Bagalkot Taluk, Karnataka, India, where the kharif season crops are more vulnerable to agricultural droughts due to uncertainty in monsoon rains, crops of rabi season grown under

Karnataka, which is one of the Southern states of India may be called the land of two monsoons, since both south west and north east monsoon brings major part of rainfall to the state. It extends over an area of 1.92 M ha. ha) with varied topography. Drought in the state presents a serious problem in arid regions, which receive less than 750 mm of annual rainfall, are drought prone and constitute more than two thirds of the geographical area of the state. The occurrence and spatial distribution of rainfall is highly variable and not dependable. More than 2/3 parts of the State receives less than 750 mm of rainfall. North Eastern Dry zone of Karnataka state has 1.56 lakh ha under irrigation and this accounts to 11.9 per cent of the cultivated area and 80 per cent of the irrigation is through canals. Predominantly this zone although said to be a kharif farming area, but due to heavy rains occurring in September and October, the zone is almost classified into a rabi farming area. Northern Dry Zone is the largest agricultural zone of the state and covers 35 taluks in Bijapur, Bagalkot, Bellary, Raichur, Koppal, Belgaum, Gadag, Dharwad and Davanagere district. The total geographical area of the zone is 47.84 lakh ha out which 36.63 lakh ha area is under cultivation. This zone is also characterized by high percentage of irrigation through different sources. The most important

Climate Change Impact Assessment

Table 1: Decadal Mean Areas (ha.) under Different Land uses in Gangavathi Taluk LAND USE

I II

III IV V

Geographical Area Forest (percent of Total Geo. Area) Lands under Non Agricultural use and Barren and Uncultivable Lands (percent of Total Geo. Area) Cultivable site, permanent pastures and trees and groves ( percent of Total Geo. Area) Fallow land ( percent of Total Geo. Area) Net area sown ( percent of Total Geo. Area)

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1970-71 to 1979- 1980-81 to 1990- 1990-91 to 2000- 2000-01to 201080 91 01 11 132131 ha. 5625 14464 14482 14482 4.3 10.9 11 11 21074 12331 12331 12331 15.9 10151 7.7 15262 11.6 80092 60.6

J.K. Neelakanth

9.3 7704 5.8 15764 11.9 81834 61.9

9.3 7497 5.7 14360 10.9 83461 63.2

9.3 7753 5.86 25445 19.3 72120 54.6 51

National Seminar on Climate Change Impacts on Water Resources Systems

residual soil moisture and rain fed minor irrigation tanks are equally vulnerable to drought hazards. To assesses the extent of crop area affected by agricultural drought during rabi 2005-06, using advanced Wide Field Sensor (AWiFS) images of Indian Remote sensing Satellite, Resourcesat-1, Normalized Difference Vegetation Index (NDVI) images which represent density, health and vigor of crops were generated satellite images and analyzed in association with cropping pattern, crop calendar, rain fall pattern and soil depth. The area affected by agricultural drought delineated in each Taluks. The study indicated the feasibility for detailed assessment of agricultural drought during rabi season on real time basis using the indigenously available AWiFS images.

MATERIAL AND METHODS The study area of Koppal District, Karnataka, India (15O 09’ and 160 34’ N-Latitude : 750 46’ and 770 35’ ELongitude ) was conducted. North-Eastern dry zone of Karnataka is a part of North Karnataka (Fig.1) consists of four taluks namely Gangavathi, Koppal, Kustagi and Yelburga comes under Koppal district. Rainfall data of all the rain gauge stations under these 4 taluks of the study area was collected and used for the study to assess and Characterize the Agricultural drought situation and frequency of drought based on 30 years 1975-2010 data. To manage drought, Contingency crop planning schedules & dry land technology for drought, animal husbandry, horticulture and agro forestry have been analyzed and presented.

Geology and Physiographic The study zone consists of both red (loam to sandy loam) and black soils (clay loam to clay). The acidic rocks viz., granites, gneisses, sand stone and quartzite are the parent material for red soils where as basic rocks viz., basalt, diorite, Meta basalts and chlorite schist for black soils. The physiographic of red soils exists over hills and ridges on rolling and undulating lands of the plateau. The black soils occur on gently sloping lands, plains, plateau, and summits and in valleys of the terrain. Rainfall Analysis and Draught Characterization Koppal district with four taluks comes under NorthEastern dry zone of Karnataka. The average annual rainfall of Koppal, Kustagi, Yelburga and Gangavati taluks are 600mm, 571mm, 582mm and 523mm respectively. The rainfall in the SW monsoon constitutes about 80 percent of the annual rainfall. August and September is the rainiest month. The Koppal district gets some rain during the later part of summer. The variation in the rainfall from year to year and impact of agricultural Productivity analyzed and presented in Fig 2 and 3. Net Area Sown in Koppal District It is seen from Tables 1 to 4 that net sown area expressed as percentage of total geographical area has decreased as given below during the study period. Gangavathi-60.6 percent to 54.6 percent; Kushtagi taluk: 77.9 per cent to 66.8 per cent ; Yelburga taluk: 82.3 per cent to 73.4 per cent.

Table 2: Decadal Mean Areas (ha.) under Different Land uses in Koppal Taluk

I II

III

IV V

Land Use Geographical Area Forest (percent of Total Geo. Area) Lands under Non Agricultural use and Barren and Uncultivable lands (percent of Total Geo. Area) Cultivable site, permanent pastures and trees and groves (percent of Total Geo. Area) Fallow land (percent of Total Geo. Area) Net area sown (percent of Total Geo. Area)

1970-71 to 1979-80 136765 ha. 10779 7.9 27190

1980-81 to 1990-91

1990-91 to 2000-01

2000-01to 2010-11

10779 7.9 24522

10779 7.9 14561

10779 7.9 27191

19.9 2649

17.9 3459

10.6 7634

19.9 2126

1.9 12075 8.8 82683 60.5

2.5 9179 6.7 87297 63.8

5.6 22643 16.6 82611 60.4

1.6 8970 6.5 87689 64.12

Table 3: Decadal Mean Areas (ha.) under Different Land uses in Kushtagi Taluk Land Use

I II

52

Geographical Area Forest (percent of Total Geo. Area) Lands under Non Agricultural use and Barren and Uncultivable lands (percent of Total Geo. Area)

1970-71 to 1979-80 135779 ha. 4110 3 10852 8

J.K. Neelakanth

1980-81 to 1989-90

1990-91 to 2008-09

4110 3 9987 7.4

4110 3 9987 7.4

2000-01 to 2008-09

4110 3 9987 7.4 Table 3 (Contd.)…

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Characterization of Agricultural Drought in Koppal District of Northeastern Parts of Karnataka, India …Table 3 (Contd.) III Cultivable site, permanent pastures and trees and groves (percent of Total Geo. Area) IV Fallow land (percent of Total Geo. Area) V Net area sown (percent of Total Geo. Area)

5463 4 10854 8 105745 77.9

4912 3.6 13276 9.8 103490 76.2

4709 3.5 25406 18.7 90356 66.5

4709 3.5 26294 19.4 90679 66.8

Table 4: Decadal Mean Areas (ha.) under Different Land uses in Yelaburga Taluk Land Use Geographical Area ,ha Forest (percent of Total Geo. Area) II Lands under Non Agricultural use and Barren and Uncultivable lands {percent of Total Geo. Area) III Cultivable site, permanent pastures and trees and groves (percent of Total Geo. Area) IV Fallow land (percent of Total Geo. Area) V Net area sown (percent of Total Geo. Area) I

1970-71 to 1979-80 147830 151 0.1 4443

1980-81 to 1989-90

1990-91 to 2008-09

2000-01 to 2008-09

80 0.1 5988

80 0.1 5988

80 0.1 5988

3 2720 1.8 20108 13.6 121638 82.3

4.1 2864 1.9 18725 12.7 120174 81.3

4.1 2865 1.9 32741 22.1 106156 71.8

4.1 2865 1.9 30419 20.6 108478 73.4

Fig. 1: Map showing Study Aear of Koppal District

Thus decrease in case of Gangavathi , Kushtagi and Yelburga taluks was noticed. In case Koppal taluk only net sown area has increased from 60.5 percent to 64.12 percent of its geographical area. Overall decreased trend was observed and this was due to the Impact of Climate change In order to study the year to year variations of net sown area in greater detail its time series graph for Koppal district as a whole has been prepared for the period 1981-82 to 2010-11 and is presented in Fig. 2. Main features are net sown area which is 419000 ha during 1981-82 has become 358000 ha during the recent year of 2008-09 indicating an overall decrease during the study period. Time series graph and five year moving average curve depicted in Fig. 4.16 indicates

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that net sown area has been steadily decreasing up to 1991-92, increasing from 1991-92 to 1998-99 and decreasing again thereafter. To study these trends quantitatively, linear trend equations (decreased) have been fitted for these periods (1981-82 to 2010-11) as indicated below. y=-3021.t +42497R²=0.407 (R=0.637) Where t is serial No of years with 1981-82=1, and Y is Net sown area in ha during year. Considering the whole study period, net sown area in Koppal district has been decreasing at the rate of 3021ha per year. This has occurred mainly due to increase of fallow lands and is not quite desirable for the increased agricultural production of the district.

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0.6 0.5 Area sown, M ha

1000.0 900.0 800.0 700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0

y = -3021x + 42497 R² = 0.407

0.4 0.3 0.2 0.1

1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

0

Rainfall, mm

National Seminar on Climate Change Impacts on Water Resources Systems

Year Rainfall, mm

Area sown, M ha

5 year moving average of area sown

Linear trend of area sown

0.08 Area sown, M ha

1000 900 800 700 600 500 400 300 200 100 0

y = 2280.x + 2073. R² = 0.93

0.07 0.06 0.05 0.04 0.03 0.02 0.01

1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

0

Rainfall, mm

Fig. 2: Variation of Net Area Sown with Rainfall in Koppal District

Year Rainfall, mm

Area sown, M ha

5 years moving avearge of area sown

Linear trend of area sown

Fig. 3: Variation of Area Sown More than Once with Rainfall in Koppal District

Area Sown More than Once

Whole study period 1981-82 to 2010-11

Areas have sown more than once in Koppal district depicts in Fig. 3. The same has increased from 9600 ha in 1981-82 to 70900 ha during 2009-10. Five year moving average curve and trend line have also been indicated the increasing trend. However a slower increasing rate is noticed in time series graph as well as moving average curve from 2001-02 onwards. Following are the trend equations for these periods where Y is the area sown more than once in Koppal district during the year t.

y = 2280.t + 2073.R²=0.937 (R=0.96)

54

Where t is serial No. of year with 1981-82 = 1 Thus Gangavathi taluk and Koppal taluk are the main contributors for the area sown more than once in Koppal district. There has been some decrease in area sown more than once in Koppal and Kushtagi taluks. There is scope for improving further cropping intensity at Gangavathi taluk.

J.K. Neelakanth

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2010-11

2009-10

2008-09

2007-08

2006-07

2005-06

2004-05

2003-04

2002-03

2001-02

2000-01

1999-00

1998-99

1997-98

1996-97

1995-96

1994-95

1993-94

1992-93

1991-92

1990-91

1989-90

1988-89

1987-88

1986-87

1985-86

1984-85

1983-84

1982-83

150 145 140 135 130 125 120 115 110 105 100 1981-82

Cropping intensity, per cent

Characterization of Agricultural Drought in Koppal District of Northeastern Parts of Karnataka, India

Year Gangavati

Koppal

Kustagi

Yelburga

Fig. 4: Variation of Cropping Intensity in Koppal District

Cropping Intensity in Koppal District The time series graph of cropping intensities depicts in Fig. 4 for all four taluks in Koppal district. Gangavathi taluk leads in cropping intensity. Starting from 112 percent in 1981-82, cropping intensity has reached 145 percent in 2010-11. However the same is more or less stagnant during 2004- 05 to 2010-11. Koppal has reached a cropping intensity of 117 percent, where as dry taluks of Kushtagi and Yelburga have reached only 108 percent. Neelakanth, J.K (2012)

CONCLUSION The maximum agricultural drought severity were found in all the four taluks. Hence efforts should be made to improve the situation by introducing the practice of growing the short duration crops and low water requirement or green manure or fodder crop etc in order to avoid keeping the land as fallow.

REFERENCES Anonymous (2008), Water resources at a glance, Central Water Commission, Govt. of India, New Delhi. Appa Rao, G. (1991), Drought Climatology, Jal Vigyan Samiksha, Publication of High Level Technical Committee Report, National Institute of Hydrology, Roorkee. Chaudhari, K.N. and Dadhwal, V.K. (2004), Assessment of impact of drought-2002 on the production of major kharif and rabi crops using Standardized Precipitation Index (SPI). Journal of Agro meteorology, 6:10-15. Gurmil Singh, Venkataraman, C Sastry, G and Joshi, B.P. (2006), “Manual of soil and water conservation practices” Oxford and IBH Publications Co. Pvt Ltd.P-48-49. Loukas, A. and Vasiliades, L. (2004), Probabilistic analysis of drought Spatial-temporal characteristics in Thessaly region, Greece. Natural Hazards and Earth Sciences, 4:719-731.

Drought Preventive Measures by Contingent Cropping Plan Select efficient crops and cropping systems matching the length of growing season. Some of the promising crops for rainfed uplands are maize, cowpea, arhar, blackgram, rice bean, ragi, groundnut, sesame, castor, pumpkin and sweet potato. Adopt intercropping/mixed cropping system in recurrent drought prone areas. Store rain water to use as life saving irrigation. On-farm water harvesting structures lined with 6:1 soil cement mortar of 6 cm thickness in 10% land area helps to harvest the rainwater for providing protective irrigation. Plough and sow the crops across the slope to develop a ridge and furrow type of land configuration for effective soil

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moisture conservation to overcome drought for longer period. Apply lime @ 0.15 to 0.25 LR (500 kg lime) mixed with FYM @ 5.0 t/ha in furrows at the time of sowing in acid soils. Neelakanth, J.K (2012)

Murthy. C.S., Shedha Sai. M.V.R., Bhanuja Kumari V., Prakash V.S. and Roy P.S. (2008), study of crop condition and assessment of agricultural drought in rabi season using IRS- AWiFS images. Journal of Agrometeorology 10 (1): 19-26 Neelakanth, J.K. (2012), .Drought Characterization Impact Assessment and Management Strategies for Koppal District North–Eastern Karnataka. Unpublished Ph.D thesis Vasudevan, P. and Pathak, N. (2000), Drinking Water: The India Scenairo. DRWH Water Quality: a Literature Review, Milestone 1: Report C1.

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Flood

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R.K. Suryawanshi

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R.K. Suryawanshi

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Rainfall based Digital Flood Estimation Techniques R.K. Suryawanshi and S.S. Gedam Center of Studies for Resource Engineering, IIT Bombay, India [email protected], [email protected]

R.N. Sankhua National Water Academy, Pune, India [email protected]

ABSTRACT It is being experienced that the climate change is to cause severe floods. Peak flow estimation for flood forecasting as well as for real time water management with innovative use of modern technological tools has been the need of the hour. Hydrologic parameters such as rainfall, runoff etc vary both in space and time. For most of the hydrological models, one of the main components involved is rainfall-runoff process. Determination of more accurate spatial rainfall and resulting runoff on each hydrological unit is the technological challenge and is the major cause of hydrological uncertainties. Considering this, an attempt has been made in this study to make innovative use of spatial tools available. Using geospatial technology (SRS, GIS) it is possible to achieve more accurate areal rainfall. Spatially varied resultant runoff and distributed route flows from various segments of the water shed. This paper discusses Digital Rainfall Model-an innovative concept used for estimation of areal rainfall for the storm events. Digital Runoff Model- for spatial estimation of runoff depths (effective rainfall) using Arc-CN Runoff tools with Land use and soil map and antecedent moisture conditions of the catchment as inputs. Digital flood estimation has been attempted using Clarks approach using the spatially distributed time area diagram for the watershed. The results of this overall innovative Digital Flood Model are compared with conventional tools for observed nine storm events occurred in Panshet reservoir catchment within Krishna river basin in India.

KEYWORDS: Digital Rainfall, Digital Runoff, Digital Flood, GIS, SRS

INTRODUCTION With the recent hydro-meteorological events as experienced during past two-three years, it is seen that the effects of the climate change are gradually being visible. The climate change is to cause severe floods. To deal with these natural disasters, modern tools of geospatial technology can be used for quick and more accurate flood forecasting. Estimation of more accurate inflows during the storm event has been a crucial task for flood forecasting as well as real time water management for the storage reservoirs. Hydrologic parameters such as rainfall, runoffs etc. vary both in space and time. For most of the hydrological models, one of the main components involved is rainfall-runoff process. Determination of more accurate amount of average rainfall and resulting runoff that a given storm event will produce on each hydrological unit is the technological challenge as it is the major cause of error in the applied hydrology. To take care of the spatial variability, the hydrological unit is divided into various sub-units with hydro meteorologically homogeneous characteristics like rainfall pattern--- etc. to arrive at more accurate areal rainfall-runoff. Larger the number of sub-units better is the accuracy. Using modern tools of geospatial technology, (SRS, GIS) it is possible to achieve more accurate areal rainfall and resulting runoff. NSCCIWRS

THE STUDY AREA The study area is the Panshet reservoir catchment within Krishna river basin in India. The catchment is elongated in shape having area of about 116 sq km. The region is hilly having steep slopes predominant land use being thin forest & agriculture. The average annual rainfall is about 2.5 m. Intense storms occur in the region resulting in flash floods. The study basin has rainfall network of four stations namely at Mangaon. Koshimgarh, Shirkholi, & Panshet. The study area and the locations of the rainfall stations are depicted in Fig. 1. The nine number of peak storm events of different periods are as per Table 1.

Fig. 1: The Study Area & Rainfall Network

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National Seminar on Climate Change Impacts on Water Resources Systems

Table 1: Storm Events Event No. 1 2 3 4 5 6 7 8 9

Storm Month/Year July-86 July-89 July-89 Aug-90 Aug-90 Aug-90 Aug-90 Aug-90 Aug-90

Storm Duration Hrs 66 63 57 51 75 75 51 75 54

Time Step Hrs 3 3 3 3 3 3 3 3 3

Total Rainfall (mm) 192 529 266 92 250 362 162 179 89

Observed Runoff (mm) 100 307 166 55 201 219 101 126 54

DIGITAL RAINFALL ESTIMATION The estimation of areal rainfall using the observed point rainfalls at various stations can be achieved using various techniques which include conventional as well as recent tools. [1][2] The spatial distribution of rainfall is affected by many factors. Rain-gauge networks are designed to sample this distribution optimally. A method of estimating mean aerial rainfall should be able to represent this distribution. The mean areal value (R) of rainfall over a basin area A, can be algebraically expressed as R = A r (x, y) d x d y / A dx dy

rainfall values r1, r2, --- rN measured by gauges. In this study the point rainfall of the four stations collected for the study area is distributed spatially using the innovative concept of Digital Rainfall Model (DRM). The Digital Rainfall Model (DRM) concept is similar to Digital Elevation Model (DEM). The pixel wise spatial distribution of the rainfall is achieved for each of the three hourly spell of the rainfall using Inverse Distance Weights (IDW) available in spatial analysis in ARC-GIS tools. The sample pixel wise distribution of rainfall in the study basin for the duration is depicted in Fig. 2 Finally the combined digital average rainfall for the event in the basin is computed. Accordingly the hyetographs computed with corresponding observed runoff hydrographs are shown in Fig. 3

(1)

Where r(x, y) is the rainfall surface function which is never known but can be approximated from the point

Fig. 2: Digital Rainfall Distribution

Fig. 3: Digital Rainfall Hyetographs 60

R.K. Suryawanshi

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Rainfall based Digital Flood Estimation Techniques

DIGITAL RUNOFF ESTIMATION For estimation of spatially distributed runoff forms the crucial exercise in hydrologic modeling. Wide range of models which include empirical lumped mathematical models to conceptual distributed deterministic models are in vogue.[3][4]. Application of GIS for runoff computation has been widely attempted. Since the runoff generation depends on spatially varied catchment characteristics, climatic parameters as well as antecedent moister conditions the estimation of its spatial variation is essential for more accurate results. [5] The runoff curve number (CN) method developed by the Soil Conversation Services (SCS) with CN values for Indian conditions are used for the purpose. The objective is to estimate direct runoff depth from storm rainfall depth based on the parameter referred to as “Curve Number” which are estimated on the information obtained from various thematic maps of the catchment such as land use, land cover/ soils, as well as antecedent moisture condition using following equation. Q = [CN (P-2) –200]2/CN [CN (P-8) + 800]

Fig. 4: Digital Runoff Layers

(2)

Subject to P > (200/CN)-2, else Q = 0 Where, P = Total Precipitation Q = Direct runoff CN =Curve Number (0-100) The SCS have classified various soils characteristics on the basis of infiltration rate and the runoff curve numbers for various combinations of soils and covers. The curve numbers are assessed using the clip layer obtained using land use and hydrologic soil group layer respectively with appropriate antecedent moisture conditions using ARC-CN. The satellite image (IRS -LISS-III) of the study area has been used for getting classified into land use groups using ERDAS IMAGINE. Soil map of the study area is based on the All India Soil Survey Map. The clip intersection layer of land use and soil map is obtained using ARC-CN. [6] [7] Using these layers and the lookup table for AMC –II conditions, the CN for each land use category & in turn digital runoff layers. for each rainfall depths of the storm event are computed. The areal average of the digital runoff with respect to each of the digital rainfall is plotted. The computed value of each of the three hourly digital runoff compared with the observed runoff along with the respective scatter plots for all the nine storm events are shown in Fig. 4. Digital runoff as obtained is shown in Fig. 5

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Fig. 5: Digital Runoff Results

DIGITAL FLOOD ESTIMATION From The runoff estimation using various deterministic methods It is then necessary to determine the flow estimation at the forecast point. The most common deterministic approach for the purpose is to develop the Unit Hydrograph commonly known as UG. For deriving the catchment response function which is UG, the Clark’s approach uses three parameter i.e. time of concentration (Tc), a storage attenuation coefficient (R), and time-area diagram, which can be drawn for the gauged basin using the isochrones for various durations.. Using these parameters and the time area diagram with convolution of unit instantaneous rainfall is routed through linear reservoir to obtain the Instantaneous Unit Hydrograph (IUH)( Fig 6) The IUH can be converted into Unit Hydrograph (UG) of any desired duration

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CLARK METHOD FOR segment

SURFACE RUNOFF AND INTERFLOW 55

77

44

66

33

22

11

isochrones

7 6 time

5 4

3

2 1

tU H(

ea ar

)

Time-area Time-areadiagram diagram

1-hour 1-hour Unit UnitHydrograph Hydrograph

considered between 2 & 4 Hrs whereas R/(R+Tc) values were varied between 0.2 to 0.4. The comparative analysis of results obtained using these various sets of Tc & R/(R+Tc) reproducing all the events satisfactorily, it is seen that most appropriate set has values 2.4 hrs and 0.3 respectively. This means that Tc is 2.4 hrs and the storage coefficient is 2 hrs for the study area’ The Effective rainfall depths which generate net runoff as obtained using digital runoff approach are used for convolution with the UG ordinates to arrive at the net inflow hydrographs for each of the observed events.

time

Linear Linear reservoir reservoir

TTc c kk

IUH IUH

UH UH

Fig. 6: Clark Approach

For deriving the representative catchment response function (UG), the input information needed is the Tc & Time Area Histogram based on isochrones. Isochrones can be digitized and interpolated to have spatial accuracy using ARC GIS (Fig. 7). The spatially distributed Time Area diagram based in the shorter & shorter intervals of the time of concentration (Tc) is routed through the liner reservoir having storage coefficient (R). The values of Tc & Rare to be decided using parameter optimization approach. Users of this model are of the view that it is better to decide the non dimensional parameter R/(R+Tc) for deciding R since Tc & R are generally interdependent. Using various pre determined values of Tc & R/(R+Tc), unit hydrograph (UG) ordinates are derived by routing the Time Area Histogram through linear reservoir with storage coefficient as R and appropriate routing time interval. The parameter optimization has been carried out using the FORTRAN program. Tc values were

Fig. 7: Isochrones

The convoluted and routed flood hydrographs for average effective rainfall and the digital effective rainfall are compared with the observed runoff hydrographs at the outlet for all the nine events which are depicted in Fig 8. The average relative error has been computed for both resultant flood hydrographs with respect to the observed events using RE = (Qobs- Qcomp)/Qobs (3) Where RE = Relative Error, Qobs = observed flow, Qcomp= computed flow The error analysis results in the form of cumulative relative errors for each event are shown in Table 2

Fig. 8: Digital Flood Hydrograph Results 62

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Rainfall based Digital Flood Estimation Techniques

Table 2: The Relative Error Analysis Events AVG RF DGTL RF

1 -1.091 -0.995

2 -0.281 -0.653

3 -0.093 0.033

4 -0.729 -0.077

From the relative error analysis it can be seen that, for most of the event times the computed hydrographs obtained using digital rainfall were close to the observed events showing lower values of relative errors. Hence it is seen that the digital rainfall, digital runoff and digital routing techniques as used above are simple and quick methods for computing more accurate flood discharges taking care of spatial and temporal variations of the hydrological parameters using geospatial tools.

CONCLUSION For real time flood forecasting as well as water management purpose, more accurate assessment of rainfall as well as runoff is essential. GIS/ SRS based spatial analysis tools can be effectively used to have more accurate assessment of spatially variable parameters such as rainfall & runoff. The results as obtained in the study indicate that the digital analysis gives better results compared to conventional tools.

REFERENCES Aronica G. and. Cannarozzo M. (1999) “Studying the hydrological response of urban catchmets using a semi distributed linear, non linear model’’ Journal of Hydrology 238,pp 35-43. Brand I., Fernandez P. and Bokraoui F. (1999), “Study of rainfall–runoff process in the Andes region using a continuous distributed model” Journal of Hydrology 216, pp 155-171 Cameron M. Zealand, Donald H. Burn Slobodan, P. Simonovie (1999), “Short Term Stream flow forecasting using artificial neural networks” Journal of Hydrology 214, pp 32-48

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5 -0.133 0.085

6 -0.329 -0.674

7 -0.179 -0.322

8 -0.353 -0.199

9 -0.255 0.021

Total -3.444 -2.780

Deemetris Kostsoyiannis Christian et al (2000) “Rainfall disaggregation using adjusting processes on Poisson cluster model” Journal of Hydrology 246,pp 109-122 James E. Ball and Kin Chai Luk (1998) “Modeling Spatial Variability of rainfall over a catchment”–Journal of Hydrologic Engineering ASCE, pp 122-130 Ministry of Agriculture, Govt. of India “Estimation of Direct runoff from rainfall ’’ Handbook of Hydrology, Ch.5 Sezin Tokar A., Peggy A. Johnson (1999), “Rainfall–Runoff Modeling using Artificial Neural Network”, - ASCE Journal of Hydrologic Engineering Vol4 No.3 (July 1999) Paper No. 14723 (232-239) Schumann A.H., Funke R. and Schultz G.A. (1999) “Application of GIS for conceptual rainfall runoff modeling “ Journal of Hydrology 240,pp 45-61 Xiaoyong Zhan ArcCN-Runoff: (2004) “An ArcGIS tool for generating curve number and runoff maps” February 2004 Environmental Modelling & Software Training Volumes (HP-I), National Water Academy, Pune, India http:/www.eusoils,jrc.it/esdb_archive/EuDASM/Asia/lists/cin. htm http:/www.water.usgs.gov/wid/FS http:/www.agu.org/eos http:/www.susquehannafloodforecasting.org http:/www.nidm.gov.in/idmc/Proceedings http:/www.alyuda.com/neural-networks-software.htm

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Development of Flood Inundation Model for Surat City under Changing Climatic Condition in Tapi Basin P.V. Timbadiya Assistant Professor, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

P.L. Patel Professor & Head, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

P.D. Porey Director, SV National Institute of Technology-Surat, Gujarat, India [email protected]

ABSTRACT The past climate change studies revealed the variation in the climatic parameters across the whole world. In present study, the extreme annual rainfall of four rainfall gauging stations, namely, Bhusaval, Burhanpur, Gidhade and Ukai dam, in the Tapi basin are analysed for their trend in the recent past. Also, the trend of four stream gauging stations are analysed to ascertain the impact of climate change on extreme flow in the Tapi basin. Keeping in view the aggravating trend of extremes, i.e. rainfall and flood discharges, in the Tapi basin, the results of a 1D 2D coupled model developed for the Surat city is reported taking in to account the outflow from the Ukai reservoir as upstream boundary condition while the tidal variation in the sea as the down stream boundary condition. The model is capable of simulating the flow in the stream as well urban flood plain of the Surat city

KEYWORDS: Trend Analysis, 1D-2D Coupled Model, Extreme Annual Flow, Land Use/ Land Cover, Inundation Map

during 1994, 1998 and 2006. Flood for year 2006 alone caused a damage to the tune Rs. 21000/-crores for the country excluding loss of human lives (150 people) in the incident.

INTRODUCTION

Almost 80% of area of the Surat city was flooded in year 2006 with highest outflow discharge of about 25780 m3s-1 from Ukai dam which is located about 100 km upstream of the Surat city. The floods events were experienced in the Surat due to high outflow from the Ukai dam mainly in the August and September months. The Ukai dam is multipurpose project situated at chainage 596 km on 724 km long Tapi River shown in Fig. 1.The change in inflow into the Ukai reservoir due to possible climate changes is a major issue to plan resilience strategy against the flood in the Surat city. Present study has been planned to fulfill following.

The Surat city, Gujarat, India is located near the Arabian Sea coast, one of highly urbanized cities of the country and situated on the tail portion of the Tapi River. Apart from the Surat city as such, many industries including KAPS (Kakrapar Atomic Power Station), Reliance Industries Limited, Larson and Toubro Limited, Essar Steel Limited, ONGC (Oil and Natural Gas Corporation Limited), KRIBHCO (Krishk Bharti Cooperative Limited) etc. are located on bank of lower Tapi River. After commencement of Ukai dam in 1972, the Surat city had faced major floods in the past

Fig. 1: Index Map of the Tapi River Basin 64

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Development of Flood Inundation Model for Surat City under Changing Climatic Condition in Tapi Basin

Objectives: 1.

Detection of trend and variability of extreme daily rainfall of four rain gauge stations namely Bhusaval, Burhanpur, Gidhade and Ukai over Tapi basin.

increasing trend. In Fig. 5, the Ukai station shows a monotonic increasing trend particularly in the medium range. Similar analysis was also reported earlier by Timbadiya (2013). A n n u a l m a x im u m d a ily r a in fa ll in m m a t B u rh a n p u r

3.

Trend analysis of extreme annual flow at Gopalkheda, Burhanpur, Sarangkheda and Ukai dam gauging stations in the Tapi basin. Development of 1D-2D coupled hydrodynamic model and its calibration for future flood simulation.

TREND DETECTION & VARIABILITY OF EXTREME DAILY RAINFALL

Table 1: Details of Meteorological Stations and Time Series Data Station

1 2 3 4

Data Period 1976-2007 1970-2007 1972-2007 1970-2007

Bhusaval Burhanpur Gidhade Ukai

20 0 18 0 16 0 14 0 12 0 10 0 80 60 40 20 0

The observed extreme annual rainfall data were used to detect the trends at four rain gauge stations namely Bhusaval, Burhanpur, Gidhade and Ukai (Bhuvandas et al. 2013). This innovative methodology proposed by Şen (2012), applicable to any time series irrespective of their sample size, serial correlation structure and nonnormal probability distribution functions, has been used. The brief summary of the data, included in Table 1 and procured from India Meteorological Department (IMD), have been used for foregoing analyses.

No.

22 0

0

Mean of Daily Maximum Rainfall (mm) 101.77 96.72 79.38 168.03

20

40

60

80

100

1 20

14 0

160

180

200

220

2 40

F ir s t h a lf o f th e s e r ie s ( 1 9 7 0 - 1 9 8 8 )

Fig. 3: Trend Analysis of Daily Maximum Rainfall at Burhanpur A n n u a l m a x im u m d a ily ra in fa ll in m m a t G id h a d e 240

Second half of the series (1990-2007)

2.

Second half of the series (1989-2007)

24 0

220 200 180 160 140 120 100 80 60 40 20 0 0

20

40

60

80

100

120

140

160

180

200

220

240

F irs t h a lf o f th e se rie s (1 9 7 2 -1 9 8 9 )

Fig. 4: Trend Analysis of Daily Maximum Rainfall at Gidhade

A n n u a l m a x i m u m d a i ly r a i n f a l l i n m m a t B h u s a v a l

An nual m a xim um d aily rain fall in m m at U kai 220

240

200

220

180 160 140 120 100 80 60 40 20 0 0

20

40

60

80

100

120

140

160

180

200

220

240

F i r s t h a lf o f t h e s e r i e s ( 1 9 7 6 - 1 9 9 1 )

Fig. 2: Trend Analysis of Daily Maximum Rainfall at Bhusaval.

Second half of the series (1989-2007)

Second half of the series (1992-2007)

240

200 180 160 140 120 100 80 60 40 20 0 0

20

40

60

80

100

1 20

140

160

18 0

200

2 20

240

First ha lf o f the series (1970 -19 88)

Fig. 2 shows that the rainfall data at Bhusaval, the lower values fall near the 1:1 line showing no significant trend whereas the higher rainfall values has an increasing trend. In Fig. 3, at Burhanpur, it is more or less monotonic in nature with higher rainfall values following an increasing trend. The trend at Gidhade also shows an trend (Fig. 4) as the data points fall in the second half of the series with respect to the first half. The higher rainfall value here has slightly NSCCIWRS

Fig. 5: Trend Analysis of Daily Maximum Rainfall at Ukai

TREND ANALYSIS OF EXTREME ANNUAL FLOW Annual peak flow time series for different stream gauging stations, being used in the present study, are shown in Table 2 for the data procured from Central Water Commission (CWC).

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Table 2: Annual Extreme Flow Time Series Data Name of River Purna Tapi Tapi Tapi

Duration of Data 1980-2007 1972-2007 1978-2007 1972-2009

Frequency 3 -1

Name of the Station Gopalkheda Burhanpur Sarangkheda Ukai

Annual peak discharge (m s )

Sr. No. 1 2 3 4

Hourly Hourly Hourly Hourly

4000

Trend line

2000

1000

3000

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

2000

1983

0

Trend line

Year

Fig. 8: Time Series of Annual Peak Flow (Gopalkheda Gauging Station)

1000

0

Fig. 6: Time Series of Annual Peak Flow (Sarangkheda Gauging Station)

25000

20000

15000

10000

4000

3000

2000

1000

0 0

1000

2000

3000

4000

5000

First half of the series (1980-1993)

Fig. 9: Trend Analysis of Annual Peak Flow (m3s-1) at (Gopalkheda) 25000

Annual peak discharge (m3s-1)

The trend line on the time series of annual maximum flow is shown in Fig. 6. Also, the innovative trend analysis as proposed by Sen (2012) is included in Fig. 7 for the same station. From both the Fig. s, it is observed that annual peak discharges are in increasing trend particularly high discharges at the Sarangkheda station. Similarly, the trend line on time series of extreme annual flow and innovative trend analysis are shown in Figs. 8 and 9 respectively. Both Figs.8 and 9 indicate an increasing trend of peak flow at the Gopalkheda station. Similar trend analyses are reported for Burhanpur and Ukai stream gauging stations, see Figs. 10-13. The trend of extreme annual flows show an increasing trends at both the station.

Second half of the series (1994-2007)

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

5000

Year

Second half of the series (1993-2007)

3000

1981

3 -1

Annual peak discharge (m s )

5000

4000

20000

15000

Trend line

10000

5000

5000

3 -1

Fig. 7: Trend Analysis of Annual Peak Flow (m s ) (Sarangkheda Stream Gauging Station)

66

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

25000

1983

20000

1981

15000

1979

10000

First half of the series (1979-1992)

1977

5000

1973

0

1975

0 0

Year

Fig. 10: Time Series Of Annual Peak Flow (Burhanpur Gauging Station)

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Development of Flood Inundation Model for Surat City under Changing Climatic Condition in Tapi Basin

DEVELOPMENT OF 1D-2D COUPLED HYDRODYNAMIC MODEL AND ITS CALIBRATION

Second half of the series (1991-2007)

25000

20000

Keeping in view with an increasing trend of rain fall and extreme flow in the Tapi basin, there is an urgent need for development of early warning system for forecasting the floos in the highly urbanised Surat city. The prediction of inflow into the Ukai Reservoir, reservoir operation and development of a hydrodynamic model for the lower Tapi river (down stream of Ukai Reservoir) are the integral components of the said early warning system. The 1D models are relatively simpler in nature, efficient, however, less accurate in flood inundation. Such models, perform well till the water is confined on both banks of the river. On other hand, 2D models are accurate in flood mapping, however, relatively complex and high time consuming in forecasting the flood in the flood plain. The advantage of 1D and 2D coupled model can be gained by coupling them in such a way such that 1D model works in the stream while the 2D model in the flood plain. The 1D2D coupled model for the lower Tapi basin has developed using MIKE FLOOD, a software developed by Denish Hydraulic Institute (DHI, 2009).

15000

10000

5000

0 0

5000

10000

15000

20000

25000

First half of the series (1972-1990)

Fig. 11: Trend Analysis of Annual Peak Flow (m3s-1) t (Burhanpur Gauging Station)

Annual peak discharge (m3s-1)

35000

30000 25000

20000

Trend line 15000 10000

5000

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

0

Year

Fig. 12: Time Series of Annual Peak Flow (Ukai Dam Gauging Station) Second half of the time series (1992-2009)

40000

35000

30000

25000

20000

15000

10000

5000

0 0

5000

10000

15000

20000

25000

30000

35000

First half of the tim e series (1972-1991)

Fig. 13: Trend Analysis of Annual Peak Flow (m3s-1) (Ukai Gauging Station)

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40000

Total 190 cross sections were available for the lower Tapi River reach between Ukai dam to the Sea. The x-z co-ordinates were entered as raw data in the cross section editor. Observed sea level and flood hydrograph of the year 2006 from Ukai dam have been taken as downstream and upstream boundary conditions respectively for the development of 1D model for the Lower Tapi River. The calibration of 1D model parameter, i.e. Mannings roughness, has been carried out using trail and error method by comparing the simulated water levels with observed water levels along the lower Tapi River at Kakrapar weir, Mandavi bridge, Ghala and Nehru bridge (Surat City) stream gauging stations using root mean square error (RMSE). It is found that the simulated results are closer to observed stages for Manning's n channel roughness corresponding to 0.03. Accordingly, ‘n’ =0.03 has been considered for further simulation for 1D model. The contour plans of the Surat City and its outskirt area were collected from Surat Municipal Corporation and converted into the raster data using Spatial Analyst tool of Arc GIS 10 with the cell size 25  25 m. The aforesaid data were saved in ASCII data format and transported to MIKE Zero Toolbox (Grd2Mike tool) to create the bathymetry of the study area. Indian Remote Sensing Satellite P-6 LISS III image dated February 10, 2009 was used to classify the land use and land cover pattern of the Surat City using supervised classification in ERDAS IMAGINE 10. The classification of land use pattern was undertaken for the whole floodplain area upstream of the sea boundary condition and the area up to which the flood was inundated in the floodplain

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during the flood of August 2006. Major land use/land covers were classified in the area are residential area (330.803 km2), agricultural area (187.975 km2) and water body (74.881 km2). The Manning's roughness value, ‘n’ used for each class was taken from previous investigations as shown in Table 3. The single (global) weighted value for Manning’s ‘n’ for Flood plain was derived and found to be 0.14. Table 3: Manning’s Roughness Value ‘n’ used for Floodplain Description Manning’s ‘n’ Source Residential area 0.20 Alkema, 2003; van der Sande et al, 2003 Agricultural area 0.07 Connell et al, 2001 Water body 0.03 Chow et al, 1959; van der Sande et al, 2003

The 2D bathymetry of the Surat City and its outkirt area has been coupled with 1D hydrodynamic model of the lower Tapi River using MIKE FLOOD. The flood of year 2006, (August 06, 2006 Time: 6:00:00 to August 10, 2006 Time: 02:00:00 (total 92 hours)) has been selected as simulation period to include the flood peak within the selected period. Fixed time step of 5 seconds have been selected in HD modelling while results saving frequency was 1 hour. The discharge and water level hydrographs were generated at every alternate point with 1 hour interval. Also, flood inundation map of the Surat City and its outskirt area has been generated with the aforementioned simulation. Flood inundation map of the Surat city for flood of year 2006 at different times are shown in Figs.14-16. Clearly, the inundation is maximum in the city on Aug.8,2006 at 24:00:00 hours. The simulated inundation depths for all the zones were compared with corresponding observed depths in the whole flood plain for different values of Mannings 'n' in terms of RMSE values. The RMSE value corresponding to n=0.14 has been found to be 0.89 for the combined zones.

Fig. 15: Flood Inundation of Surat City and Outskirt Area on Date: 07-08-2006 at Time 24:00:00 having Ukai Discharge: 23,597 m3s-1

Fig. 16: Flood Inundation of Surat City and Outskirt Area on Date: 08-08-2006 at Time 24:00:00 having Ukai Discharge: 25,770 m3s-1

CONCLUSION The work reported in the presented study, can be summarised as follows: a.

The trend of extreme rainfall at all four rain gauging stations, using innovative trend analysis methodology proposed by Şen (2012), indicated an increasing trend in the Tapi basin.

b.

The trend lines on time series of past observed annual peak flow data and innovative trend analyses at four stream gauging stations, revealed an increased trend of flood flow in the Tapi basin.

c.

The calibrated 1D/2D hydrodynamic model can be utilized to simulate the flood of different return periods in the coastal floodplain area of the Surat City under changing climatic conditions. The information

Fig. 14: Flood Inundation of Surat City and Outskirt Area on Date: 07-08-2006 at Time 12:00:00 having Ukai Discharge: 19,974 m3s-1 68

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Development of Flood Inundation Model for Surat City under Changing Climatic Condition in Tapi Basin

received from the model could be useful for flood plain management, levee design, flood damage assessment and flood inundation mapping etc. in the lower Tapi River and its floodplain. d.

The Study presented the database to derive the resilience strategy against flood in the Surat city for floods of different return periods.

ACKNOWLEDGEMENT Authors are thankful to Technical Education Quality Improvement Programme (Phase II) for funding Centre of Excellence (COE) on ' Development of Water Resources and Flood Management at SVNIT Surat'. Present study is part of the activities the said COE. Authors are also thankful to the Central Water Commission (CWC) Govt. of India; Surat Municipal Corporation (SMC) and Water Resources Water Supply and Kaplpsar Department, Govt of Gujarat for providing necessary data for present investigation.

REFERENCES Alkema, D. (2003) “Flood risk assessment for EIA; an example of a motorway near Trento, Italy.” Studi Trentini di Scienze Naturali–Acta Geologica, 78:147-153.

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Bhuvandas, N, Timbadiya, PV, Patel, PL, Porey, PD (2013). "Analysis of trends and variability in time series of extreme daily rainfall in Tapi basin, India", Proceedings of 35th IAHR World Congress, A11949, Chengdu, China, Tsinghua University Press, 2013. Chow, V.T. (1959). Open-channel hydraulics, McGraw-Hill, New York. Connell, R.J., Painter, D.J., and Beffa, C. (2001). “Twodimensional flood plain flow. II: Flow Validation.” Journal of Hydrologic Engineering, 6(5):406-415. DHI (Danish Hydraulic Institute). (2009). MIKE FLOOD: 1D-2D Modelling. User manual. Huang Y., (2010) “Rapid flood risk assessment using GIS technology.” International Journal of River Basin Management, 7(1):3–14. Şen Z., (2012) “An innovative trend analysis methodology.” Journal of Hydrologic Engineering, 17(9):1042–1046. Timbadiya P. V., Mirajkar A. B., Patel P.L. and Porey P.D, (2013) “Identification of trend and probability distribution for time series of annual peak flow in Tapi Basin, India.” ISH Journal of Hydraulic Engineering, 19(1): 11-20. Van der Sande, C. J., de Jong, S. M., and de Roo, A. P. J. (2003) “A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment.” International Journal of Applied Earth Observation and Geoinformation, 4: 217-229.

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Flood Forecasting and Reservoir Operation as an Adaptive Measure for Climate Change in Tapi Basin Shekharendu Jha Deputy Director, M&A Dte, Narmada and Tapi Basin Organization, Central Water Commission, Vadodara [email protected]

Rishi Srivastava Superintending Engineer–Coord, Narmada and Tapi Basin Organization, Central Water Commission, Gandhinagar

Vikas Barbele EAD–HydroMet, Tapi Division, Narmada and Tapi Basin Organization, Central Water Commission, Surat

ABSTRACT Surat City and its surrounding area is the most flood prone area in the Tapi Basin. The flood moderation for this area is done by Ukai Reservoir. The flood forecasting and Reservoir operation are two of the most important factor for flood moderation. The forecasting is done by the Surat Division of Central Water Commission and the accuracy has been commendable. The flood of 2006 has been taken as case study for reservoir operation and shown that a proper operation can save major flooding of Surat even in the extreme flood event like that in the year 2006.

KEYWORDS: Climate Change, Central Water Commission, Tapi Basin, Flood Forecasting, Ukai Dam, Reservoir Operation, Telemetry

INTRODUCTION As per the Comprehensive Mission Document for National Water Mission under National Action Plan for Climate Change, one of the likely impacts of climate change on water resources could be in the form of increased flood events due to overall increase in the rainy day intensity. Insuch a scenario, the flood forecasting mechanism and complementing it the Reservoir Operation of the reservoir for moderating flood would be a crucial factor in adapting to the climate change. This paper discusses the preparedness of flood forecasting by Central Water Commission for Tapi Basin as well as the Reservoir operation of Ukai Reservoir for 2006 flood which can be taken as a case study of an extreme flood event expected more frequently in case change in climate takes place.

FLOOD FORECASTING As per the Comprehensive Mission Document for National Water Mission under National Action Plan for Climate Change, one of the likely impacts of climate change on water resources could be in the form of increased flood events due to overall increase in the rainy day intensity. Along with structural measures, the Government of India lays parallel emphasis on non-

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structural measures for flood damage reduction like flood forecasting, flood plain zoning etc. The work of flood forecasting in different river basins is one of the major activities being done by Central Water Commission. As per the report of the Rashtriya Barh Ayog (National Flood Commission) submitted in1980, the lower Tapi Sub-basin is demarcated as flood prone area in the Tapi Basin. The city of Surat is most vulnerable in this area.

Fig. 1: Flood Prone Area of Tapi Basin (Demarcated in Red)

Tapi Division of Central Water Commission in Surat is presently entrusted with flood forecasting in the river Tapi(inflow forecast for Hathnur and Ukai reservoirs and level forecast for Surat). Flood Forecasting Setup of Tapi Division for Tapi River Under the Hydrological Observations (HO)/ Flood Forecasting (FF) set up, Gauge, Discharge, silt and Water quality are being observed at 45 stations are as under.

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Flood Forecasting and Reservoir Operation as an Adaptive Measure for Climate Change in Tapi Basin

Table 1: Details of HO/FF Stations under Tapi Division Name of Sub Division Upper Tapi sub Division Middle Tapi Sub division Lower Tapi sub Division Total

Gauge

Gauge, Discharge & Silt 1

Gauge, Discharge, Silt & Water Quality 2

Only Rainfall

Total

5

Gauge, Discharge & Water Quality --

3

11

4

1

--

1

3

9

3

--

2

--

1

6

12

1

3

3

7

26

The data of 18 Nos. Gauge/ Gauge discharge/ Rainfall sites in Tapi basin are received through wireless on real time and are being utilized for formulation of forecasts. The locations of all the Gauge, Gauge discharge, and Wireless and rainfall stations under F.F. Network in Tapi Basin is given at Plate no. 1. For the improvement of Forecasting work and availability of data throughout the year, Telemetry system has been introduced in Central water commission. During the XI th Five Year plan, the Telemetry system at 38 remote stations has been installed in the Tapi Basin with two model stations under Tapi Division, Surat. The names of the stations are given at Annex-I Methodology The methodology used for both level and Inflow forecast, is as per the C.W.C. manual on flood forecasting. Mainly graphical technique is used for most of the Sites. In graphical techniques various co-relation diagrams depicting the effect of basin parameters are prepared and tested for accuracy. This Division had prepared a number of co-relation diagrams, stage discharge curves/tables, and travel time curves for different forecasting Sites based on 10 to 15 years data. These curves are updated every year and used for flood level forecasts. Inflow forecasts / flood level forecasts are issued as per criteria fixed by State Govt. The procedure followed for inflow forecast for Hathnur and Ukai Dam and the level forecast for Surat are described below: Hathnur Dam

These forecasts are monitored during the period of forecasts and revised (if necessary) depending on the conditions developed in the Intermediatecatchment between Burhanpur/ Yerli to Hathnur Dam. Ukai Dam In normal situation, 12 hourly regular Inflow forecast is issued forwhich Gidhade has been considered as base station. Travel time from Gidhadeto Ukai is taken 12 hours. All the real time stages of past 12 hours of Gidhade are converted into discharges by using S-D Curve. These discharges are added together and multiplied with time to get the expected volume of Inflow in million cubic metre at Ukai in next 12 hours. Contribution of rainfall received or the loss due to infiltration inthe intermediate catchment and also release of Prakasha, Sarangkheda and Sulwada barrage when gate operated are also considered before arriving at final Inflow forecast. These forecasts are monitored during the period of forecast and revised, if necessary, depending upon the conditions developed in the intermediate catchment between Gidhade to Ukai. Surat Based on the releases of water from Ukai Dam and Hydro-meteorological data of downstream of Ukai, flood level forecasts for Surat city are formulated by using various correlation, S-D Curve, Time lag curve etc. and issued to user agencies by about 6 to 8 hours in advance. Dissemination of Forecasts

6 Hourly regular Inflow forecasts are issued for Hathnur Dam. Burhanpur and Yerli G-D sites have been considered as base stations for issue of inflow forecast. Travel time from Burhanpur and Yerli is taken as 6 hours. The past 6 hours stages of Yerli and Burhanpur are converted into discharge (by using SD Curves of respectivesite). The averages of these discharges are multiplied with time [6 hrs] to get expected volume of Inflow in million cubic metres at Hathnur dam in next 5 or 6 hours. Due consideration is given to the NSCCIWRS

contribution of rainfall received or the loss due to infiltration in the intermediate catchment before arriving at the final Inflow forecast.

Dissemination is made on top priority by Wireless/ Telephone/ E– Mail or by sending special messenger to the civil authorities as per the guidelines given in Flood Memorandum of State Government. In addition to dissemination of forecast to the user agencies, messages are conveyed to newspapers also over telephone if required by them. Based on the forecast, the State Government authorities arrange the rescue work in well in advance to broadcast / telecast

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National Seminar on Climate Change Impacts on Water Resources Systems

the flood messages over Radio / T.V. so as to inform the people of the region about the situation of coming floods well in advance. Efficacy of Forecasts The forecast done by Tapi Division has been very accurate as can be seen in Table 2.

RESERVOIR OPERATION One of the adaptations to climate change may be to change reservoir operations to get more flexibility in its operations. It is apprehended that climate change would trigger high intensity short duration rains causing huge inflow in the reservoir over a short period of time. As discussed earlier, the lower Tapi Basin is the most flood prone area in the Tapi Basin with Surat being the most vulnerable city. The flood moderation for Surat and its surrounding area is done by the Ukai reservoir. With the moderation of flood at Ukai reservoir, no major floods were experienced at Surat till 1994. During 1994, 1998 and 2006 floods of the order of 14870 m3/s, 19820 m3/s and 28315 m3/s were experienced. However, the flood of 2006 was the biggest flood in last three decades. Due to very heavy releases commencing with the order of 3553 m3/s at 2300 hrs on 5th August continuously from the Ukai Reservoir, the water level started rising alarmingly at the flood forecasting station at Surat. Table 2: Overall Forecast Performance of Forecasting Stations (Inflow and Level Forecast) for Last 10 Year Year Total Nos. of Nos. of Forecast % of Forecast within Accuracy Issued Permissible Limit 2003 83 83 100 2004 51 50 98.04 2005 114 114 100 2006 193 185 95.9 2007 168 160 95.24 2008 57 55 96.5 2009 44 42 95.45 2010 191 190 99.5 2011 143 143 100 2012 260 258 99.23

The out flows from Ukai Dam were substantially increased and was about 5930 m3/s at 10.00 hrs of 6th August and were further increased. The water level observed at Nehru Bridge was 5.05 m at 1800 hrs on 6th August but started rising alarmingly and recorded as 6.90 m by 2400 hrs. The river continued to rise further and crossed warning level by 0500 hrs on 7th August. The Tapi crossed its danger level by 1100 hrs on 7th August, and surpassed its previous HFL of 12.01 m, recorded in 1968, with a water level of 12.10 m at 0100 hrs on 8th August. The water level continued to rise and attained the peak of 12.50 m which remained steady

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from 0700 hrs to 1600 hrs of 9th August 06. The water level at Nehru Bridge started receding very slowly thereafter. The river Tapi remained above warning level of 8.5m during the period from 0500 hrs on 7th August to 0200 hrs on 12th August and flown over danger level of 9.5 m during the period from 1100 hrs on 7th August to 1700 hrs on 11th August. The catastrophic floods had created havoc at Surat by surpassing its previous HFL and its continuance above danger level of 9.5 m for about 4 days and 6 hours [1100 hrs on 7th August to 1700 hrs on 11th August]. The case of 2006 is of utmost importance even from the point of view of climate change. It is this type of rainfall and inflow that is perceived to be more frequent in case climate change does occur. The flood of 2006 has been widely studied and discussed in public forums. Some of them are mentioned below: 

Flood Assessment by Integrated Hydrological Modeling with RS and GIS in Water Resources Management-Dhruvesh P. Patel (Ph.D Thesis).



Feasibility of Tapti Basin Floods Moderation with Updated Technology and Management Dr. Mahesh D. Desai and ShriRavin M. Tailor



Feasible Structural and Non- Structural Measures to Minimize Effect of Flood in Lower Tapi Basin, Dhruvesh P Patel and Mrugen B Dholakia



Lessons from Massive Floods of 2006 in Surat City: A framework for Application of MS/OR Techniques to Improve Dam Management to Prevent Flood-DileepMavalankar and Amit Kumar Srivastava



Flood Water Surface Profile in Tapi RiverSurat, G. I. Joshia& A. S. Patel



Improving Carrying Capacity of River Tapi (Surat, India) By Channel ModificationAgnihotri P.G and Patel J.N

However, in this paper, a different approach has been taken with respect to the moderation of flood event. But before proceeding with the discussion, here are some facts about Ukai project relevant to its reservoir operation and consequent flood in Surat. The Ukai multipurpose project on river Tapi in Gujarat was completed in 1973 to provide benefits of irrigation, hydropower generation and partial flood control. The dam was designed to cope with Standard Project Floodof 43,490 m3/s (156.56 MCM/Hr)and Probable Maximum Flood of 59,920 m3/s (215.71 MCM/Hr). The spillway has the capacity to pass 37,859 m3/s (136.29 MCM/Hr) at FRL and 46,269 m3/s (166.57 MCM/Hr) at MWL.

Shekharendu Jha

NSCCIWRS

Flood Forecasting and Reservoir Operation as an Adaptive Measure for Climate Change in Tapi Basin

Table 3: Data Relevant to the Operation of Reservoir Sr. No. 1 2 3 4

Level MDDL FRL MWL Top of Dam

Elevation (m) 82.296 105.156 106.99 111.25

Capacity (MCM) 684.394 7414.29 8480.180

The peak outflow from the Ukai dam and the level of water at Nehru Bridge for the year 2006, 2012 and 2013 is shown in the figure below: Danger Level: 9.5 m at Nehru Bridge, Surat Warning Level: 8.5 m

The Safe Carrying Capacity of the river on the d/s was considered to be of the order of 24,069 m3/s (86.65 MCM/Hr). However, the Safe Carrying Capacity of the Tapi river near Surat is reported to have been significantly reduced due to encroachment in the flood plain areas, silting in the river bed and effluents caused by Singanpur weir constructed on the river close to the city. It is assessed that the river at Surat can now; carry only 11,327 m3/s (41 MCM/Hr) without causing significant damages. Nehru Bridge (Hope Bridge) is located at SuratOlpad-Sahol road which is designed for high flood level (HFL) of GTS-RL +11.515 m. The warning and danger levels with respect to flooding of Surat cityare fixed as 8.5 m and 9.5m respectively which corresponds to discharge of 11328 m3/s and 13,027 m3/s in the Tapiriver in Surat.

Fig. 2: Outflow and Level at Nehru Bridge

The Reservoir operation is simulated for the inflow of 2006 based on the forecast given by Central Water Commission. As can be seen from the figure given below, (Fig 4) the forecast was very accurate.

Fig. 3: Forecasted Inflow and Actual Inflow

Fig. 4: Different Rule Curves NSCCIWRS

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National Seminar on Climate Change Impacts on Water Resources Systems

It is important to note that the Reservoir Operation is mostly done on the basis of existing Rule Curves. Various Rule Curves that existed in 2006 and as modified later is depicted in the graph above (Fig. 5). The simulation is done considering the following criteria: 

With the warning of huge impending floods, its moderation necessitated that the reservoir operation breached the rule curve governed by conservation purpose and occasionally for power consideration.



Low flooding has been allowed to avoid severe floods.



Hourly Outflow has been controlled at predetermined level allowing for level fluctuations.



Only the period of peak flood i.e 2nd August, 2006 to 14th August 2006 is considered.

Simulation results are presented graphically in subsequent sections:

Fig. 5: Comparison of Actual Outflow and Simulated Outflow

Fig. 6: Comparative Chart for Actual Reservoir Level and Simulated Reservoir Level

CONCLUSION It can be seen that acting on the forecast by Central Water Commission; it would have been prudent to empty the reservoir to the extent that there was slight compromise in the power generation. The initial outflow of 45 MCM/Hr when the first forecast came on 2nd August, 2006 proved to be very effective in moderating the flood later on. The maximum outflow could be restricted to 60 MCM/ Hr even during the peak inflow of 134.64 MCM/Hr on 8th August, 2006. Corresponding to this the actual release was 84.78 MCM/Hr. The actual release went upto a maximum of 92.79 MCM/Hr on 9th August, 2006 which caused

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major flood in the Surat City. However, the simulated outflow is still at 60 MCM/Hr on this date. The major flood has been avoided with the reservoir operation as shown in the simulation.

REFERENCES Apprisal report Tapi January (2007), Tapi Division, Central Water Commission, Surat. Apprisal report Tapi January (2012), Tapi Division, Central Water Commission, Surat. Various official records of Tapi Division, Central Water Commission, Surat.

Shekharendu Jha

NSCCIWRS

Flood Forecasting and Reservoir Operation as an Adaptive Measure for Climate Change in Tapi Basin

Plate 1: F.F. Network in Tapi Basin

ANNEX–I Telemetry Network in Tapi Basin Sr. N. 1 2 3 4 5 6 7 11 12 13 14 15 16 17 18 19 20 21 22 32 33 34 35 38 39 40

NSCCIWRS

Name of Station Chikaldara Chiklod Shelgaon Nandurbar Khetia (Pansamal) Dusane (Nizampur) Sagbara Gopalkheda Teska Dedtalai Burhanpur Yerli Lakhpuri Morane Sarangkheda Savkheda Gidhade Ghala Surat Hathnur Girna Dam Dahigaon Ukai Dam Bhusawal Modeling CentreBhusawal Modeling Centre Surat

River Tapi Tapi Tapi Tapi Tapi Tapi Tapi Purna Tapi Tapi Tapi Purna Purna Girna Tapi Tapi Tapi Tapi Tapi Tapi Girna Girna Tapi Tapi __ _

Type of Station (Censor installed- RF/RF&G/ AWS RF RF RF RF RF RF RF RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G RF&G,AWS ModelingCenter Modeling Centre

Shekharendu Jha

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NSCCIWRS

Ratan Panchal

2

Hydel Power

NSCCIWRS

Ratan Panchal

1

NSCCIWRS

Ratan Panchal

2

An Overview of Sardar Sarovar Project with Special Reference to Power Component N.K. Bhandari Secretary, Sardar Sarovar Construction Advisory Committee, Vadodara

N.P. Namdeo Assistant Secretary, Sardar Sarovar Construction Advisory Committee, Vadodara ABSTRACT The Sardar Sarovar Project (SSP), originally proposed in the year 1946, is an inter-state multi-purpose project on the river Narmada in Gujarat. The project was inaugurated by the late Prime Minister, Shri Pandit Jawahar Lal Nehru, on the 5th April, 1961. The joint venture between the states of Gujarat, Madhya Pradesh, Maharashtra and Rajasthan is implemented as per the decisions of the Narmada Water Disputes Tribunal (NWDT)-1969, considering the development of the water resources of the basin as a whole as per its award in 1979. The completion of the project is delayed because of inter state matters, environmental issues, complex construction problems and Rehabilitation & Resettlement issues. The Unit-I component of the project involves main dam construction which started in April 1987 and was programmed to be completed by the end of January 1998. However, the construction work remained held up for long, due to litigation in the Supreme Court of India which was cleared in October 2000, accordingly the spillway portion could be raised above EL110.64m to EL 121.92m by December 2006. Further works to complete the dam with FRL of is yet to be completed. The Unit-III of the project involves mainly works in regard to the Canal Head Power House (CHPH), underground River Bed Power House (RBPH) and the Garudeshwar weir required for the reversible operation of the RBPH. All works of CHPH were completed in January 1998; all the six Units of RBPH have been commissioned by 2006. The construction work of Garudeshwar weir is started from Feb. 2013 and is in progress. Substantial power generation has been achieved & till June 2013, 27641.166 Million Units of energy has been generated by both power houses. The benefits of power generated have almost paid back the expenditure incurred on the power component. The paper discusses various aspects of Unit-I & Unit-III of the project and the benefits which have been envisaged by power generation.

Keywords: Interstate, Multi Purpose, Mega Project, NWDT, SSP, SSCAC, NCA, RBPH, CHPH, Narmada

INTRODUCTION The river Narmada, being one of the least tapped rivers in India, is the largest west flowing river in the country. It traverses Madhya Pradesh, Maharashtra, Gujarat and meets the Gulf of Cambay covering the total length of about 1312 kms having catchment of about 97, 410 sq. km. out of which 85, 858 square kilometer in Madhya Pradesh, 1658 square kilometer in Maharashtra and 9894 square kilometer in Gujarat. The mean annual rainfall in the basin is 1120 mm. The annual yield is about 34.5 Cu.Km. Even now large portion of the water of the Narmada continues to flow to the sea unused. The maximum recorded flood in the river is about 70, 847 cumecs. In spite of the huge potential, there was hardly any development in the basin prior to independence. Inter-State differences on sharing of waters of the river prevented any concrete action even after independence. The then Ministry of Irrigation & Power in 1969, constituted the Narmada Water Disputes Tribunal (NWDT) to adjudicate upon the water disputes. As per the decision of the Tribunal in 1979, out of the 34.5 cu.km utilizable quantum of Narmada water, Madhya Pradesh is to share 22.5 cu.km, Gujarat 11.1cu km, Rajasthan and Maharashtra to share 0.6 & 0.3 cu km respectively. As per the award Narmada Control Authority (NCA) is to monitor the water NSCCIWRS

sharing in the basin and Sardar Sarovar Construction Advisory Committee (SSCAC) is to oversee the implementation of the Sardar Sarovar Dam & Power House works.

SARDAR SAROVAR PROJECT The rainfall in most parts of Gujarat and Rajasthan is not only scanty, but very erratic with large spatial variations. Lack of adequate recharge has resulted into over-exploitation of the ground water in major water deficit regions of Suarashtra and Kachchh resulting in salinity ingress. The advancement of desert in many parts of Kachchh, North Gujarat and Rajasthan as well as general shortage of power resulting in lack of development in these regions. To address these issues, the Sardar Sarovar Project (SSP), one of the largest interstate water resources projects is being implemented by the participating states of Maharashtra, Madhya Pradesh, Gujarat and Rajasthan in co-ordination with Narmada Control Authority (NCA) and Sardar Sarovar Construction Advisory Committee (SSCAC) established under NWDT award. The project comprises a concrete gravity dam to impound a reservoir with gross storage of 9.5 cu.km for using about 11 cu km of Gujarat share of Narmada waters. The irrigation infrastructure having Narmada Main Canal transferring water to Mahi, Sabarmati basins as well as reaching to river basins of Luni, Banas, Saraswati, Vatrak, Som etc. to irrigate over 2 M Ha of drought prone CCA in

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National Seminar on Climate Change Impacts on Water Resources Systems

Gujarat and Rajasthan, apart from providing public water supply to over 8 thousand villages and 135 urban centers. As a major contributor in country’s hydropower share, the SSP has installed capacity of 1450 MW in its two power stations. The controlled outflows from the reservoir provides flood protection to the lower reaches of the river. In addition, there will be benefits of fisheries development, recreational facilities, water supply for industries, agro industrial development, conserved forest, employment generation etc. Because of availability of water & power, effect on agricultural & industrial production is already visible. With adequate and quality water available throughout the year, improvement in conditions of health, hygiene and sanitation, improved standard of living in the region is not distant dream. In addition, navigation & recreation infrastructure if properly planned and implemented may convert the project area to be one of the biggest tourist attractions. One statue of Sardar Vallabhbhai Patel, 182m high likely to be tallest in the world is proposed to be erected about 3.32km D/S of Sardar Sarovar Dam at Sadhu Tekri.

WATER AVAILABILITY The utilizable quantum of waters of Narmada at Sardar Sarvor dam site on the basis of 75% dependability is assessed at 34.5 cu.km. being shared in the proportion as 73% by M.P. 36% by Gujarat, 1% by Maharashtra and 2% by Rajasthan. The requirements of SSP have to be met from the releases by Madhya Pradesh and by inflows from the intermediate catchment, surplus to the requirements of Madhya Pradesh below Narmada Sagar and Maharashtra. Assuming the uniform releases, the amount of water to be released by Madhya Pradesh per month would be 0. 835 cu km. for the SSP reservoir. The NWDT has defined the utilization of waters in various stages Viz. Stage-I, Stage-II and Stage-III starting after 10 years, 30 years and 45 years respectively from the commencement of construction of dam. Although the construction work of dam was started in 1987, the project has been delayed due to various reasons. The Stage-I may likely to end after full completion of dam height of 138.68 m (FRL). Utilization of water at various stages of development considered by NWDT is given in following Table1. Table1: Utilization of Water as Per NWDT State

Utilization in cu km Stage-I (10 yrs Stage-II (30 yrs Stage-IIII (45 from Start of from Start of yrs from Start of Construction) Construction) Construction) M.P. 7.4 16.0 22.5 Maharashtra 0.3 0.3 0.3 Gujarat 3.1 11.1 11.1 Rajasthan 0.6 0.6 0.6 Total 11.5 28.1 34.5 *The utilizable waters at Sardar Sarovar Dam site at75% at (34.5cu km) 80

Table 2: Apportionment of Water among Party States Party States Madhya Pradesh Gujarat Rajasthan Maharashtra Total

MAF 18.25 9.00 0.50 0.25 28.00

MCu.m 22511.01 11101.32 616.74 308.37 34537.44

STORAGE The SSP reservoir is one of the unique storages in the country. The drainage area up to dam site is 88, 000 sq km. As per NWDT award, height of Sardar Sarovar dam should is fixed at Full Reservoir Level 138.68 m with Maximum Water Level at 140.21 m The gross storage capacity of the reservoir is 9.5 cu km with submergence of about 370 sq. km. (which is only 0.4 % of the total catchment) having submergence per unit storage of about 39 sq km /cu. km which is about half of the same for the up stream Indira Sagar Project. The live storage capacity is 5.8 cu km. The reservoir is elongated with total length of about 214 km and average width of 1.77 km. The storage being more or less confined to the valley, the evaporation and infiltration losses are comparatively low. The maximum recorded flood in the river is about 70, 847 cumecs. The dam is designed for passing the peak flood of 87000 cumecs. For chute spillway has 30 radial gates, 7 number in auxiliary spillway having size 18.30mx18.30m (60' x 60') and 23 numbers radial gates for service spillway having size18.30mx16.76m (60' x 55') being provided to negotiate the above design flood. While designing water resources projects, provision for accumulation of silt is made in working out economic life of projects by providing dead storage pocket so that the live storage does not get affected. For SSP, the design sedimentation rate is about 0.54 mm/year has been adopted which is much less than the normal rate of about 1 mm/year adopted for many reservoirs. This may be because of the series of upstream reservoirs trap efficiency and elongated as well as forested catchment of the river.

STRUCTURAL FEATURES & PROGRESS OF CIVIL WORKS The project comprises a 1.21 km long and 163m high concrete gravity dam with total volume of concrete of 6.82 MCM. The top of dam is at EL 146.50m and the crest level at EL121.92 m whereas the Full Reservoir Level (FRL) and Maximum Water Level (MWL) being at 138.68 m. and 140.21m. respectively. The Minimum Draw Down Level (MDDL) is at 110.64m and the Normal Tail Water Level (NTWL) being at EL 25.91m The dam has service spillway with 23 bays (18.30mx16.76m) and the auxiliary spillway with 7 bays (18.30mx18.30m). The design of the dam allows for a horizontal seismic coefficient of 0.125g and it also covers an additional risk due to so called reservoir

N.K. Bhandari

NSCCIWRS

An Overview of Sardar Sarovar Project with Special Reference to Power Component

induced seismicity. Seismological instruments network and other geotechnical instrumentation network required for monitoring the dam as well as the effect on the periphery of the reservoir is established. Year wise Achievement of construction of block level is given below in table No. 3:

Energy Dissipation arrangement

Length of N.O.F. Top Level of NOF & Rockfill Dam

Table 3: Minimum Block Levels (as on October 2013) Ending December 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 October 2013

Minimum Block Level (m) As Per Contract As Per RIS Achieved Award 12/89 45.00 23.00 25.50 75.00 45.00 33.00 85.00 65.00 59.00 111.00 85.00 69.00 121.92 110.00 80.00 138.68 121.92 80.30 -138.68 (FRL) 80.30 --80.30 --85.00 --90.00 --90.00 --94.00 --100.00 --110.64 --110.64 --121.92 --121.92 --121.92 --121.92 --121.92 --121.92 --121.92 --121.92

CONSTRUCTION OF GARUDESHWAR WEIR Garudeshwar Weir is located 12.10 km downstream of Sardar Sarovar Dam near Village Garudeshwar on Right Bank. The work of construction of Garudeshwar Weir has been started by the agency from 08.02.2013. Construction of approach roads, diversion structure, excavation and coffer dam is in progress. The salient feature of the proposed weir is given below:Garudeshwar Weir (Ungated) is located @ 12.10 Km D/s of Sardar Sarovar Dam Design discharge : 62815 cumec (22.18 Lakh cusecs) 1 in 50 year flood Full Reservoir Level : EL 31.57 m HFL at Design Discharge : EL 44.65 m Tail Water Level at : EL 40.20 m (Design Flood-1 in 50 Design Discharge year flood) Gross Storage Capacity : 8720 Ham (70708 Aft) [87.20 Mm3] Dead Storage Capacity : 5422 Ham (43974 Aft) [54.22 Mm3] Live Storage Capacity : 3298 Ham (26734 Aft) [32.98 Mm3] Total Length of Weir : 1187 m (i.e. 609 m (29 Nos. Spillway block) + 42 m (2 Nos. NOF block on Right Bank) + 147 m (7 Nos. NOF block on Left Bank) + 389 m (Rockfill Dam) Type : Solid concrete weir with Rock fill dam on left bank Overflow section : Ungated Ogee with 609 m length (29 Blocks–21 m each ) NSCCIWRS

Top width of N.O.F. & Rockfill Dam Length of Rockfill Dam

: Sloping Apron Stilling Basin having invert level from EL17.94 m to EL 12.0 m have been suggested by SSPH Directorate, CWC, New Delhi. (2012) : RHS 42 m LHS 147 m : EL 49.50 m (Free Board criteria for Maximum Flood of 1 in 500 year (28.354 lakh cusec) : 7m : 389 m

IRRIGATION The Narmada Main Canal having length of 458 km and 1133 cumecs capacity of water discharge at the head which tapered down to 71 cumecs at the tail i.e. Gujarat–Rajasthan border would be the largest irrigation canal in the world. The canal is to provide record irrigation facilities to over 2 M Ha. of land in Gujarat & Rajasthan and to about 37.5 Th Ha. in the tribal hilly tracts of Maharashtra through lift. The irrigation system is being developed with detailed elaborate and micro level planning. As per the GOG, the command area is divided into 13 agro climatic zones and each zone is further subdivided in to irrigation and drainage blocks ranging from 4000 to 10, 000 Ha. Involvement of farmers in the construction activities and there after for irrigation management is aimed at to ensure efficient user friendly managed by the Water Users' Associations (WUAs) based on Participatory Irrigation Management (PIM). As per the NWDT award, the cost of Unit-I (Dam and appurtenant works) is shared by Gujarat and Rajasthan in the ratio of 18:1. as well as the cost of canal as per the guidelines.One of the unique features in the command area of SSP is to deliver the irrigation water to WUAs. These WUAs are to manage distribution within their block called Village Service Area (VSA). Thus the management of the minors, sub minors and field channels to be owned and looked after by the WUAs. Rotational Water Supply (RWS)–Varabandhi, the volumetric supply of water, the micro level canal systems with appropriate structures, the evaluation based on delta basis, discouraging water intensive crops, micro irrigation system like drip and sprinkler, are some of the features being adopted by GOG and Govt. of Raj. to make the sustainable irrigation system. STATUE OF SARDAR PATEL One likely to be tallest statue in the world of Sardar Vallabhbhai Patel, 182m height is proposed to be erected about 3.32km D/S of Sardar Sarovar Dam at Sadhu Tekri. Proposed features are: 

182m tall statue of the Iron Man of India.



Location: Sadhu Bet (Island) @ 3.32 km downstream of Main Dam.

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National Seminar on Climate Change Impacts on Water Resources Systems



Estimated Cost: Rs. 2, 500 Cr



PMC Appointed: Turner, USA.



Facilities envisaged: Viewing gallery @ 450 ft,



Audio-visual gallery.



Exhibition.



Museum.



Research Centre.



Recreation.



Connectivity by ferry service.



Metro rail, multilane road.

PUBLIC WATER SUPPLY GOG has made a special allocation of 1.05cu km (0.86 MAF) of water to provide public water to 135 urban centers and 8215 villages (45% of total 18144 villages of Gujarat) within and out-side command in Gujarat for present population of 18 million and prospective population of over 40 million by the year 2021. All the villages and urban centers of arid region of Saurashtra and Kachchh and all "no source" villages and the villages affected by salinity and fluoride in North Gujarat will be benefited. Water supply requirements of several industries will also be met from the project giving a boost to all-round development. GOR has also made sufficient provision to supply drinking water to a population of about 45.88 lakhs in 1336 villages and 3 towns situated around the Narmada canal.

HYDROPOWER There are two power houses namely River Bed Power House (RBPH) and Canal Head Power House (CHPH) with an installed capacity of 1200 MW and 250 MW respectively. The power would be shared by three states-Madhya Pradesh-57%, Maharashtra-27% and Gujarat 16%. This will provide a useful peaking power to western grid of the country which has very limited hydel power production at present. The RBPH is an under ground power house stationed on the right bank of the river located about 165 meters downstream of the dam. It has six number of Francis type reversible turbine generators each of 200 MW installed capacity each operating under a head range of 116.6 m. to 75 m. The T.G. Sets are supplied by M/S Sumitomo Corporation, Japan and M/S BHEL. The units can operate at minimum reservoir water level of 110.64 meters. These six units have been commissioned in a phase manner during Feb-05 to June-06. The

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generation of energy depends upon inflow of water from upstream projects and need of water for irrigation in Gujarat. The CHPH is a surface power station in a saddle dam on right bank of the reservoir having total installed capacity of 250 MW (5 x 50 MW) with operating under head range of 46.13 m. to 18.12 m. These five units have been commissioned in a phased manner during Aug-04 to Dec-04. These units can be operated with minimum reservoir water level of EL110.18 m. The CHPH is being operated in consultation and as per advice of NCA based on irrigation requirement of Gujarat/Rajasthan and availability of water in reservoir as well as the release from upstream project of Madhya Pradesh. The energy generated from both the power houses is to be evacuated through 400 KV level through interconnecting transformers situated in RBPH switch yard. The 400 KV switchyard is indoor type having Gas Insulated Switch (GIS) Gear and Bus bars. The energy is transmitted to party states i.e. Gujarat, Maharashtra and Madhya Pradesh in the proportion of 16:27:57 through 400 KV double circuit transmission lines, namely SSP-Kasor & SSP-Asoj, SSP-Dhule and SSPNagda respectively. All the transmission lines are commissioned and charged. The operation and maintenance of SSP power complex and transmission lines is being done by Gujarat State Electricity Company Limited (GSECL). A series of micro hydel power stations are also planned on the branch canals where convenient falls are available. In all SSP would generate 5, 000 million units of electricity with target of annual power generation worth of about Rs. 400 Crores. The power generation at CHPH depends upon the releases for irrigation for Gujarat and Rajasthan and power generation at RBPH depends upon the water surplus arising after meeting the irrigation requirements of the states. The NWDT in its report considered the likely pace of irrigation development by the party states for utilization of their allocated shares and indicated three stages of irrigation development in the basin, reckoned from the date of Gazette notification of the report. Thus the power benefits from SSP will depend upon the irrigation use by the party states which is a complex situation involving multipurpose river system which has dynamic characteristics. The Central Electricity Authority together with the power beneficiary states of Madhya Pradesh, Maharashtra, and Gujarat carried out different studies for evaluation of power benefits from SSP complex using multi reservoir simulation system. All the studies were taken into account while arriving at the power benefits. The power benefits with effect of the three stages of irrigation development given in Table 4.

N.K. Bhandari

NSCCIWRS

An Overview of Sardar Sarovar Project with Special Reference to Power Component

Table 4: Power Benefits Effect of Irrigation Development Stage of Irrigation Development

Years

Stage-I 1 Stage-II 2 Stage-III 3

Firm 3635 517 0

00-10 20-30 30-45

RBPH (GWH) Seasonal 1431 1334 520

Total 5066 1851 520

Firm 213 676 440

CHPH (GWH) Seasonal 190 111 345

Total 403 787 785

1

Partial Irri. dev. in Gujarat and full in Raj. to utilize 3.74cu km out of 11.65cu km share of two States. Irri. dev. in Guj. to increase from 3.74 to 11cu km. For MP upto 15.94cu km out of 22cu km allotted. 3 Full irri. dev. in M.P.as well. 2

Table 5: Generation of Power through CHPH & RBPH or SSP (As on 30th June 2013) Year CHPH 173.515 189.858 228.073 316.874 337.040 520.889 327.548 508.550 67.836 3322.11

August ’04 to March’ 05 April’ 05 to March’ 06 April’06 to March’ 07 April’ 07 to March’ 08 April’ 08 to March’ 09 April’ 09 to March’ 10 April’ 10 to March’ 11 April’ 11 to March’ 12 April’ 12 to June’ 13 Grand Total.

Power Generation (In Million Units) RBPH 89.742 1761.924 3372.009 4118.818 1980.633 1980.438 3261.192 3850.746 857.242 24319.056

REA Benefits (In Million Units) Total 263.257 1951.782 3600.082 4435.692 2317.673` 2501.327 3588.740 4359.296 925.078 27641.166

229.812 1897.949 3175.2880 4358.077 2447.7720 2268.354 3511.499 4252.294 904.687 27074.708

Table 6: Details of Booked Expenditure up to June 2013 (As per information given by SSNNL) Unitwise total of Expenditures

Booked Expenditure

(A) Undisputed Unit-I (Dam & Appurtenant works) Unit-II (Main Canal) Unit-III (Hydropower) Group-IV (Branches & Distribution) Group-V Exp. (sharable unallocated) Group–VI (Non-sharable) Sub-Total of (A) (B) Disputed Interest on market borrowing R&R Cost Rockfill dykes & Link channel cost Sub-Total of (B) Total of Booked Expenditure (Total of (A) and (B))

2989.75 5870.80 3033.56 13813.42 941.56 -1695.47 24953.62 13486.46 2021.01 80.45 15593.92 40547.54

Total Expenditure on SSP up to June 2013 Rs 40547.54 Crore

941.56

13813.42

-1695.47 3033.56

13486.46 5870.8

86.45

2989.75

2021.01

DISPUTED EXPENDITURE Unit-I

Unit-II

Unit-III

Gr.-IV

Gr.-V

Gr.-VI

Interest

R&R

Dy & LC

Fig. 1: Details of Booked Expenditure up to June 2013 NSCCIWRS

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Table 7: Expenditure & Revenue from Power Generation (As on June 2013) Party States

% of Share

1 Madhya Pradesh Maharashtra Gujarat Total

Expenditure on Power House (Rs. In Cores) (Unit-III)

Benefit of Power (In Million Units) (REA)

3 1760.35 833.86 494.13 3088.34

4 15432..58 7310.17 4331.95 27074.70

2 57 27 16

Total Benefit (@ Rs. 2 Net Benefit of States per unit) (Rs. In crores) (Rs. In crores) (5)-(3) = (6) (4) x (Rs. 2) = (5) 5 6 3086.52 1326.17 1462.03 628.17 866.39 372.26 5414.94 2326.60

Table 8: Undisputed Payment & Share of States (As on June 2013 State Madhya Pradesh Maharashtra Gujarat Rajasthan Total

Undisputed Share Cost (Rs. in Crores) 2917.93 1382.18 20345.75 705.92 25351.78

Share Paid by the State (Rs. In Crores) 2134.53 1101.27 20345.75 677.75 24259.30

Thus, it can be observed that the firm energy from the RBPH would vary from 3635 GWH (415 MW continuous) in the initial years to NIL in the final stages of irrigation development. To preserve the capacity value of this station and its continued utility as a peaking station through out its life, pump-turbine (reversible) and generator-motor sets are installed at this station. The machines at RBPH would thus run for about 6 hours daily during the period of peak demands, discharging water into the lower reservoir, created by constructing a tail pool dam at Garudeshwar 12 Km downstream of Sardar Sarovar Dam. During the offpeak periods the water from this lower reservoir would be pumped back into the main reservoir by drawing power from the grid thus making the water available in the upper reservoir for peaking operation of the next day. The power benefits from CHPH would increase from 213 GWH (24.3 MW continuous) to 440 GWH (50.2 MW continuous) in the final stages of irrigation development.

Benefit to States by Power Generation (Rs. In Crores) 3086.52 1462.03 866.39 NIL 5414.94

Madhya Pradesh to the sea could be of great advantage. As per the plans of inland water ways, the river Narmada is one of the 10 water ways identified by Ministry of Surface Transport, Govt of India. Prefeasibility study carried was out in 1981. The Phase-I of the study for navigation in a length of 637 km of the Narmada from Hoshangabad to the sea was completed in 1985. Phase-II includes the crossing other difficult river stretches and Phase-III relates to financial and economic analysis. It is proposed to cross the 4 major dams by providing by passes. The study on Phase-II & Phase-III, under Inland Waterways Authority of India (IWAI) with Union Ministry of Shipping is carried out by WAPCOS.

ECONOMIC ASPECTS

NAVIGATION

As per the NWDT award, the cost of dam and appurtenant works (Unit-I) is shared between irrigation and power in the ratio of 43.9 and 56.1. The capital cost of power portion of SSP is shared by Madhya Pradesh, Maharashtra and Gujarat in the ratio of 57:27:16 respectively. The upstream Indira Sagar project in Madhya Pradesh being the feeder storage for the SSP, as per the award, Madhya Pradesh to take up and complete the construction of Indira Sagar dam with FRL 262.13 concurrently with or earlier than the construction of Sardar Sarovar dam for which Gujarat credits Madhya Pradesh each year 17.63% of expenditure on account of the cost of construction of Indira Sagar dam. As for the irrigation component, the cost of Sardar Sarovar dam and appurtenant works (Unit-I) is shared by Gujarat and Rajasthan in the ratio of 18:1. The cost of SSP Canal with its design approved by NCA is shared between Gujarat and Rajasthan as per the guidelines laid down by the tribunal.

One of the important uses of the storages can be the water transport. With the series of reservoirs all along the Narmada river, linking the land locked state of

It is to note that the based on the decision of NWDT Award, the project report of SSP was prepared by GOG in 1981 and submitted to Central Water

Revenue from Power Generation The table given below gives the details of expenditure incurred by different States vis-à-vis revenue earned by the States. The power generated by SSP is accounted as ratio wise generation from beneficiary States. To know the benefit/revenue earned the computations in this table are done by taking Rs. 2 per unit. The above table clearly shows that States have earned appreciable benefit in power generation. The figure shown in table No. 8 shows that other party States i.e. MP and Maharashtra has earned more benefit in power generation than to GOG for construction of SSP.

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An Overview of Sardar Sarovar Project with Special Reference to Power Component

Commission (CWC) for clearance with an estimated cost of Rs. 3333 crore at 1979-80 price level. The environmental clearance to the project was accorded by Ministry of Environment and Forest in June 1987. Investment clearance was given by the Planning Commission at an estimated cost of Rs. 6406.04 crore at 1986-87 price level in October 1988. Revised estimated cost of the project at 2008-09 price level amounting to Rs. 39240.44 crore has been approved by the Planning Commission. An amount of Rs. 40547.54crore has been booked as total project expenditure by the GOG, up to the end of October 2013.

ENVIRONMENTAL AND R&R ISSUES The total SSP reservoir submergence of about 377 sq km at FRL comprises about 112 sq km of agricultural land, 135 sq km of forests and 130 sq km of river bed and waste land. Percentage of area submerged to area irrigated is 1.65% for the SSP, compared to 4 to 5% in many projects in the country and around the world. It is estimated that the ratio of project beneficiaries to project affected persons stands as high as 100:1. In all 245 villages of the three states viz. 193 Villages of Madhya Pradesh, 33 villages of Maharashtra and 19 villages of Gujarat are affected. Total of over 40 thousand families are displaced, because of SSP. The NWDT award has detailed directions on the definition of oustees, lands to be compulsorily acquired, liability of Gujarat to pay compensation for land acquisition and rehabilitation, provisions for rehabilitation, programme for payment by Gujarat to Madhya Pradesh and Maharashtra on this account. The R & R package includes: 

Land: Equivalent to that acquired with a minimum of two Ha. and maximum of eight Ha. fully irrigated, provided 25 per cent of the landholding is lost. All major sons are entitled to land up to two ha. each.



Encroacher Oustees: They are treated as land oustees provided the encroachment is prior to 13th April, 1987. Land entitlement is between one and two Ha. depending on the size of encroachment.



Landless Oustees: to get Rs. 49, 300/-for productive assets and others without productive assets to get Rs. 33, 150/-.



House plots: Developed residential plots measuring 60'x90' in rural areas to be provided to oustees families and their major sons/unmarried major daughters.



Rehabilitation Grant: Agricultural landless laborers to get Rs. 18, 700/-and all other laborers to get Rs. 9, 350/-. All major sons to be treated as landless.



Transportation grant: Free transport to relocation site or, when not availed of, a lump sum of Rs. 5000/-relocation grant is payable.



Facilities at R&R sites: Civic amenities like drinking water, electricity, health, education, play ground, places of worship, etc. to be provided.

In December 1999, Resettlement and Rehabilitation Sub-Group of NCA was constituted with Secretary, Ministry of Social Justice and Empowerment as Chairperson to monitor the progress of land acquisition, and R&R aspects of the project affected families (PAF). The Supreme Court in its final judgment of October 2000, gave directions for completion of dam as per the award of NWDT. The Court gave directions for the immediate raising of the dam from EL 85.0 m to EL 90.0 m with direction to the NCA to give permission for further raising of the dam height on pari-pasu completion of the R&R and Environmental measures. The NCA action plan of 2001 is as per Table 9. The SSP reservoir submergence includes 135 sq km of forests. Increase in vegetal cover in 3.4 M. Ha. of Gross Command Area, gains due to compensatory forest, tree plantation 100 times and Carbon Dioxide (CO2) fixation to large extent by 70 times in the region are some of the measures taken by GOG. GOG also proposed to develop wild life sanctuaries viz. "Shoolpaneshewar wild life sanctuary" on left bank, Wild Ass Sanctuary in little Rann of Kachchh, Black Buck National Park at Velavadar, Great Indian Bustard Sanctuary in Kachchh, Nal Sarovar Bird Sanctuary and Alia Bet at the mouth of the river are some of the measures for protection of flora & fauna.

Table 9: Jan 2001 Action Plan OF NCA Dam Height (EL) 100.00m 110.00m 121.00m 138.68m# #

Scheduled Date for Completion of R&R December 2001 December 2002 December 2003 December 2004

Scheduled Date for Completion of Dam Level June 2002 June 2003 June 2004 June 2005

yet to be achieved

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Table 10: Revised Implementation Schedule of Unit-iii Milestone Dates for RBPH (RIS-June 2002) S. No. 1. 2. 3. 4.

Name of Activity Civil Works

1. 2.

Civil Works of powerhouse cavern Construction of Tail Race Channel Fabrication and erection of Penstocks Supply & erection of draft tube gates in Collection Pool and trash rack in exit tunnels. Electrical Works T.G. Set supply T.G. Set erection

3 4

Supply & erection of misc. items

5

6

400KV GIS Switchyard

400KV Transmission line (a)SSP-Dhule-I & II (Mah. border) (b)SSP-Nagda-I & II (M.P. border) Commissioning

Target Date for Completion 30.06.2005 31.05.2004 31.12.2004 31.12.2004 31.05.2002 30.09.2004 (first unit) 31.05.2006 (All units) Completed 30.05.2004 (first Unit) 31.01.2006 (All Units) Completed Completed Unit-I on 30.09.2004 Unit-II on 31.01.2005 Unit-III on 31.05.2005 Unit-IV on 30.09.2005 Unit-V on 31.01.2006 Unit-VI on 31.05.2006

LEGAL & INSTITUTIONAL ASPECTS

CONCLUSION

The NWDT award, includes the orders on sharing of waters (including surplus and distress in any year), related to FRL of Sardar Sarovar dam, sharing of costs and benefits, regulated releases by Madhya Pradesh for the requirements of SSP, payment to be made by Gujarat for regulated releases etc. The award also includes the institutional arrangements to be set up for implementation and compliance of its decisions made which are subject to review at any time after a period of 45 years from the date of publication in the official gazette. Accordingly constitution of an Inter-State Administrative Authority known as ‘Narmada Control Authority’ (NCA) and a Review Committee for the purpose of securing compliance with and implementation of the decision and directions of the Tribunal has been established. Also as per the award, the Sardar Sarovar Construction Advisory Committee (SSCAC) has been functioning for ensuring efficient, economical and timely execution of Unit-I (Dam & Appurtenant works) and Unit-III (Power Complex) of the project.

The Sardar Sarovar Project on river Narmada is a landmark case as an integrated plan of harnessing Narmada river water resources to meet the water needs of the river basin areas as well as to transfer surplus water to Saurashtra and Kachchh region which have no other dependable water source, ensuring to minimize the ecology degradation, advancement of desert and salinity ingress in the regions. Even though, one of the most widely discussed and legally implicated projects, resulting into of heavy time & cost overrun, the SSP, a technological wonder, is going to prove be the project with huge socio-economic impact, environmental sustainability and the fine example of interstate cooperation in the history of independent India.

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REFERENCES Report of the NWDT awards Various facets of Sardar Sarovar Project by shri N. K. Bhandari Secretary SSCAC published in India Water week 2012.

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Impact of Climate Change

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Impact of Climate Change on Water Resources M.L. Kansal JPSS Chair Professor (Hydropower) and Professor, Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, India

Surendra Kumar Chandniha Research Scholar, Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, India ABSTRACT India is the seventh largest country of the world with an area of about 329 Million Hectares (M.ha) and is divided into seven physiographical regions. It has both extremes of heat and cold, aridity and humidity, drought and flood. It has a wide variety of climate and weather conditions. The major causes of such climatic variation are the great Himalaya in North and ocean in the South. The Himalaya provides barricade to protect the cold wind effect from central Asia and major part of rainfall takes place due to Ocean in the South and Himalaya as a barrier to the clouds. Climatic conditions critically influence the water resources of the country as major part of it gets contributed by the South-West monsoon. Some natural activities and the anthropogenic activities result in the emission of Green House Gases (GHGs) which in turn results in climate change. In this study, the main indicators of climate change are discussed. Also, the impact of GHGs emission on global warming and on climate change is discussed. Further, the qualitative impact of climate change on water resources of India is briefly discussed.

KEYWORDS: Anthropogenic Activities, Indicators of Climate Change, GHG

INTRODUCTION Climate change is a significant and lasting change in the statistical distribution of weather patterns over periods ranging from decades to millions of years. It refers to changes in the average surface temperature increasing (global warming) or decreasing which then causes a long term change in weather. Natural events and human activities are believed to be contributing to an increase in average global temperatures. Climate change arising due to the increasing concentration of greenhouse gases in the atmosphere since the pre-industrial times has emerged as a serious global environmental issue and pose a threat and challenge to mankind (Sharma et al., 2006, Patwardhan, 2000, Goyal, 2004, Nordhaus et al., 2000). Major Green House Gases (GHGs) are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), fluorinated industrial gases such as hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6), and water vapour.

For example, Brahmaputra-Barak-Ganga System accounts for about 60% of total surface water resources. Western and Southern regions experience severe deficit in water availability. Average annual rainfall in India is about 1190 mm which varies from 100 to 12000 mm with a coefficient of variation between 15-70. Total rainfall days and hours are about 80 and 100 respectively. Nearly 80% of the annual rainfall takes place in only 3 to 4 months. Thus, water storage is required to meet the various demands in space and time. Further, the problem gets aggravated because of various anthropogenic activities which results in emission of GHGs which results in global warming and the climate change. In this study, the main indicators of climate change are discussed. Also, the impact of GHGs emission on global warming and on climate change is discussed. Further, the impact of climate change on water resources of India is also briefly discussed.

ANTHROPOGENIC ACTIVITIES AND ITS IMPACT ON CLIMATE CHANGE

Water is the prime requirement for the existence of life and has been a major concern to mankind. In fact, most of the civilizations have developed on the banks of the river, primarily because of the reason that these rivers play important role in the existence of mankind. India is a vast country with a geographical area of about 3.29 X 106 km2 which is the 7th largest in the world and has a population of about 1.27 billion. It is expected that total water requirements by 2050 will be about 1200 BCM (Food + Drinking Water + Industrial +Environmental and ecology +Navigation Energy Generation + Losses) (800+110+100+20+15+70+80 = 1195 BCM), whereas the availability of water will be between 1100-1400 BCM.

Anthropogenic activities contribute to climate change by causing changes in Earth’s atmosphere through GHGs emission, aerosols (small particles), different forms of pollution, and foreign materials. The largest known contribution comes from the burning of fossil fuels (vehicular, aero planes, thermal power plants etc.). The burning of fossil fuel releases carbon dioxide gas in the atmosphere. Greenhouse gases and aerosols affect climate by altering incoming solar radiation and outgoing infrared (thermal) radiation that is part of Earth’s energy balance. Global warming arising from the anthropogenic-driven emissions of greenhouse gases and consequent climate change have emerged in the last two decades as one of the most serious environmental issues to ever confront humanity.

Major source of water supply in India is precipitation which is highly uneven in space and time.

The GHGs accumulation has increased significantly due to industrial and urbanization. As per

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IPCC (2007), the GHGs over the past 2000 years has increased manifold as shown in Fig. 1. Main Indicators of Climate Change National Oceanic and Atmospheric Administration (NOAA) of USA identified 7 indicators (Tropospheric temperature, humidity, temperature over the ocean, sea surface temperature, sea level, ocean heat content and temperature over land) that would be expected to increase in a warming world, and 3 indicators (Glaciers, sea ice and snow cover) would be expected to decrease (Fig. 2).

Fig. 1: Variation of GHGs Concentration over Past 2000 Years (IPCC, 2007)

security and the occurrence of water related disasters, triggered by extreme events. According to the fourth assessment report of IPCC (2007), future climate change is likely to affect spatio-temporal availability of water, agriculture and increase risk of hunger, energy availability and requirements, as well as more rapid melting of snow and glaciers. Presently 10% of the earth’s landmass is covered with snow, with 84.16% of the Antarctic, 13.9% in Greenland, 0.77% in the Himalaya, 0.51% in North America, 0.37% in Africa, 0.15% in South America, 0.06% in Europe. Outside the polar region, Himalaya has the maximum concentration of glaciers. 9.04% of the Himalaya is covered with glaciers, with 30-40% additional area being covered with snow. In Ladakh, the northern most region of India, all life depends on snow. Ladakh is a high altitude desert with only 50mm of rainfall. Ladakh’s water comes from the snow melt– both the snow that falls on the land and provides the moisture for farming and pastures, as well as the snow of the glaciers that gently melts and feeds the streams that are the lifeline of the tiny settlements. India has 5243 glaciers covering an area of 37579 km2 and containing 142.88 km2 of ice. According to the Intergovernmental Panel on Climate Change (IPCC), “glaciers in the Himayalas are receding faster than in any other part of the world and if the present rate continues, the likelihood of them disappearing by the year 2035 and perhaps sooner is very high if the earth keep getting warmer at the current rate”. According to the IPCC report the total area of glaciers in the Himalaya will shrink from 1930051 square miles to 38,000 square miles by 2035.

Fig. 2: Ten Indicators for a Warming World

EFFECT OF CLIMATE CHANGE ON WATER RESOURCES Although the subject of climate change is vast, the changing pattern of hydro-climatic inputs and catchment response deserves urgent and systematic attention since it will affect water, food and energy

(a) Historical Gangotri Glacier Flow Pattern

Results have shown that Gangotri glacier, which is one of the largest in the Himalaya, has exhibited retreat up to 819 ±14 m and lost 0.41 ±0.03 km2 (~0.01 km2/yr) at its front from 1965 to 2006 as shown in Fig. 3 (a)(Bhambri et al. 2011). Glacial lakes are formed due to the melting of ice and snow from glaciers. Due to the faster rate of melting of the glaciers, possibly due to global warming, water is accumulating at an increasing rate in these lakes. Formation of Glacial lakes in the Himalaya is shown in Fig. 3(b).

(b) Glacial Lakes in the Himalaya Region

Fig. 3(a, b): Impact of Climate Change on Glaciers Source: Allen (2007) 90

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Impact of Climate Change on Water Resources

Fig. 4(a,b): Damage in Kedar Valley

Fig. 5: Impact of Climate Change on Coastal Area Source: http://www.dnrec.delaware.gov/Climate Change/Pages/ClimateChangeCoastalAreas.aspx

Sudden outburst of such lakes may be one of the major sources of damage at the downstream. For example, Kedar valley of the Himalaya has faced widespread damage due to various anthropogenic activities, cloud and lake burst, etc. as shown in Fig. 4 (a) and (b).

reserves. This would alter the frequency of droughts and floods. In addition, snow- and glacier-fed basins will experience increasing flow rates due to higher melt flow in the initial stages and it may come down as the snow/glacier cover as shown in Fig. 6.

The climate change in coastal areas may cause shoreline erosion, coastal flooding, and water pollution which may affect man-made infrastructure and coastal ecosystems. Coasts are sensitive to sea level rise, changes in the frequency and intensity of storms, increases in precipitation, and warmer ocean temperatures. In addition, rising atmospheric concentrations of carbon dioxide (CO2) are causing the oceans to absorb more of the gas and become more acidic. This rising acidity could have significant impacts on coastal and marine ecosystems. Overall, the climate change will result in a higher or lower rainfall or may cause changes in its distribution which would influence the spatio-temporal distribution of runoff, soil moisture, and groundwater NSCCIWRS

Fig. 6: Projected Change Water Cycle Due to Anthropogenic Activities and Urbanization Source: Karl et al. (2009)

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These effects can reduce the quality of water and can damage the various infrastructure. In India, some of the climate change hazards are as follows: 

Rajasthan- Drought



Rann of Kutch – Sea level rise



Mumbai-Salt water intrusion



Kerala –Productivity of Forest



Uttarakhand flood



Tamil Nadu-Coral bleaching



Ganges – Sedimentation problem

increased and attempts are being made to reduce and mitigate its impact as it is affecting the regional development. This study highlights the contribution of anthropogenic activities in GHG emission which in turn results in global warming. Global warming makes changes in the hydrological cycle and results in Spatiotemporal variation in runoff. Climate change in India is affecting the water resources due to its impact on glaciers and sea. It is expected that the change in rainfall pattern and the change in seasons will bring negative impacts on overall development of water resources and its management. Hence, there is a need for creating awareness and for taking action so that its impact can be minimized and mitigated.



Sunderbans-Sea level rise

REFERENCES



Northwest India-reduction In rice yield

It is for these reasons that the climate change and its impact on water resources is drawing the attention of researchers across the world. Major categorization of research being carried out in the area of climate change and water resources is shown in Fig. 7.

Allen J. (2007) NASA image, Earth Observatory (Land Processes Distributed Active Archive Center). Bhambri R, Bolch T, Chaujar RK, & Kulshreshtha SC (2011) Glacier changes in the Garhwal Himalaya, India, from 1968 to 2006 based on remote sensing. Journal of Glaciology. 57(203), 543–556. Goyal RK (2004) Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India). Agric Water Manage 19(1):1–11. Inter-Governmental Panel on Climate Change (IPCC) (2007) Fourth assessment Report. Karl TR, Melillo JM, Peterson TC (2009) USGCRP-Global Climate Change Impacts in the United States, Cambridge University Press, New York, NY. Nordhaus WD., and Joseph B (2000) Warning the World: Economic Models of Global Warming. Book MIT Press (MA).

Fig. 7: Research on Climate Change & Water Resources

Sharma S, Bhattacharya S, & Garg A (2006) Greenhouse gas emissions from India: A perspective. Current Science Bangalore. 90(3): 326

CONCLUSION Water resources are inextricably linked with climate. Climate change is an ongoing and complex process and is affecting the availability of fresh water on the planet. That is why, globally, the researchers are focusing their attention towards the various sectors of climate change and its impact on water resources in terms of quantity and quality. Over the past few years, the awareness has

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http://www.epa.gov/climatechange/impactsadaptation/water.html http://www.globalissues.org/article/233/climate-change-andglobal-warming-introduction http://www.dnrec.delaware.gov/ClimateChange/Pages/Climat eChangeCoastalAreas.aspx.

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Impacts of Climate Change on the Canal Network of Sardar Sarovar Project M.B. Joshi General Manager (Technical & Coordination), Sardar Sarovar Narmada Nigam Limited, Gandhinagar, Gujarat, India [email protected]

KEYWORDS: Canal Network, Climate change

INTRODUCTION Climate is changing and we have all been experiencing that. In Gujarat State, on 8th November, 2013, Daily Weather Report of the India Meteorological Department (IMD) showed that Maximum Temperature in the State deviated from Normal in the range of-2 to +2 °C, whereas Minimum Temperature deviated in the range of-2 to +4 °C. As regards rainfall, Normal date for onset of south-west monsoon is June 10 for south Gujarat, June 15 for Saurashtra, Middle and North Gujarat and July 1 for Kachchh. Experience of recent years has however shown that this has been consistently delayed by almost one fortnight to one month. Except Naliya (in Kachchh), all the weather stations in the State have registered higher rainfall than the Normal ones and the increase has been significant (30% to 200%).Normal withdrawal date is 15th September, however in the current year, even after 1st October the rainfall continued and as compared to the longterm averages, entire State received rainfall in excess of +20% or more except Dangs and Kachchhwhere rainfall was deficit and, Surendranagar, Anand, Navsari, Valsadwhere rainfall was normal. As against the normal annual rainfall value of 975 mm, Rajkot City received about 3600 mm of rainfall this year, of which 510 mm was received in just 36 hours (on September 25)! In the Impact Analysis of Climate Change, it is important to note that the projections being made under various studies, though scientific, are critically dependent on various postulates made. It is not as simple as change from one weather parameters set ‘A’ to another set ‘B’. In fact the transition from set ‘A’ to ‘B’ is also span in decades and therefore it is going to have its own impacts. It is a well-known fact that increased concentration of greenhouse gases (GHGs) in the atmosphere resulted inwarming of the global climate system by 0.74 °C between 1906 and 2005. The past 50 years have shownan increasing trend in temperature @ 0.13 °C/decade, while the rise intemperature during the past one and half decades has been much higher. The trendsof rise in temperature, heat waves, droughts and floods, and sea level shown by theIndian scientists are in line with the InterGovernmental Panel on Climate Change (IPCC) though magnitude of changes could differ. The mean NSCCIWRS

temperature in Indiais projected to increase up to 1.7 ºC in kharif (July to October) and upto 3.2 ºC duringrabi (November to March) season, while the mean rainfall is expected to increase by10% by 2070 (IARI, 2012). Even for a given case wherein total precipitation meets the normal value, its increased variability (coefficient of variance) leads to an increased water demand for agriculture sector. On the other hand it is also argued that due to increased atmospheric CO2 concentrations, water-use efficiency for some types of plants would increase, which would increase the crop yield per unit of water consumed (water productivity–‘more crop per drop’). However, in arid and semi-arid regions, the ratio may even decline as yields decrease due to heat stress. Overall the irrigation sector will be affected most strongly by climate change, as well as by changes in the effectiveness of irrigation methods. An interesting study carried out by the Indian Agricultural Research Institute (IARI) to determine the vulnerability of agricultural production to climate change, for different crops in different regions has found that increases in temperature (by about 2ºC for Wheat and 2-4 ºC for Paddy)reduced potential grain yields in most places. Regions withhigher potential productivity (such as northern India) wererelatively less impacted by climate change than areas withlower potential productivity (the reduction in yields wasmuch smaller). The study also confirmed that reductions in yields as a result of climate change are predictedto be more pronounced for rain fed crops (as opposed toirrigated crops)and under limited water supply situationsbecause there are no coping mechanisms for rainfall variability. Overall, temperature increases are predicted to reduce rice yields. In the case of Paddy, Eastern regions are predicted to be most impacted byincreased temperatures and decreased radiation, resulting inrelatively fewer grains and shorter grain filling durations. By contrast, potential reductions in yields due to increasedtemperatures in Northern India are predicted to be offset byhigher radiation, lessening the impacts of climate change. In water stressed areas changes in irrigation water use are not merely driven by the increased irrigation water demand, but also by the factors like changes in demands for higher value uses (e.g., for urban areas), future management changes, and changes in availability.the impact of climate change on optimal

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growing periods and yield-maximising irrigation water use has been modelled, assuming no change in irrigated area and climate variability (Döll, 2002; Döll et al., 2003). Applying the SRES A2 and B2 scenarios as interpreted by two climate models, these authors found that the optimal growing periods could shift in many irrigated areas. Net irrigation requirements of China and India, the countries with the largest irrigated areas worldwide, change by +2% to +15% and by-6% to +5% for the year 2020, respectively, depending on emissions scenario and climate model. Different climate models project different worldwide changes in net irrigation requirements, with estimated increases ranging from 1 to 3% by the 2020s and 2 to 7% by the 2070s. The largest global-scale increases in net irrigation requirements result from a climate scenario based on the B2 emissions scenario. An indirect but small secondary effect on water demand would be the increased electricity demand for air-conditioning and cooling, which interaliawould increase water withdrawals for power generation. A statistical analysis of water use in New York City showed that above 25°C, daily per capita water use increases by 11 litres/1°C (roughly 2% of current daily per capita use) (Protopapas et al., 2000). For the State of Gujarat, having almost 50% urban population which is further growing rapidly, inter-linkages between water and energy sectors need due consideration in analyzing impacts of climate change. It is worth mention here the important role of water quality. Increased precipitation intensity may periodically result in increased turbidity and increased nutrient and pathogen content of surface water sources. The water utility serving New York City has identified heavy precipitation events as one of its major climatechange-related concerns because such events can raise turbidity levels in some of the city’s main reservoirs up to 100 times the legal limit for source quality at the utility’s intake, requiring substantial additional treatment and monitoring costs (Miller and Yates, 2006). Holistic view of water management is needed here and it is in this context that researchers have aptly concluded that the institutions of water governance will play a large role in determining the overall social impacts of a change in water availability, as well as the distribution of gains and losses across different sectors of society. Institutional settings differ significantly both within and between countries, often resulting in substantial differences in the efficiency, equity, and flexibility of water use and infrastructure development (Wichelns et al., 2002; Easter and Renwick, 2004; Orr and Colby, 2004; Saleth and Dinar, 2004; Svendsen, 2005). As an integral part of the inter-State multipurpose SardarSarovar Project on river Narmada, a vast canal 94

network of about 75,000 km length is under implementation to annually convey 11.7 billion cubic meter (BCM) of water over distances upto 700 km. This infrastructure is being developed in a phased manner and so far a hierarchical canal network of about 22,000 km length has been completed, which includes the world’s largest lined irrigation canal, the 458 km long Narmada Main Canal. Once completed, this would facilitate assured irrigation to 1.8 million hectares of culturable command area in the state of Gujarat besides drinking water supply to 9633 villages and 131 urban centres. The project is an endeavor to address the regional imbalances in distribution of surface water resources and is vital for survival of millions of people of Gujarat. Therefore it is known as the “Lifeline”. Climate change will probably alter the desired uses of water (demands) as well as actual uses (demands in each sector that are actually met). Views of Gujja, 2003, saying “There is a profound difference between satisfying demand and managing it, especially as unmanaged demand generally continues to expand” are pertinent. Peculiar phenomenon of actual water demands overshooting what the project had promised is likely to be observed in SSP, due to an array of reasons and climate change can be an important one amongst them. In the absence of projectspecific projections on climate change, analyzing its impacts might be felt immature but peeping into them is a worthwhile exercise. The SSP Canal Network draws Narmada water from the reservoir created by SardarSarovar Dam, the terminal dam of the integrated river valley development scheme, which has a designed Live Storage capacity of 4.75 million acre feet (MAF). The reservoir is fed by the regulated releases from the upstream reservoirs on river Narmada and by the runoff generated in the intervening catchment. As per the allocation of Narmada water decided by the Narmada Water Dispute Tribunal (NWDT) under Article 262 and the Inter-State Water Dispute Act, Madhya Pradesh, Maharashtra, Gujarat and Rajasthan are allocated 18.25 MAF, 0.25 MAF, 9.0 MAF and 0.5 MAF water respectively at 75% dependability, when the total utilizable quantum in the river valley is 28 MAF. Surplus or distress if any is also to be shared in the same ration. An institutional mechanism is well in place to ensure such water sharing in a more regulated manner and therefore despite the fact that designed Live Storage capacity of SardarSarovar is just 50% of the allocated share of Gujarat and Rajasthan (to be catered from Sardarsarovar), demands can be satisfactorily met with. However, keeping in view the fact that Narmada river is 1312 km long and its catchment area uptoSardarSarovar Dam is 88,000 km2, impact of climate change in terms of spatial and temporal distribution of rainfall may be significant.If the recorded hydrological statistics are to serve as a guide to understand the variability of water

M.B. Joshi

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Impacts of Climate Change on the Canal Network of Sardar Sarovar Project

availability, out of last six years (2006-07 to 2011-12), total utilizable quantity was less than 28 MAF in 3 years and the variance recorded was 41.42 MAF (+48%) to 19.11 MAF (-32%). This definitely calls for efficient operation of the reservoirs and canal systems. Further, if we try to analyze in more details, in the monsoon 2012, total utilizable quantity of water was 37.55 MAF. However, as the present Live Storage capacity is just 1.27 MAF, from 7th August 2012 midnight onwards, Dam overflow was observed for almost 50 days, resulting into total spillage of 12.7 MAF.In 2013 again, during 18th July to 20th October, overflow was recorded for total 79 days (in four spells) and total 30.633 MAF water spilled over. Overflow in the mid of July was an unprecedented event this year, however, from the view point of better resilience to deal with consecutive drought years or consecutive spate years, it is important to ensure that the planned storages in the entire river valley are developed at the earliest and utilized optimally. After having peeped into the supply system, let us try to have a look at the demand management through the SSP canal network. 458 km long Narmada Main Canal has been operationalized since March 2006 and water is being supplied right upto Gujarat-Rajasthan border. This main canal is a conveyance canal and feeds 38 Branch Canals (secondary canals). In the State of Gujarat, out of its total annual allocated share of 9.0 MAF, 7.94 MAF is earmarked for irrigating 1.8 million hectare command, 0.86 MAF for domestic use and 0.2 MAF for industrial use. Irrigation water demands as driven by the climate change are going to play the main governing role in operation of SSP canal network. While the planning and execution of the canal network is based on the principle of ‘Equitable Distribution’, entire command area of 1.8 million hectare has been divided into 13 different agro-climatic zones. Out of these, Regions 1 & 2 (about 0.35 million hectare) have mean annual rainfall of 800 to 1000 mm. Regions 11, 12 & 13 (about 0.45 million hectare) have as low as 400 to 600 mm. All other Regions are having mean annual rainfall of 600 to 800 mm. Due to this inherent variance in climatic parameters, possibility of even out is more and SSP canal system can thus cope up better with the impacts of climate change. As regards temporal variations in rainfall pattern, canal capacity has been designed for optimized cropping patterns for various Regions,taking into consideration stipulated rainfall in each Region at 50% dependability. In this exercise, normal monsoon season is considered as June to September and rains in October are considered only in Regions 2, 3 & 4. While all the Regions are considered to have rainfall in June, climate change can have significant impact here. However, two important points are noteworthy (a) the Narmada Main Canal is designed for the peak discharge of 1133 cumecs (40,000 cusecs), but is not planned to be run at this capacity NSCCIWRS

throughout the year. Therefore in the fortnights other than the peak demand fortnight, NMC has surplus carrying capacity that can be made use of. (b) The Main Canal and other major canals having designed discharge carrying capacity more than 8.5 cumecs (300 cusecs) are decided to be operated on “Controlled Volume Concept” that will facilitate use of the canal itself as an in-line storage. NMC alone can provide 220 MCM of storage capacity at its full supply depth. In case the canal network is to be utilized for conveying surplus flood water of Narmada as a result of climate change, the carrying capacity of the SSP canal network will have to be optimally utilized with adequate arrangement of safe release of such surplus water. In SSP canal network, at appropriate locations and subject to the technical feasibility Escape structures have already been provided to release flood water into other en-route rivers. Using this mechanism, SSP canals have been used for inter-basin transfer of surplus flood water of Narmada. If climate change results in greater water scarcity relative to demand, adaptation may include technical changes that improve water-use efficiency, demand management (e.g., through metering and pricing), and institutional changes that improve the tradability of water rights. So far as water use efficiency is concerned, Main canal, Branch Canals and Distribution System (uto Sub-Minors) are considered to have 3.36%, 3.44% and 6.7% losses with respect to release at main canal head. Overall efficiency of the canal system including field application and operation losses is considered as 60%.Any improvement in this can help in easing out the pressure created due to increased water demand as a result of climate change. Canals are having lined freeboard ranging from 1.25 m (Main Canal) to 0.3 m (Minors) that offers additional discharge carrying capacity in case of extreme need, without endangering safety of the canal. In the strategies to meet with the challenges of climate change, Response time of the system would be critical keeping in mind the large distances involved, vast geographical spread and fluctuations envisaged in the demand and/or supply. SSP canals will be required to be operated on “Controlled Volume Concept” as planned for i.e. Canal Conveyance Network will have to be operated at their respective designed depths irrespective of discharge fluctuations, which will help in improving the response time (Joshi, 2001). However, the time required for initial filling of these canals and that for emptying (if and when required for maintenance) duly respecting the drawdown criteria may be enormous for larger canals. Synchronized operation of various control structures can provide much needed flexibility in operation of such a vast canal network and therefore SCADA based remote

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monitoring and control system may help tackle the climate change challenges in a better way. The World Bank report-“India’s Water Economy: Facing a Turbulent Future”–suggests to ensure that water is managed in a much more flexible, efficient and environmentally sustainable manner. Even with full awareness of the challenges involved, one should know that these are the challenges that a welfare state wedded to improve the lot of the underprivileged class of people, must face boldly and successfully (Subramanyam, 2004). As we are approaching towards the end of this ‘InternationalDecadeforAction–Water for Life’, 2005-2015, managing water has become more challenging than ever before. I think it is time to revisit the basic principles of holistic approach especiallyfor water management profoundly professed by many ancient Indian scriptures, a classical example of which is the Atharva Veda (2000-1500 B.C.).

DISCLAIMER Views expressed in this paper are individual views of the author and not necessarily represent the organization he belongs to.

REFERENCES Gujja Biksham (2003), India’s Proposed Interlinking of Rivers, Draft Concept Note for a Civil Society Consultative Dialogue IARI (2012), Climate Change Impact, Adaptation andMitigation in Agriculture: Methodology forAssessment and Application, Ed. Pathak et al. IPCC (2007), Climate Change 2007, Fourth Assessment Report. Joshi M. B. (2001), Operation of SardarSarovar Conveyance System, Water Resources Development, Vol. 17, No. 1, March 2001. Subramanyam R. G. (2004), Inter-linking of Rivers, Deccan Herald, May 11, 2004.

One should take proper managerialactiontouse and conserve the water from mountains, wells, rivers and also rainwater for use in drinking, agriculture, industries etc. AtharvaVed 19.2.1

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ENSO and its Impact on Monsoon Rainfall in Central India D.S. Arya1, A. Greeballa2 and A. Murumkar3 1,2,3

Department of Hydrology, IIT Roorkee–247667 1

[email protected] ABSTRACT

El Niño Southern Oscillation (ENSO) phenomenon is a natural part of the global climate system resulting from the interactions between large-scale ocean atmospheric circulation processes in the equatorial Pacific and Indian Oceans. It refers to a seesaw shift in surface air pressure at Darwin, Australia and the South Pacific Island of Tahiti. El Niño refers to warming ofthe eastern tropical Pacific and its sister phase La Niñarefers to cooling. There is also a neutral phase during the transition between the two phases. In India, rainfall received during the south-west monsoon season (June to September) is very significantfor its economy. Study of the linkages between monsoon rainfall variability and ENSOare important. The analysis of the effects of ENSO on monsoon rainfall in the Bhima River basin located in Central India is an attempt to search these linkages. Gridded monsoon rainfall data of 10x10resolution and ENSO indices data (SOI, MEI, Niño 3.4 and DMI) during 1951-2003 were used in this study. Analysis shows that the monsoon rainfall is positively correlated with SOI index whereas negatively correlated with all other indices. Many studies describe a significant change in the regional climate after 1976. Hence, data analysis was also performed in two different durationsi.e before and after the climate shift year. Correlation between monsoon rainfall and ENSO indices increases after the climate shift year. ENSO phase wise analysis shows that a strong/weak monsoon rainfall is associated with La Niña phase / El Niño phase except for the grids that are located on higher elevation in Western Ghats. Analysis of monsoon rainfall data of La Niña and El Niño phases explains that more rainfall is received in most of the grids during the La Niña phases which becomes higher after the climate shift year; during El Niño phase less rainfall is received which becomes lesser after the climate shift year.

KEYWORDS: ENSO, La Niña, El Niño, ENSO Indices, Monsoon Rainfall

INTRODUCTION El Nino/ Southern Oscillation phenomenon is an oceanic and atmospheric phenomenon centered in the tropical Pacific region as shown in figure 1. It is a cycle containing a phase when the eastern and east-central tropical Pacific sea surface temperature (SST) is above its normal value, and an opposite phase when those regions have temperatures below normal SST. The warm phase is known as El Niño, and the cold phase as La Niña. ENSO phases are currently predicted using a host of dynamical and statistical models. Statistical models predict on the basis of a long history of past observations, and do not use any physics. Dynamical models, by contrast, predict using the equations of atmospheric and oceanic motion.

The Southern Oscillation is characterized by an inter-annual seesaw in tropical sea level pressure between the western and eastern Pacific, consisting of a weakening and strengthening of the easterly trade winds over the tropical Pacific. Bjerknes (1969) recognized that there is a close connection between El Nino and the Southern Oscillation (ENSO) and they are two different aspects of the same phenomenon. During an El Niño or La Niña, the changes in Pacific Ocean temperatures affect the patterns of tropical rainfall from Indonesia to the west coast of South America, a distance covering approximately one-half way around the world. These changes in tropical rainfall affect weather patterns throughout the world.El Niño and La Niña occurrences are commonly known to be linked with extreme climate around the globe (Kane 1997, 1999;Kiladis and Diaz, 1989; Ropelewski and Halpert, 1987, 1996; Misir et. AL. 2013), and also impacts on mean annual flows (Dettinger and Diaz, 2000) and on peak flows (Ward et al., 2010).

ENSO INDICES

Fig. 1: ENSO Region in Pacific Ocean (http://Biophysics.sbg.ac.at/Atmo/Elnino.htm) NSCCIWRS

It is essential to have at least two ENSO indices, and perhaps more to approximately describe the character of ENSO events. In reality, ENSO simulation is a crucial task in the climate system and a primary measure of success has been the magnitude of sea surface temperature (SST) anomalies in the Niño 3.4region (Latif, 2001). Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI), Niño 3.4, the Thermal Anomaly Index (τ:Tau,. also known as JMA SSTA: Japan Meteorological Agency Sea Surface Temperature D.S. Arya

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Anomaly) and Dipole Mode Index (DMI) are ENSO indices used in ENSO impact studies. SOI is the standardized anomaly of monthly Mean Sea Level Pressure (MSLP) differences, measured at Papeete, Tahiti (149.6°W, 17.5°S) and Darwin, Australia (139.9°E, 12.4°S). MEI is based on six variables; sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and the total cloudiness fraction of the sky (C) observed over the tropical Pacific. Niño3.4 (N3.4) is a five months running average of SST (Sea Surface Temperature) anomalies over the 1200W to1700W and 50S to 50N region in the Pacific ocean. The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. τ is the sea surface temperature (SST) over the 90˚to 150˚ W and 4˚N at 4˚S geographical delimited region in the Pacific Ocean. τ is the 5 month running mean of SST anomalies over the aforementioned region. An ENSO year covers the period from 1 October to 30 September. An El Niño year occurs when 0.5 ≤ τ 8000 villages.

As per 25 year- perspective plan of the Planning Commission, 88.5 MH is to be developed under watershed program by the end of 13th Five Year Plan. Out of the above, 22.2 MH has already been developed during 9th and 10th Plan through a watershed approach. Out of the remaining rain fed area of 66.3 MH, it is proposed to develop 36.6 MH during the 11th Plan through integrated watershed approach. The rest of the area i.e. 29.7 MH would be considered for development in the subsequent two five year plans. During the first year of 12th Five Year Plan, the Department is expected to complete all the remaining ongoing projects of DDP, DPAP and IWDP which are approximately 3,250 in number. Budgetary requirement for ccomplishing this task is Rs 288 crore. The 12th Five Year Plan will also

A.M. Shekh

NSCCIWRS

Application of Watershed based R&D Voyage in India with a Sketch from Gujarat

oversee completion of watershed projects on an area of 22.65 mHa sanctioned during the last three years of 11th Five Year Plan, assuming an average project period of five years. The budgetary requirement for this will be Rs 21,350 crore. The Department proposes to cover an area of 25 mHa during the twelfth Five Year Plan @ 5 mHa per year. As the project period on an average is five years, these projects will be at different stages of completion during the Five Year Plan. The

budgetary requirement for this will be Rs 14,722 crore. As the proposed 12th Five Year Plan also includes taking up of a stream of pilot projects for special areas, an amount of Rs 100 crore has been included in the estimate. Based upon these expectations the anticipated size of 12th Five Year Plan works out to be approximately Rs 36460 crore at the current rates (Table 2).

Table 2: Projected Watershed Development Programmes During Future Five Year Plans Five Year Plans

X Plan (2002-07) XI Plan (2007-12) XII Plan (2012-17) XIII Plan (2017-22) Total

Area Coverage Envisaged (Mha) 15.0 20.0 25.0 28.5 88.5

Cost of Development (Rs./ha) 5000-7000 6000-8000 7500-9500 9000-11000

CONCLUSION India has shown excellent efforts for wider applications of watershed concept at varied scale of time and space. Starting from conceptualization to real applications at grass root level, the impacts are of high worth, providing a scientific direction for judicious utilization of scarce natural resources with their integrated management. value Positive indicators (owing to watershed based approach) on catchments under specific land uses viz. ravenous/ degraded, grasslands, rangelands, forest lands, agricultural lands were vastly realized and accordingly paved the path for flourishing of watershed based R&D and other modified programs and strategic planning at local, regional and national level. Findings from Indian watersheds are being well recognised & replicated across the globe by many developed and developing nations. Even the prevailing burning issues like climate change are too interlinked with this important concept to deal with futuristic challenges even at micro scale. In India watershedbased development has been the strategy for growth and sustainability of agriculture in the vast semi-arid and dry sub-humid regions popularly called rain-fed regions. Watershed Development Projects have been undertaken to enhance agricultural production, conserve natural resources base and ensure rural livelihood since 1980s. Initially soil and water conservation was the primary objective of the program which attracted large public investments in the last 25 years. Large investments have been assigned for watershed based development in the National 5-Year Plans since 1990s and more have been earmarked till 2025. Looking into insights of various case studies presented herein still remains a lot of scope to refine and tune the conceptual applications overcoming a few misconceptions which still exists in field at micro scale.

REFERENCES Assessment of Watershed Development Programs in Gujarat (2001) In: Policy and development Initiatives; Submitted to Planning Commission, Govt of India. NSCCIWRS

Total Cost Average (Rs. Crores) 9000 14000 21250 28500 72750

Cost Sharing Ratio* 50:25:25 40:30:30 30:30:40 25:25:50

Cost Sharing (Rs Crores) By Centre States People 4500 2250 2250 5600 4200 4200 6375 6375 8500 7125 7125 14250 23650 19950 29200

Final Report of Minor Irrigation and Watershed Management for the 12th Five Year Plan (2012-17) ; Planning Commission, Govt of India, New Delhi Gaur, M.L. Gaur and Panjab Singh (1994) “Application for Watershed Concept in Developing Highly Degraded Waste Lands of Bundelkhand Region” In Agro Forestry Systems for Degraded Lands- Vol-2, pp 938-946. Published by Oxford & IBH Publishing Co Pvt Ltd ISBN 81-204-0952-3. Gaur, M.L. (2005). Watershed Development Status of Madhya Pradesh State. Detailed report submitted in accordance to suggestions in SRC-2005 from CSWCRTI, RC, Vasad, 37 pp. Gaur, M.L. (2005a) “Status of Water Resources and Watershed Based Conservation Efforts in Madhya Pradeh: An Overview”. In: Resource Conserving Technologies for Social Upliftment (Eds: V.N. Sharda, K.S. Daddhwal, S.K. Dhyani, G.P. Juyal, P. Dogra,A. Raizada, and O. P. S. Khola, Pub. Indian Association of Soil & Water Conservationists, Dehradun, pp. 159-165. Gaur, M.L. (2006), “Watershed Development Status in Gujarat-Updated Status Report as per IRC-2006 recommendation”, CSWCRTI (ICAR), Research Center, Vasad Gujarat. Gaur M.L. (2006a), “Watershed Development Status in Madhya Pradesh-Updated status report as per IRC-2006 recommendation”, CSWCRTI (ICAR), Research Center, Vasad, Gujarat. Patel, Tapan and Tanvi K Patel, (2011) “: Convergence of Watershed Development Programme & MGNREGA to ensure Drinking Water Security Where do we stand? “ Research Report; Development Support Centre, Ahmedabad, India. Samra, J.S. (1997) “Status of Research on Watershed Management”. Paper presented at the 173rd Meeting of General Body of ICAR, New Delhi, CSWCRTI, Dehradun. Shah, Anil C. (2001), ‘Policy Changes That Didn’t Work’Inclusion of Forestland in Watershed, 2001, Development Support Centre, Ahmedabad, India. Shah, Anil C (1999) “In the Hands of the People–Indian Case Study of Watershed Development”. Policies That Work. International Institute of Environment & Development, London, U.K.

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Natural Water Springs of Mid-Himalayas: A Watershed Recharge and Release System in Need of Management Neeraj Kumar Bhatnagar [email protected], [email protected]

R.K. Nema National Institute of Hydrology, Roorkee

ABSTRACT Natural water springs receive the input from the rainfall in middle Himalayas where the spring flows are directly affected of many hydrometeorological and other factors, some of these are precipitation and global warming as well as regional warming. Study of natural spring flow is of importance and is back bone of all agricultural, social and financial activities of the habitat settled in the mountainous regions of middle Himalayas. It was found that the discharge of springs of Chandrabhaga and Danda watersheds are very sensitive and responds to precipitation and to the watershed characteristics like impact of anthropogenic activities. Spring to spring water transfer has been analysed considering the spring wise status and demand of water. Gravity flow will be used for inter-spring water transfer.

KEYWORDS: Hydrometeorological Data, Spring Rainfall Regression.

INTRODUCTION Two micro watersheds were instrumented and data were collected to study the springs and their flows in Chadrabhaga and Danda watersheds of Mid Himalayas of Garhwal Uttarakhand. Indian Himalayan watersheds are rich in natural resources and provide various resources for the existence of life in mountains, especially in Mid Himalayas. The elixir of life water is also made available to millions of downstream people through its perennial river system. The importance of mountains springs can be considered very high as these are the main water resources for drinking and other household purposes for the habitat of this region. At the intersection points of sloping ground and impermeable strata with ground water table, these natural spring sprouts. Mostly, unconfined aquifers are the water sources for the springs where the water flow comes out under the gravity. Behavior of a spring can only be administrated and forecasted by studying their temporal discharge variation or commonly known as the spring hydrograph (Vashishth and Sharma, 2007). The yield of spring flow during rainy and non rainy seasons is affected by rainfall and recharge area characteristics and the recession of seasonal springs was much more rapid than the perennial springs (Negi and Joshi, 1996; Negi and Joshi, 2004). Negi and Joshi (2004) quoted that the highest water producing rate about 405×103 l/d is obtained from fluvial originating springs whereas the colluviums originating springs are at the lowest rate and producing 7.2×s been 103 l/d. 206

The response of karst springs to climate change and anthropogenic activities was reported by Yonghong Hao et al. (2009) for the Niangziguan Springs, China and found that discharge has been declining since the 1950s. The response of the springs to climatic change and anthropogenic influence were attempted using a model-based discrimination between phases in the stream discharge record. Results show that the contribution of climate change to depletion of Niangziguan Springs is 2.30 m3/s and the contribution of anthropogenic activities ranges from 1.89 to 2.90 m3/s. It was indicated by Yonghong Hao et al. (2006) that the karst aquifers at the Liulin Springs respond remarkably to climate changes, in particular to changes in precipitation input. Probability of drought, flash floods, enhanced evaporation and other hydro-meteorological parameters are increased in a changed scenario conditions and more intense rainfall are the resultant effect of warming causes increase in the energy of the climatic system (Shrestha et al., 1999, Jianchu et al., 2007). The natural spring-flow trends in Mid Himalayas are a very interesting hydrological phenomenon and these are governed by the various hydro-meteorological parameters and one of these is climatic change. The behaviour of spring flow is very much affected from climate variability that should seriously be taken on the scale of high priority across all regions and socioeconomic sectors of the globe as the phenomenon of climate change is happening all over and can very well be felt and observed. Evidences indicate that the

Neeraj Bhatnagar

NSCCIWRS

Natural Water Springs of Mid-Himalayas: A Watershed Recharge and Release System in Need of Management

impacts of climate change are already creeping in and will worsen in the future. Global climate change requires, often, very local action and thus local information on future changes which is not available very frequently. For example the increased demand of irrigation water in a warmer and potentially dryer future climate (Bavay M., et al, 2013). This may generate conflicts of interest with other water uses such as power generation or may cause severe ecological consequences. Human-induced emissions of heattrapping greenhouse gases (IPCC 2007) are one of the major causes of the atmospheric warming that has occurred over the last 50 years. Therefore, Studies on climatic trends have to rely on records from south of the Himalaya stations or from outside the Tibetan Plateau.

METHODS Domain Garhwal Himalayas are drained almost entirely by Ganga and its tributaries, the various drainage systems are: (a) Alaknanda (b) Ganga (c) Bhagirathi and (d) Yamuna. Water is the most abundant and at the same time the least utilized and managed resource of the region. Steep gradient with rapid runoff is the major problematic factor to a productive utilization of water resource. Sub-surface flow from spring has been a traditional source of drinking water in the region. However, people have to walk down distances to collect water because of the growing scarcity of ground water recharge and drying up of the springs.

temperate, subtropical, pine and subalpine forests. Figure 1 shows the location of selected watersheds. Chandrabhaga watershed is located geographically between latitude N300 18’ and N300 19’ and longitude E780 35’ and 780 36’. The altitude in this watershed ranges from 1150 m to 2350 m above MSL. It is a subhumid region with the average annual rainfall of 1200 mm in this region. The total area of watershed is 434 ha. The area falls under the Jakhnidhar block of TehriGarhwal district (Uttarakhand), and consist of ten villages viz. Anjanisain, Badera, Bhainsoli Malli, Bhainsoli Talli, Bhutwara, Dapoli, Kaintholi, Kelan, Migwali and Saima. Chandrabhaga watersheds have many springs. Danda watershed is located geographically between latitude N30° 14' and N30° 16' and longitude E 78° 37' and E 78° 39'. The altitude in this watershed ranges from 780 m to 1700 m above MSL. It is a sub-humid region with the average annual rainfall in this region of the order of 900 mm. The total area of watershed is 442 ha. The study area, locally known as Khas Patti and is located in the Hindolakhal Block (Devprayag Tehsil) of Tehri-Garhwal district.

The springs of freshwater of the Study areas watersheds are gravitational fracture springs, produced mainly due to two fractures (a) presence of fractures in the rock and (b) due to the over cropping of water table at the surface. The main water source in the area is the springs. Springs dries up with early summer as the soil which has water retaining capacity are being degraded due to deforestation and thinning of forest cover and or due to rainfall pattern with increasing high intensity storms with longer dry spells. Chandrabhaga and Danda watersheds are the study areas in ‘Western Himalaya’ agro-ecological region of the sub-humid ecosystem at elevation of 720 m to 2350 m. Climate in this region is warm with air temperature variations from 3oC to 35oC, sub-humid to humid and per-humid with average annual rainfall 900 to 1200 mm. Soils are shallow and medium, loamy, brown forest and podzolic soils with low and medium available water content; and deep loamy Tarai soils with high available water content. Water deficit is generally low (300-500 mm), and length of growing period of crops is 180-210 days. Type of forest is moist,

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were considered but due to unavailability of flow in few springs, the measurements were continued with only 18 numbers of springs. The location map for the springs and tanks of Chandrabhaga and Danda is given in Figure 1. Hydrometeorological Data The domain has been analyzed with hydrometeorological data and the following variables are necessary to be considered.

208

Wind velocity.



Precipitation.



Streamflow.

Spring Flow Analysis For both the watersheds Chandrabhaga and Danda the daily recorded discharge of a particular highest yielding spring is shown in figure 2 with the mean rainfall of the watershed. The spring response to monsoon rainfall can easily be identified. This response slowly goes down during non-monsoon period. The immediate response of monsoon and its slow decay during periods of nonmonsoon indicates that the recharge area of spring is in close vicinity of spring with in the watershed and is due to only rainfall. From the daily spring record of a period of two to eleven years, it can be seen that all the springs in the watershed flows throughout the year. Maximum, minimum and average monthly flow information was extracted individually for all springs from the daily flow data to have behaviour of the spring’s flow of the watershed. The information on individual stream is used in section while considering the drinking water availability. The recorded data summary for the springs of Chandrabhaga and Danda watersheds along with maximum, minimum, average flow and water availability is reported in table 1&2.

Neeraj Bhatnagar

0

4

100 Rainfall, mm

Chandrabhga

spring 3 discharge, l/s

1 - J a n -1 1

1 - J a n -1 0

1 - J a n -0 9

1 - J a n -0 8

1 - J a n -0 7

Time, days

1 - J a n -0 6

300 1 - J a n -0 5

0 1 - J a n -0 4

200

1 - J a n -0 3

2

R a in f a ll, m m

6

1 - J a n -0 2

Total fifty numbers of springs were under observation for both the watersheds and daily spring flow discharge was recorded. As for as possible all the available springs in the watersheds were considered for observation leaving one or two that falls under deep forest and are not easy to reach. For Chandrabhaga watershed, a total number of 21 springs were measured. Initially for Danda watershed a 29 numbers of springs



1 - J a n -0 1

An integrated approach of hydrological instrumentation, investigation, remote sensing and GIS, a database of spatial and non-spatial in two watersheds was adopted and automatic recording units were installed and used to measure hydrological variables.. Appropriate modelling was done to simulate behaviours of the springs of watersheds. The rainfall measurements were based on four to five automatic rain-gauges installed in the watershed. The spring flow measurements were manually recorded by measuring the time taken for a specific amount of water coming out of the spring. During high flow season in monsoon months, the quantity of water measured was 5.0 litres and in non-monsoon months the amount was reduced to 1.0 litre. Since, the springs are throughout distributed in watersheds, the measurements were taken on alternate days or maximum in three days interval in both watersheds of study area.

Relative humidity.

1 - J a n -0 0

The lithology of study area falls under the rocks of Chandpur formation (Lesser Himalaya). The rocks of Chandpur formation are named as "the olive green and grey phyllite interbedded and finely inter-banded with meta-silt stone and a very fine grained wackes with local metavolcanics" (Valdiya (1980).



D is c h a r g e , l/ s

Danda watershed consists of eleven villages, namely Danda, Centauli, Mayali, Rumdhar, Gajeli, Unnana, Limgad, Tayari, BurkotHingolia, and part of Dugyar. The area is known for scarcity of drinking water.

Air temperature.

1 - J a n -9 9

Fig. 1: Location Maps for Chandrabhaga (53J11/SW) and Danda (53J/12) Indicating Location of Springs and Tanks in Watersheds



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Natural Water Springs of Mid-Himalayas: A Watershed Recharge and Release System in Need of Management

6

response as this period is receiving the winter rain. Further the period February to May is dry and the spring response is again non-linear. The similar behaviour is observed with the springs of Danda.

0

200

Rainfall, mm

Danda Watershed

Spring-2, discharge, l/s

1 -J a n -1 1

1 -J a n -1 0

1 -J a n -0 9

1 -J a n -0 8

1 -J a n -0 7

1 -J a n -0 6

1 -J a n -0 5

1 -J a n -0 4

1 -J a n -0 3

1 -J a n -0 2

600 1 -J a n -0 1

0 1 -J a n -0 0

400

1 -J a n -9 9

2

R a in f a ll, m m

D is c h a r g e , l/ s

4

From the study of spring flow behaviour, the whole period of year could also be divided in to two distinct groups viz. (i) June to September and (ii) September to May. Regression equations could be developed for above two periods separately and further improved relationship between the cumulative rainfall and cumulative spring flow can be achieved. Spring flow (Cumulative) on water year basis (Chandrabhaga Watershed)

250

Time, days

Fig. 2: Daily Discharge Hydrograph for Spring Number Three of Chandrabhaga and of Spring Number Two of Danda Watershed

The average annual volume of water of springs has been estimated from the daily recorded values and is 21.29 mm for Chandrabhaga and 37.93 mm from Danda watersheds. For a comparative performance the volume of water measured at out let V-notch and average rainfall is 189.39 mm and 1067 mm respectively for Chandrabhaga and 278.4 and 742 mm respectively for Danda watershed.

C u m u la t iv e s p r in g f lo w ( W a t e r y e a r ) , l/ s

spring 5 spring 6

200

spring 7

150

spring 8 spring 9

100 50 0 0

200

400

600

800

1000

1200

Cumulative rainfall (Water year), mm

Spring Rainfall Regression Analysis

The cumulative spring flow and rainfall for five selected springs of Chandrabhaga and Danda watersheds are presented in figure 3 & 4. It can be seen that for all cases, the cumulative spring flow behaviour showed an accumulated power response with accumulative rainfall for the period June to September. A non-linear response from September to January, possibly because this period is non-monsoon period and the rainfall in this period is too less. Again for the period January to February the spring showed a linear NSCCIWRS

Fig. 3: Relationship between Cumulative Rainfall and Cumulative Spring Flow for the Springs (5, 6, 7, 8, 9) of Chandrabhaga Watershed Spring flow (cummulative) on waer year basis (Danda Watershed) 250

C u m m u la t iv e s p r in g f lo w ( W a t e r y e a r ), l/ s

In order to understand the behaviour and response, the daily data has been summed up for the monthly and monthly mean has been estimated based on two to eleven years data. The rainfall-spring flow relationships are developed for Chandrabhaga and Danda springs. It was observed that correlation coefficient was rarely above 0.64 for cases of monsoon, non-monsoon and yearly for the springs of watersheds. In order to account for the delayed response/non-linear behaviour of the watershed between two variables the relationships are developed on water year basis by considering cumulative mean monthly rainfall (Ra, mm) and respective cumulative stream flow values (Sp, l/s) starting from June to May. The correlation so obtained was too high and the value of r2 for these cases was never below 0.90.

Spring 5

200

Spring 6

150

Spring 7 Spring 8

100

Spring 9

50 0 0

200

400 Cummulative rainfall (Water year), mm

600

800

Fig. 4: Relationship between Cumulative Rainfall and Cumulative Spring Flow for the Springs (5, 6, 7, 8, 9) of Danda Watershed

Spring-Rainfall Lag Characteristics Considering daily data and converted monthly data the spring-rainfall lag characteristics have been identified. Different springs of Chandrabhaga and Danda indicated a lag of 1 to 30 days with increase in correlation 0.01 to

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maximum 0.31 and 0 to 2 months with increase in correlation 0.01to maximum 0.64 (table 4 & 5). A higher value of spring rainfall correlation in days or month indicates the instability of spring and being more effected by the rainfall. Increase in spring–rainfall correlation and lag for different spring has been related for both the watersheds (figure 5). It can be seen that the ratio of minimum to maximum correlation decreases with increase in time lag in days. The same holds good for both the watersheds. The developed behaviour supports that an increase in time lag decreases the correlation and it could be an indicator for identifying any improvement of springs of watershed. It can be seen that the springs could be classified in to three groups viz. (1) springs with continuous flow, (2) springs with interrupted flow and (3) spring gone dead/ frequent dead. The springs under category dead are of the type as frequently dead or permanently dead and are only those that faced construction and development and/or road widening and cutting. Naturally the springs have not gone dead but the springs with continuous flow and with interrupted flow are approximately equal in numbers. It indicates that watershed is deteriorating. In order to restore or preserve the natural springs, there is a need to increase the Springs-rainfall lag.

S p r in g - R a i n f a l l la g c o r r e la t i o n r a t i o (M a x ./ M in . )

Plan for water availability plan for household purposes can only be based on reliable sources of water. In the study area watersheds springs are the only dependable source of waer. All other sources are unreliable and uncertain. Thus, spring water is considered for determining the adequacy of water availability to the people of the watershed, especially for their domestic requirements. In order to fulfill the objective, a detailed survey of the watershed was carried out. Spring wise dependable population survey based on its location was identified through Geographical Positioning Survey (GPS). 1.00

Springs Chandrabhaga Expon. (Springs Chandrabhaga)

Springs Danda Expon. (Springs Danda)

The required domestic water demand in Chandrabhaga watershed was estimated to be 31526 l/d compared to actual use as 24659 l/d which shows that he actual water use is slightly less but close to required domestic water demand. The minimum monthly spring’s flow availability through all springs in use is 8316 l/d and that is 1/3 of actual water demand as 24659 l/d. It suggests that the springs under minimum flow condition is unable to meet required demand. The similar trend is observed individually almost for all the springs. The average monthly spring’s flow availability is 267315 l/d and is around ten times of minimum monthly spring water available through springs. The required domestic water use demand of Danda watershed is 47980 l/d compared to actual domestic use as 15423 l/d around 1/3 of required domestic demand. A one third use of the required demand simply signifies the standard living conditions of the habitation and difficulty in availing water. For every spring the similar trend of water consumption was observed. The minimum monthly spring’s flow availability through all springs in use is 17246 l/d and that is slightly more than the actual water use as 15423 l/d. It indicates that water availability even under lowest condition of flow is meeting the demand. The average monthly spring’s flow availability is 244386 l/d and is around ten times of minimum monthly spring water availability through springs. CONCLUSION Monitoring of rainfall was carried out at multiple sites in each watershed using automated rain gauges. Average annual rainfall is slightly less than 1200 mm in Chandrabhaga and 900 mm in Danda, and about 70% rainfall is received during monsoon period.

0.80 0.60 0.40 0.20 0.00 0

y(Chand) = 0.8764e-0.0232x

y(Danda) = 0.7314e-0.0432x

R2 = 0.4985

R = 0.5132

5

10

Danda watershed receiving comparatively less rainfall and is yielding more to spring flow as well as to runoff.

2

15

20

25

30

35

Time lag, days

Fig. 5: Relationship between Spring–rainfall Lag Correlation Ratio and Spring-rainfall Time Lag for Watersheds

The domestic water requirement was estimated throughtaking in account the population of human and 210

animal and multiplying by standard norms of water use further called as required domestic water demand. The actual water use information was taken from each family survey called actual domestic water use. The information regarding use of water for different other purposes was also taken from each family and was added to actual domestic water use. The individual family information was grouped spring wise and the actual domestic water use was estimated on each spring.

Springs are the principal source of water for domestic uses and for down slope irrigation. A power relationship exists between cumulative rainfall and cumulative spring flow. The spring-rainfall lag characteristics were studied in order to understand the behaviour response of the springs of the area. The spring-rainfall lag

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Natural Water Springs of Mid-Himalayas: A Watershed Recharge and Release System in Need of Management

characteristics have been identified considering daily data and to converted monthly data. Different springs of Chandrabhaga and Danda indicated a lag of 1 to 30 days with increase in correlation up to maximum 0.31 and for monthly lag analysis; it is 0 to 2 months with increase in correlation up to maximum 0.64.It was observed that the ratio of minimum to maximum correlation decreases with increase in time lag in days. The same holds good for both the watersheds. The required domestic water demand for Chandrabhaga watershed for existing population is 31526 l/d compared to actual water use as 24659 l/d. The actual water use is slightly less but close to required domestic demand. The required domestic water demand of Danda watershed for existing population is 47980 l/d compared to actual domestic water use as 15423 l/d around 1/3 of required demand. Water storage structures are essentially required to store spring flow of the non use periods for domestic use during non-monsoon months. Planning is required especially for proper storage of spring water and management and if required the transfer of water in collaboration with the existing social laws, from “excess” areas to “shortage” areas, through gravity flow or by pumping. Water harvesting structures are required to store rainwater in monsoon months in order to increase the flow in required selected springs. Efficient drip irrigation method is highly recommended with horticulture crops as to maximize the use of limited spring water potential.

(Dec. 2010). The National Institute of Hydrology (NIH) operated the project.

REFERENCES Bavay M, Grunewald T and Lehning M., 2013. Response of snow cover and runoff to climate change in high alpine catchments of Eastern Switzerland., Advances in water resources 55, pp 4-16. Dyurgerov, M. (2003). Mountain and sub polar glaciers show an increase in sensitivity to climate warming and intensification of the water cycle. Journal of Hydrology, 282, pp 164-176. IPCC. (2007). Climate change (2007). Mitigation of climate change. Cambridge University Press, Cambridge. Jianchu X., Shrestha, A, Vaidya R., Eriksson, M. and Hewitt K. (2007). The melting Himalayas, ICIMOD Technical paper, ICIMOD, Nepal. Negi G.C.S. and Joshi V. (1996), Geohydrology of springs in a mountain watershed: The need for problem solving research. Current Science, 71(10), pp 772–776. Shrestha, A.B., Wake, C.P., Mayewski, P.A. & D.B.J.E. (1999). Maximum Temperature trends in the Himalaya and its vicinity: An analysis based on temperature records from Nepal from period 1971-94. Journal of Climate, 12, pp. 27752787. Vashishth, A.K. and Sharma H.C. (2007), Study on hydrological behaviour of a natural spring,Current Science, 93(6).

AKNOWLEDGEMENT

Yonghong Hao, Tian-Chyi J. Yeh, Zongqiang Gao, Yanrong Wang, and Ying Zhao (2006). A gray system model for studying the response to climatic change: The Liulin karst springs, China, Journal of Hydrology (2006) 328, 668– 676.

Authors acknowledge the data and study results of the Department of Science & Technology (DST), Govt. of India project ES/11/741/2003 extended up to Dec. 2010 entitled ‘Integrated Hydrological Study for Sustainable Development of two Hilly Watersheds in Uttaranchal

Yonghong Hao, Yajie Wang, Yuen Zhu, Yi Lin, Jet-Chau Wen, and Tian-Chyi J. Yeh (2009), Response of karst springs to climate change and anthropogenic activities: the Niangziguan Springs, China, http://ppg.sagepub.com/content/33/5/634.abstract

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Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds B. Krishna Rao Central Soil and Water Conservation Research and Training Institute, Research Centre, Vasad–388306, Anand, Gujarat [email protected]

R.S. Kurothe Central Soil and Water Conservation Research and Training Institute, Research Centre, Vasad–388306, Anand, Gujarat

Gopal Kumar Central Soil and Water Conservation Research and Training Institute, Research Centre, Vasad–388306, Anand, Gujarat

V.C. Pande Central Soil and Water Conservation Research and Training Institute, Research Centre, Vasad–388306, Anand, Gujarat

P.K. Mishra Central Soil and Water Conservation Research and Training Institute, 218 Kaulagarh Road, Dehradun–248195, Uttarakhand

ABSTRACT An investigation was carried to determine the effect of climate change on design rainfall and its effect on design and costing of soil and water conservation structures in watersheds. For this investigation, the micro watershed located at Central Soil & Water Conservation Research & Training Institute, Research Centre, Research farm, Vasad was selected and rainfall data from 1957-2012 was used. The analysis showed that as a result of climate change, there is significant increase in number of extremely heavy rainfall days as well as rainfall amount. The increase in design rainfall of various soil and water conservation structures has increased by 11, 30, and 38% for design of staggered contour trenches, contour bunds and check dams respectively. The cost of for construction of staggered contour trenches, contour bunds and check dams in watersheds have been increased by 26, 28, and 12% respectively. This investigation reveal that, there is a need to account for design & costing of soil and water conservation structures in the light of the climate change and might need a relook in all the watershed programmes of the Government of India.

KEYWORDS: Climate Change, One Day Maximum Rainfall, Soil & Water Conservation Structures, Design, Costing

INTRODUCTION The rainfall received in an area is an important factor in determining the amount of water available to meet various demands, such as agricultural, industrial, domestic water supply and for hydroelectric power generation. The global climatic data analysis clearly confirms a change in the climate (IPCC, 2007). In India, too, the effect of climate change over rainfall, rainy days and water resources has been studied, which bears testimony to changes in these parameters over a longterm basis (Goswami, et al., 2006; Guhathakurta, et al., 2008; Pal & Al-Tabbaa, 2009). Global climate changes may influence long-term rainfall patterns impacting the availability of water, along with the danger of increasing occurrences of droughts and floods. The 212

frequency and magnitude of extreme meteorological or hydrological events such as floods and droughts can be influenced by global climate change. Studies by various authors (Khan et al., 2000; Shrestha et al., 2000; Mirza, et al., 2002; Lal, et al., 2003; Goswmai et al., 2006, Dash et al., 2007) show that, in general, the frequency of more intense rainfall events in many parts of Asia has increased whereas the number of rainy days and total annual precipitation has decreased. Naresh Kumar et al. (2011) reported the on impact of climate change, the Irrigated maize, wheat and mustard in the northeastern (NE) and coastal regions, and rice, sorghum and maize in the Western Ghats (WG), may lose. Impacts of climate change and climate variability on the water resources are likely to affect irrigated agriculture, installed power capacity, environmental flows in the dry season and higher flows during the wet season, thereby causing severe droughts and floods in urban and rural areas (Gosain et al., 2011).

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Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds

The watershed management programme (WMP) is aimed at managing the precipitation (rainfall) in such a manner that it reduces runoff; controls flood and helps in water harvesting (surface or subsurface) so as to be used during lean period for successfully raising the crops, and for other multiple uses (aquaculture or live stock or both) (Thripathi & Sharda, 2011). It also maintains soil fertility, does not accelerate soil loss and provides livelihood support to the farmers. The watershed based rural development management programmes are designed for retention and detention of excess rainfall through engineering measures namely bunding, trenching, terracing, water harvesting structures and gully plugging and through biological measures namely adoption of agronomical practices such as contour cultivation; mulching; deep tillage; no or zero tillage; mixed, relay, alley and inter cropping; use of organic manure; agro-forestry; agri-horticulture, silvi-pasture; horti-pasture; social forestry and planting of suitable vegetation on degraded lands. All these activities help in flood prevention, drought mitigation and carbon sequestration, which are relevant issues under mitigation options needed in a climate change scenario (Thripathi & Sharda, 2011). Watershed management is the rational utilization of land and water resources for optimum production with minimum hazard to natural resources. It essentially relates to soil and water conservation in the watershed which means proper land use, protecting land against all forms of deterioration, building and maintaining soil fertility, proper management of local water for drainage, flood protection and sediment reduction and increasing productivity from all land uses. In India, the watershed management programmes implemented by various organizations, departments have revealed the positive results related to natural resources namely water, soil and vegetation (Ninan and Lakshmikanthamma, 1994, Samra, 1997, Dhyani et al., 1997, Kerr et al., 2000, Kumar et al., 2004). It helps in mitigating the below (drought) and above (excess) normal rainfall conditions; reduces the runoff, soil loss and recharges the ground water; increases and conserves the soil moisture; and builds-up the soil fertility. In India, under watershed management programmes, mainly bunding in arable lands, trenching in non arable lands and check dams in drainage lines has been implementing. The design and estimation of these structures were based on the design rainfall and watershed characteristics of the corresponding locations. The Impact of climate change on 24-h design rainfall indifferent locations of China was reported by Xi et al. 2011. The hydrological consequences extreme rainfall in a changing climate have a major impact on the design of hydraulic works in a watershed (Crisci. et al., 2002). The occurrence of extreme rainfall events will also influence the design rainfall of the

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corresponding location. These changes in design rainfall may also affect the changes in design and costing of soil & water conservation structures in watershed programmes. Keeping these considerations in view there is need to investigate the effect of climate change on design rainfall and its effect on design and costing of soil and water conservation structures in watershed programmes.

MATERIALS AND METHODS Study Site For this investigation, the micro watershed located at Central Soil & Water Conservation Research & Training Institute, Research Centre, Research farm, Vasad and surrounding farmers fields was selected. The research farm having the meteorological observatory, the weather data has been recording since 1957. The rainfall data recorded from 1957-till date were used in this study. The micro watershed is located Long: 73.0806°E, Lat: 22.4574°N in Anand District of Gujarat, India. This micro watershed is located in Mahi basin of Central Gujarat which is 18 km from Anand town and 22 km from Vadodara city of Gujarat. The watershed having the arable and non arable lands and drainage lines (Fig.1). The soils of the watersheds are sandy loam soil of has infiltration capacity of 3 to 5 cm/hr, field capacity 19-20% and wilting point 7-8.5%. Soil pH ranges from 7.5 to 7.84 and electrical conductivity 0.16 dsm-1. Soils are poor in fertility with organic carbon ranging from 0.30-0.35 percent. Average (last 50 years) annual rainfall of experimental site is 871 mm with 94% concentrated in the months of June to September (Fig. 2). July-August combine receives 61% of annual rainfall. Annual pan evaporation is 2119.4 mm that shows large deficit of moisture for long periods, and favourable crop growing conditions is restricted to 114 days. Average annual maximum and minimum temperatures are 33.7°C and 18.9°C, respectively. In this micro watershed, field bunding in cultivated lands, contour trenching in non arable lands i.e forest lands and check dams in drainage lines was proposed. Rainfall Distribution The rainfall data from 1957-2012 of the meteorological observatory located at CSWCRTI, RC, was analyzed. The year wise rainfall amount, number of rainy days, frequency of heavy rainfall days (including very heavy and extremely heavy) were determined. A day is called ‘heavy rainfall day’ if the rainfall of that day is 64.5 mm or more according to India Meteorological Department. This includes very heavy (i.e., 124.5–244.5 mm) and extremely heavy (i.e., >244.5 mm) rainfall. These data were analyzed in year wise as well as decade wise.

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Fig. 1: Rainfall Distribution 214

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Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds

Design Rainfall The adequacy of length of record was determined by the method proposed by Mockus (1960). The length of record for this watershed was found as 16 years. However, 30 years data were used for calculating the one day maximum rainfall of different return periods. For most of the soil & water conservation structures one day maximum rainfall in different return period was used. The one day maximum rainfall of 30 year period was plotted by using Weibull’s plotting position method. These plotted data were fitted by Gumbel probability distribution (Gumbel, 1954) and from that one day maximum rainfall of 1, 2, 5, 10, 25, 50, and 100 year return period in different decades was done.

The rainfall excess i.e. runoff was estimated by NRCS Curve Number method (USDA, 1972). In which the inter relationship between Initial abstraction (Ia) and Potential maximum retention (S) were (a) Ia=0.1S for black soil regions with AMC-I and AMC-II (b) Ia=0.3S for all other was proposed by Anonymous (1976), for the Indian watersheds were used. The curve numbers were selected from Anonymous (1976), handbook based on the watershed and rainfall characteristics. Potential maximum retention was estimated from these curve number by using equation 4. Runoff was estimated from the potential maximum retention and one day maximum rainfall of 10 year retun peruod by using equation 1

P  I a 2

(3)

DESIGN OF SOIL WATER CONSERVATION STRUCTURES

Q 

Contour Bunds

Where, P = storm rainfall, mm

In watershed programmes contour bunds are more extensively implemented in arable lands up to 10 % slopes. Bunding is practiced to intercept the runoff flowing down the slope by an embankment with either open or closed ends to conserve moisture, as well as reduce soil and nutrients losses and thereby increase the crop yields in rainfed areas. The cross section of the bunds will depend on rainfall factor, soil characteristics and slope of the land. The cross section represents the top width, bottom width, height and slope( Fig.2). Among this top width usually kept as 0.3 or 0.45 m and side slope 1:1 and height as variable. Height is the major factor which controls the cross section. Height of the bund normally calculated based on one day maximum rainfall in 10 years return period. The following formula were used for calculating the cross section of the bund.

Q = direct runoff, mm

Cross sectional area  (Bottom width  Top width)/2 * height h 

Re xVI

50

Where, Re= Rainfall excess, VI= vertical interval,

Fig. 2: Contour Bund Specifications NSCCIWRS

P  I a

 S

Ia = initial abstraction, mm S = potential maximum retention, mm Q 

P  0 . 3 S 2 P  0 . 7 S 

for Ia = 0.3S

(Curve Number) CN 

25400  S

254

(4) (5)

The runoff, height of the bund, cross sectional area, earth work/ha and cost/ha in cultivated lands of the watershed (Fig.1) were estimated for different decades. For cost calculation, the prevailing earth work rate at CSWCRTI, RC, Research Farm, Vasad for bunding Rs. 90/cum was used uniformly for all the decades. Contour Trenches

(1) (2)

Trenching is one of the most efficient technologies for restoration of these degraded lands which brings desirable changes though in situ conservation of moisture, soil, nutrients and energy fluxes. A contour trench is a micro depression or tiny reservoir constructed across the slope without a spillway with the objective to harvest runoff, eroded soil, nutrient, organic matter etc. and provides opportunity to the collected runoff either to get infiltrated or evaporated before the occurrence of the next rainfall. Contour trenching is an effective storm management option for the control of runoff related fluxes from micro catchments or small watersheds. The design of trenching is based on one day maximum rainfall of 2 year return period. By using one day maximum rainfall of 2 year return period, runoff was estimated by the NRCS curve number method as explained in the above section. From these estimated runoff, number of trenches were determined with 60%, 100% runoff

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storage. The trench sizes consider as 0.5 m x 0.5 m x 2 m (width x depth x length) as reported by (Rao et al., 2012). The earth work/ha and cost/ha were estimated for different decades with 60, 100% runoff harvesting. For cost calculation, the prevailing earth work rate at CSWCRTI, RC, Research Farm, Vasad for staggered contour trenches Rs. 100/cum was used uniformly for all the decades.

The earth work, concrete, brick work quantities, total cost of the proposed check dam in the drainage line of the were estimated with increase in design rainfall intensity by 20, 30 and 40% of existing design rainfall intensities in 1980s. For cost calculation, the prevailing rates at CSWCRTI, RC, Research Farm, Vasad were used uniformly for all the intensities.

Check Dams Check dams have been constructing in watershed programmes to store runoff water and thereby increase the availability of surface and ground water resources. Check dam has been synonym for watershed programme. Depending upon size of nala, its slope, watershed area and severity of the problem, suitable type of check dam can be selected. Various parts of the check dam are shown in Fig. 3. The spillway is a weir structure. Flow passes through the weir opening, drops to an approximately level apron and then passes into the downstream channel. Three steps in design of check dam are hydrologic design, hydraulic design and htructural design. In hydrologic design, peak rate of runoff and runoff volume is estimated at the site of construction for a particular return period depending upon type of structure. Peak rate of runoff can be estimated using Rational formula. Peak discharge is generally computed using rational formula as under:

Q

CIA 360

(6)

Where Q = peak discharge (cumec) C = runoff factor or coefficient I = rainfall intensity for a duration equal to time of concentration for a particular return period (mm/hr) A = watershed area (ha)

Fig. 3: Various Parts of the Check Dam

RESULTS AND DISCUSSION Rainfall Distribution The decade wise average annual rainfall amount and number of rainy days is presented in the Fig. 4 and Fig. 5 respectively. From the Figures, it is observed that there is increase in trend of annual rainfall, but there is not much change in average number of rainy days. The decade wise frequency of heavy rainfall days & one day maximum rainfall amount is presented in the Fig. 6 & Fig. 7. From these figures, it is observed that there is significant increase in number of extremely heavy rainfall days as well as rainfall amount. These increase in one day maxim rainfall can also be affect the design rainfall of the various soil & water conservation structures.

In hydraulic design, determination of height of the structure and spillway dimension is important steps in hydraulic design. Standard formula of hydraulic flow is then used to compute dimensions of various components of the structure. The following formula gives relationship between peak discharge and length and depth of the weir.

Average annual rainfall amount, mm

Q = 1.711 L h3/2/(1.1 + 0.01F

1100

(7)

Where, Q = peak discharge (cumec)

1000

R² = 0.309

900 800 700

Ave. Annual rainfall Linear (Ave. Annual rainfall)

600 500

L = length of weir (m)

1960's

h = total depth of the weir including freeboard (m),

216

1980's

1990's

2000's

Decade

and F = fall (m).

1970's

Fig. 4: Decade Wise Average Annual Rainfall B. Rao

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Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds 41

Average No. Of rainy days

40 R² = 0.059

39 38 37 36 35

Av. No. Of Rainy days Linear (Av. No. Of Rainy days)

34 33 32 1960's

1970's

1980's

1990's

2000's

Decade

Fig. 5: Decade Wise Frequency of Average No of Rainy Days heavy rainfall

40 Number of rainy days

35 30

very heavy rainfall

R² = 0.319

25

Table 1: Effect of Climate Change on Design Rainfall in Different Return Periods

20 15

R² = 0.047

return period is mostly used. From the table, it is inferred that the design rainfall of contour trenches has increase by 11%. For design of contour bunds, one day maximum rainfall of 10 years return period is mostly used. This was also reported by the Kurothe et al., 1999 & Thripathi & Sharda 2011. From the table it is also inferred that the design rainfall for contour bunds has been increased by 30%. The increase in design rainfall of various soil and water conservation structures is due to occurrence of extreme rainfall events in recent years as explained in the above section. This analysis confirmed that in recent year there is increase in design rainfall of the soil and water conservation structures due to climate change. This analysis indicated that there is necessity to relook in design rainfall of various soil and water conservation structures in different parts of India.

Return Period, Years 2 5 10 25 50 100

Extremely heavy rainfall

10 5 R² = 0.781

0

Linear ( heavy rainfall)

1960's 1970's 1980's 1990's 2000's Decade

Fig. 6: Decade Wise Frequency of Heavy Rainfall Days

121 176 213 259 293 327

124 191 236 292 333 375

11 24 30 35 38 40

Effect of climate change on design and costing of various soil & water conservation structures such as contour bunding, contour trenches and check dams were presented in the following sections.

350 Rainfall amount, mm

112 154 181 216 242 268

% Increase

Soil Water Conservation Structures

400

300 250

One Day Maximum Rainfall, mm 1980s 1990s 2000s

R² = 0.811

200

Contour Bunds

one day max rain

150 100 50 0 1960's

1970's

1980's

1990's

2000's

Decade

Fig. 7: Decade Wise One Day Maximum Rainfall Amount

Design Rainfall The one day maximum rainfall for different return periods in decade wise is presented in the table 1. From the table, it is observed that one day maximum rainfall has been increasing with decade wise. For the design of contour trenches, one day maximum rainfall of 2 years

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The decadal wise design and costing of contour bunds is presented in the table 2. Due to decadal wise increase in one day maximum rainfall, the volume of runoff has increased from 119.49 mm to 171.33mm, height from 0.54 to 0.63 m, cross section from 0.45 to 0.58 m2, earth work from112 to 143 m3 and cost from Rs.10080/ha to Rs.12870/ha. The increase in design rainfall due to extreme rainfall events has increased the contour bund height by 16 %, earth work quantities and cost by 28%. Thripathi & Sharda, 2011 was also reported the increase in one day maximum rainfall by 20,40 and 60 the corresponding increase in cross section by 30.9, 65.5 and 103.6%. This implies that there is need to to relook in design & costing of contour bunds in different parts of India with changing climatic scenario.

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Table 2: Effect of Climate Change on Design and Costing of Contour Bunds Decades

Design Rainfall, mm 181 213 236

1980s 1990s 2000s

Runoff, mm 119.49 149.48 171.33

Bund Height, m 0.54 0.59 0.63 (16%)

Cross Section, m2 0.45 0.53 0.58

Earth Work, m3/ha 112 130 143 (28)

Cost Rs./ha 10080 11700 12870 (28)

Note: Values in parenthesis represents % increase over 1980s

Contour Trenches The decadal wise design and costing of staggered contour trenches with 60%, 100% runoff harvesting is presented in the table 3 & 4 respectively. Due to decadal wise increase in one day maximum rainfall, the volume of runoff has increased from 296.29 to 374.1 mm, number of trenches from 356 to 425, earth work 178 to 224 m3 and cost from Rs 17800 to 224000 for 60% runoff harvesting. Similarly in case of 100 % water harvesting, number of trenches has increased from 356 to 425, earth work 178 to 224 m3 and cost from Rs 17800/ha to 224000/ha. The extreme rainfall events in the recent past have increased the trench design quantities and cost has been increased by 26%. Table 3: Effect of Climate Change on Design of Staggered Contour Trenches with 60% Runoff Harvesting Decades

Design Rainfall

Runoff

1980s 1990s 2000s

112 121 124

296.29 354.18 374.10

Number of Trenches/ Ha 356 425 449 (26)

Earth Work/ Ha 178 213 224 (26)

Cost/ha

17800 21300 22400 (26)

Note: Values in parenthesis represents % increase over 1980s Table 4: Effect of Climate Change on Design of Staggered Contour Trenches with 100% Runoff Harvesting Decades

Design Rainfall

Runoff

1980s 1990s 2000s

112 121 124

296.29 354.18 374.10

Number of Trenches/ Ha 593 708 748 (26)

Earth Work/ Ha 296 354 374 (26)

Cost/ Ha 29600 35400 37400 (26)

Note: Values in parenthesis represents % increase over 1980s

Check Dams The decadal wise design and costing of brick masonry check dam in drainage line of the watershed is presented in the table 5. Due to increase in designed rainfall intensity, the peak runoff has increased from 7.5 -10.5 m3/sec, earth work quantities from 13.5 to 16 m3, concre quanities 5.2-6.2 m3, brick masonry from 38 to 42m3 and cost from Rs.184279/check dam to Rs. 205505/check dam. The extreme rainfall events in the recent past has increased the peak runoff rate by 40% and cost by 12%.

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Table 5: Effect of Climate Change on Design of Staggered Contour Trenches with 100% Runoff Harvesting Design Rainfall Intensity, mm/ hr 100 120 130

Peak Runoff, m3/ sec

Earth Work, m3

Concrete Work, m3

Brick Work, m3

7.5 9 9.75

140

10.5

13.5 14.5 15 16 (19)

5.2 5.6 5.9 6.2 (19)

38 40 41 42 (11)

Amount, Rs./ Check dam 184279 193921 199720 205505 (12)

Note: Values in parenthesis represents % increase over 1980s

CONCLUSION The analysis showed that as a result of climate change, there is significant increase in number of extremely heay rainfall days (as defined by IMD) as well as rainfall amount. This increase in one day maxim rainfall is affecting the design rainfall of the various soil & water conservation structures. The increase in design rainfall of various soil and water conservation structures is due to occurrence of extreme rainfall events in recent years has increased by 11, 30, 38% for design of staggered contour trenches, contour bunds and check dams respectively. The cost of for construction of staggered contour trenches, contour bunds and check dams in watersheds have been increased by 26, 28, and 12% respectively. Department of Land Resources under Ministry of Rural Development, Government of India has prescribed enhanced cost norms from Rs 6000/ha to Rs 12000/ha in plain areas and Rs 15000/ha in difficult areas as common guideline in the execution of the watershed management programme in the country (GOI, 2008). Normally, the rise in cost norm is accounted for by the price rises in the economy and compensates for the losses incurred due to enhanced spending on various measures. There is a need to account for design change in soil and water conservation structures caused by the climate change which could enhance the design cost by up to 28%. The conventional design and costing prescribed for common soil moisture conservation programmes, in the light of the climate change, might need a relook in all the programmes of the Government of India. More so, in areas highly vulnerable to climate change impact.

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Impact of Climate Change on Design & Costing of Soil & Water Conservation Structures in Watersheds

ACKNOWLEDGMENT The authors are thankful to Sh. M. J. Baraiya, & Anand Kumar, technical staff, of CSWCRTI, RC, Vasad for collecting the data. We also acknowledge the unknown reviewers who have contributed to increasing the quality of this paper.

Watershed, Kheda, Gujarat, under Integrated Wasteland Development Programme”, Ministry of Rural Development, Govt. of India, New Delhi, CSWCRTI, Dehradun. Kurothe, RS, Samra JS, and Samarth RM (1999). “Optimum Design of Contour Bunds for Navamota watershed (Gujarat) A case study”, Indian J. of Soil Conservation, Vol.27 (1): 17-21. Lal M, (2003) “Global climate change: India’s monsoon and its variability”. J. Environ. Stud. Policy, 2003, 6, 1–34.

REFERENCES Anonymous, (1976) Handbook of Hydrology, Ministry of Agriculture, Government of India, New Delhi. Crisci AB, Gozzini F, Meneguzzo S, Pagliara, and Maracchi G (2002) “Extreme Rainfall in a Changing Climate: Regional Analysis and Hydrological Implications in Tuscany” Hydrol. Process. 16, 1261–1274. Dash S K, Jenamani RK, Kalsi, SR, and Panda SK (2007) “Some Evidence of Climate Change in Twentieth-century India”, Climatic Change, 85, 299–321. Dhyani BL, Samra J S, Juyal G P, Ram Babu, Katiyar V S, (1997). “Socio-economic analysis of a participatory integrated watershed management in Garhwal Himalayas – Fakot watershed”. Bulletin No. T-25/D-29, CSWCRTI, Dehradun.

Mirza MQ, Global Warming and Changes in the Probability of Occurrence of Floods in Bangladesh and Implications”. Global Environ. Change, 2002, 12, 127–138. Mockus V, (1960) “Selecting a Flood Frequency Method”. Trans. ASAE No. 3:48-5. Naresh Kumar S, Aggarwal PK, Swaroopa Rani1, Surabhi Jain1, Rani Saxena and Nitin Chauhan (2011) “Impact of climate change on crop productivity in Western Ghats, coastal and northeastern regions of India” Current Science, VOL. 101, NO. 3, 10, pp;332-341

Watershed

Ninan KN, Lakshmikanthamma S, (1994). Sustainable Development-The Case of Watershed Development in India, International Journal of Sustainable Development and World Ecology, 1(4), 229-238.

Guhathakurta P and Rajeevan M, (2008). “Trends in rainfall pattern over India”, Int. J. Climatol. 28 1453–1469.

Pal I and Al-Tabbaa A, (2009). “Trends in seasonal precipitation extremes- An indication of climate change in Kerala, India, J. Hydrol., 367: 62-69.

GOI. (2008). Common Guidelines for Development Projects, Goverement of India.

Guhathakurta P, Sreejith OP and Menon PA (2011). “Impact of Climate Change on Extreme Rainfall Events and Flood Risk in India” J. Earth Syst. Sci. 120, No. 3, pp. 359–373 Gosain AK, Sandhya Rao, Anamika Arora (2011). “Climate Change Impact Assessment of Water Resources of India, Current Science, Vol. 101, No. 3, pp 356-371. Goswami BN, Venugopal V, Sengupta D, Madhusoodanam MS and Xavier PK (2006). “Increasing Trends of Extreme Rain Events Over India in a Warming Environment”, Science, 314, 1442–1445. Gumbel EJ (1954). "Statistical Theory of Extreme Values and Some Practical Applications". Applied Mathematics Series, 33. U.S. Department of Commerce, National Bureau of Standards IPCC. 2007. Fourth Assessment Report. Khan, TMA, Singh, OP, and Sazedur Rahman MD (2000). “ Recent Sea Level and Sea Surface Temperature Trends along the Bangladesh Coast in Relation to the Frequency of Intense Cyclones”, Mar. Geodesy, 2000, 23, 103–116. Kerr J, Pangare V L, George P J ( 2006). Evaluation of Dryland Watershed Development Projects in India”, EPTD discussion paper No. 68, IFPRI, Washington D.C., U.S.A. Kumar Virendra, Kurothe RS, Singh HB, Tiwari, SP, Nambiar KTN, Pande VC, Bagdi GL, Sena DR, Samarth RM and Damor CN (2004). “Participatory Watershed Management for Sustainable Development in Antasar

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Rao BK, Kurothe RS, Singh AK, Parandiyal AK, Pande VC, Kumar Gopal (2012). “Bamboo Plantation Based Technological Interventions for Reclamation and Productive Utilization of Ravine Lands”, CSWCRTI, Technical Bulletin No. T-62/V-4: 30p. Samra JS (1997). “Status of Research on Watershed Management”. Paper presented at the 173rd Meeting of General Body of ICAR, New Delhi, CSWCRTI, Dehradun. Shrestha AB, Wake CP, Dibb JE, and Mayewski, PA, (2000). Precipitation Fluctuations in the Nepal Himalaya and its Vicinity and Relationship with some Large Scale Climatological parameters”. Int. J. Climatol., 20, 317–327. Soil conservaton Service, USDA. (1964). Hydrology, section 4, National Engineering Handbook, Washington, DC. Tripathi KP, and. Sharda VN (2011) “Mitigation of Impact of Climate Change through Watershed Management” JAE 48 (1)38-44. USDA, Soil Conservation Service. (1972). National Engineering Handbook, Section 4: Hydrology. USDA, Soil Conservation USDA, Soil Conservation Service, Washington. Yue-Ping Xu, Xujie Zhang, Ye Tian (2012) “Impact of Climate Change on 24-h Design Rainfall Depth Estimation in Qiantang River Basin East China” Hydrological Processes, Volume 26, Issue 26, pages 4067–4077.

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Rainfall Analysis to Plan Water Harvesting Structures in Micro Watersheds of NAU Research Farms, Bharuch Sarika Santu Wandre Ph.D. Scholar, Dept. of Soil & Water Engineering, CAET, Junagadh Agril, University, Junagadh–362001 [email protected]

Prashant Kumar Shrivastava Head & Professor, Dept. of Natural Resource Management, ASPEE College of Horticulture & Forestry, Navsari Agril, University, Navsari–396450 [email protected]

ABSTRACT Rainfall analysis using 22 yrs daily rainfall data of Bharuch meteorological station was done to estimate the runoff from micro watersheds that exist in Bharuch Research farm of Navsari Agricultural University (NAU). The hydrological data was screened for absence of trend and stability of variance and mean. Students t distribution was symmetrical around t=0. The t test performed to check stability of mean indicated that there was no trend, and variance and mean were stable, which proved that time series is stationary and the data could be used even at lower level of aggression i.e. those covering a day or a month. The runoff volume was estimated using Rational method for the sub micro watersheds 1, 2 and 3;it was 0.38, 0.9 and 1.4 cumec respectively. These discharge volumes were used to design surface drains, pond and drop spillway at appropriate placesof the farm. Since soils layers below 1 m are highly permeable, to check percolation losses, 250 micron, black plastic lining was recommended for the ponds to be constructed in micro watersheds.

KEYWORDS: Rainfall Analysis, Watershed Development, Pond, Runoff, Drainage, Water Conservation, Irrigation Planning

INTRODUCTION Bharuch research farm of Navsari Agricultural University (NAU) located near the Gulf of Khambhat in Arabian Sea falls at 72-0' E Longitude, 21.5' N Latitude and 15.64 m. MSL, Altitude. Bharuch comes under Agro climatic Zone-II & Agro-ecological situation V and is a part of southern part of Gujarat. It has semi arid climate with three distinct seasons i.e. kharif (June to September), winter (October to January) and summer (February to March). The district receives rainfall through south west monsoon which normally starts from middle of July; August and September are the months of heavy rainfall. The soil profile of Bharuch districts is highly permeable and erosive. Due to availability of water from river Narmada and rail and road connectivity, the district has a good potential for agriculture as well as industry. The main agricultural crops of Bharuch and that of University Research farm are Cotton and Pigeon pea.Water scarcity is there from rabi and summer as most of the agriculture is rain fed, rainwater goes as runoff and due to urbanization and industrial growth, ground water demands have increased manifold. Moreover, industry discharge their effluent in the river which has made the river polluted, the water quality further deteriorates during high tide in 220

sea, saline water enters the river upto several kilometers, while further impeding the flow of polluted discharges from upstream. Due to these reasons, good quality irrigation water becomes scares during three quarter of year in the district as well as in the Research Farm. Therefore, to provide life saving irrigations in rabi season and to recharge ground water harvesting activities were planned in the University farms. Water harvesting along with shallow surface drains will help in quick removal of water stagnation during monsoon. Planed Watershed management will also help in demonstrating the technique to students and farmers of the region.

MATERIAL AND METHODS Annual rainfall data from 1990 to 2012 and daily rainfall data from 1990 to 2012 wasavailable from Bharuch meteorological station. The hydrological data was screened for absence of trend and stability of variance and mean. After plotting the data it was ensured that there is no correlation between the order in which the data have been collected and the increase or decrease in magnitude of those data. To verify absence of trends Spearman’s rank correlation method was used. It is simple and distribution free. Yet another advantage is its nearly linear and collinear trends. The distribution of the variance ratio of samples from a normal distribution is known as F or fisher distribution, the test gives an acceptable indication of stability of variance.

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Rainfall Analysis to Plan Water Harvesting Structures in Micro Watersheds of NAU Research Farms, Bharuch

Test for Absence of Trend = 1−

=

I= Average intensity of rainfall for the time of concentration (Tc) for a selected design storm, mm/hr

∑ (

(1)

)

−

(2)

Where, Rsp= Spearman’s rank correlation n= total number of data d= difference

A= Drainage area, ha For the estimation of rainfall intensity, the value of K, a, b and n for the western zone are taken as 3.974, 0.1647, 0.15, 0.7327 respectively and duration t is 1 h and return period of 25 yrs. (Tideman E.M., 2007) Drop Spillway Design

i= chronological order

The Design of Drop spillway was done using the design steps used in (Suresh R.2002):

kxi= rank of the variable x kyi= the series of observation yi is transformed to rank equivalent

Q=

.

(7)

.

Q= discharge, cumec

F Test for Stability of Variance and Mean Ft =

/

.

F= free board, m (3)

.

H= head, m

Where, s2= variance

For the estimating of peak runoff rate for the design of drainage, the 5 yrs recurrence interval was taken in this area (Micheal and Ojha, 2011)

s= standard deviation

RESULTS AND DISCUSSION

n= total number of data

The series of observation yi is transformed to its rank equivalent kyi by assigning the chronological order number of an observation in the original series to the corresponding order number in the ranked series y. null hypothesis was tested (Table 1), Ho: p=0 (there is no trend) against the alternate hypothesis, H1: p 0 (there is a trend) with the test static. Students t distribution is symmetrical around t=0. The null hypothesis is accepted if tt is bounded by t ((n-2), 2.5%) < tt >t ((n-2), 97.5%), (Spearmanrcc. pdf). For the F test for stability of variance and mean the null hypothesis for the test was HO: s12=s22. Rough screening of Bharuch rainfall data was done by plotting the data as time series that revealed there are no major discrepancies. There was no trend, and variance and mean were stable. Therefore the time series is stationary and the data could be used even at lower level of aggression i.e. those covering a day or a month etc.

s=

∑

((∑ (

)

(4)

) .

xi= mean of the data The t Test for Stability of Mean tt = (

(

)

(

)

×(

)) .

(5)

Where, n= number of data in subset x= mean of the subset s2= variance The runoff volume in cumec was estimated using rational method (Rational Method.pdf). Q=

(6)

Q= peak rate of runoff, cumec C= Runoff coefficient, an empirical coefficient representing a relationship between rainfall and runoff

Table 1: Trend Analysis of the Yearly Rainfall Totals in mm at Bharuch from 1990 to 2012 Year 1990 1991 1992 1993 1994 1995 1996 1997

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i=x 1 2 3 4 5 6 7 8

RF (mm) 1183 500 1092 760 1175 589 955 503

y= Ranked Rainfall 42 233 400 420 475 500 503 549

Sarika Wandre

kxi 1 2 3 4 5 6 7 8

kyi 15 12 20 10 19 2 8 13

di -14 -10 -17 -6 -14 4 -1 -5

di2 196 100 289 36 196 16 1 25 Table 1 (Contd.)…

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National Seminar on Climate Change Impacts on Water Resources Systems …Table 1 (Contd.) 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Total Rsp tt t at 2.5% t at 97.5%

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1014 420 572 233 549 917 42 1330 1278 926 475 400 1343 889 621

572 589 621 760 889 917 926 955 1014 1092 1175 1183 1278 1330 1343

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

11 6 23 4 22 14 18 7 9 3 5 1 17 16 21

-2 4 -12 8 -9 0 -3 9 8 15 14 19 4 6 2

4 16 144 64 81 0 9 81 64 225 196 361 16 36 4 2160 -0.06719 -0.30862 -2.079 2.079

Table 2: Preferable Pond Locations at NAU Bharuch Pond

Location

I II III

Surface Drains (Parabolic Shaped) Depth (m) Top Width (m) 0.65 1.3 0.95 1.8 1.2 2

Plot No 11 B; NARP Farm Plot No 13; NARP Farm Plot No 10; Cotton Farm

Pond Dimensions (L x W x D) (m x m x m) 40 x 30 x 2.5 40 x 40 x 2.5 100 x 50 x 2.5

Table 3: Final Inlet and Outlet Dimensions of Drop Spillway Sub Area

1 2 3

Inlet (Pipe L F Diameter) m m m

0.56 0.87 1.1

3 3 4

H m

E m

Lb m

J m

Ht m

S m

M m

TW BW 3 0.20 1.2 2.0 0.40 0.067 0.05 1.732 0.368 0.45 0.67 2 0.28 1.44 1.7 0.60 0.10 0.07 1.22 0.58 0.45 0.67 1 0.38 1.74 1.5 0.76 0.13 0.095 1.50 0.10 0.45 0.67

The runoff volume obtained for the micro watersheds 1, 2 and 3 were 0.38, 0.9 and 1.4 cumec respectively. Surface drains with parabolic shape are designed for the estimated discharges of micro watersheds. On the basis of uncultivable land available that remains water logged during monsoon was selected for construction of pond, the excess water from the pond could also be discharged in the existing natural drain at the farm border. Dimension of planned ponds and that of drop spillway are in Table 2 and Table 3 respectively. To check percolation losses, total surface area of pond was considered and the amount of 250 micron black plastic required for pond lining in micro watersheds 1,2 and 3 were respectively 526.25 kg, 651

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Outlet K Head m Wall m

Side Wall m

TW 0.3 0.3 0.3

BW 0.55 0.55 0.55

Wing Wall and Head Wall Extension m TW BW 0.3 0.40 0.3 0.40 0.3 0.40

Apron Thickness cm

25 25 25

kg and 1708 kg. The Preferable Pond Locations at NAU Bharuch is given in Table 2 and in fig 2 and Fig 4.

IRRIGATION PLANNING The main agricultural crops of the region are Cotton and Pigeon pea. The Kharif sown cotton should be given 5 to 6 irrigations in South and middle Gujarat conditions. This can be further reduced to 2 to 3 when the practice of mulching is adopted. The Rabi sown cotton in South Gujarat heavy rainfall zone needs 9 irrigations after cessation of monsoon, the crop needs to be irrigated 2-3 times at monthly interval, (Anonymous 1994). Depth of irrigation for the Pigeon pea is 70 mm while for cotton it is 70 to 80 mm.

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Rainfall Analysis to Plan Water Harvesting Structures in Micro Watersheds of NAU Research Farms, Bharuch

Table 4: Estimated Cost of Earth Work Pond I II III

Location Plot No 11 B; NARP Farm Plot No 13; NARP Farm Plot No 10; Cotton Farm

Excavation Volume (Cubic Meter) 3000 4000 12500

Estimated Cost of Earthwork (Rs) 135000 180000 562500

Rate of digging and transportation at 500 m lead is assumed to be @ 45 Rs/cum

RECHARGE PIT/ WELL Dimensions of pit: 4 m x 3 m x 2.5 m and Free board of 1 m Located at in the depressions where water stagnates during monsoon, preferably at every 200 m along the border of farm, inside the farm boundary or in low lying areas within the farm, where water stagnates for long period. The rough budgetary requirement for the watershed developmental work is shown in Table 5. Table 5: Budgetary Requirement (Rough Estimates) S. No. 1 2 3 4 5 6

Item Jungle clearing Pond Excavation Pond Lining (250 Micron black plastic) in Pond I Drop Spillway in Pond I & II Fencing around ponds Recharge Pit / Well Grand total

Approx. Cost (Rs) 20000 877500 200000 100000 200000 102500 15,00,000

Fig. 2: Agriculture College and NARP Farm, NAU, Bharuch (proposed ponds)

Fig. 1: Agriculture College and NARP Farm, NAU, Bharuch

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Fig. 3: Cotton Research Station, NAU, Bharuch

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and surface drains in three micro watersheds were planned at appropriated locations, on the basis of rainfall analysis of 22 yrs rainfall records.

REFERENCES Anonymous (1994, 1995, and 1996). “Joint AGRESCO Reports No. 1994/23; 1995/24; 1996/25: Navsari Zone, Gujarat Agricultural University”. Comprehensive District Agriculture Plan (C-DAP): “District Bharuch, Dept. of Agriculture and Co Operation Government of Gujarat Gandhinagar”. Micheal. A.M. and Ojha T.P. (2011). “Principles of Agricultural Engineering Vol. II,” Jain Brothers, New Delhi: pp 509. Shrivastva P.K., Patel B.R. and Raman S. (2001).” Drainage design for the Ukai right bank canal command”; SWM IDNP Tech.Bull.15 Suresh R., (2002). “Soil and water conservation engineering”, Standard Publishers Distributors., Delhi: pp 206-233. Tideman E.M., (2007). “Watershed management, Guidelines for Indian Conditions”, Omega scientific publisher, New Delhi: pp 74-78. Fig. 4: Cotton Research Station, NAU, Bharuch (Proposed Ponds)

www.ctre.iastate.edu/pubs/stormwater/documents/2C-4, Rational Method.pdf

CONCLUSION

www.mei.org.uk/files/pdf/Spearmanrcc. pdf

Management of rain water at Bharuch Research Farms of Navsari Agricultural University was prepared. Rain water harvesting ponds, percolation pits, drop spillway

224

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Author Index Arya, D.S., 97 Balakrishnan, P., 50 Barbele, Vikas, 70 Baria, Sonal V., 140 Bhandari, N.K., 79 Bhatnagar, Neeraj Kumar, 206

Chandniha, Surendra Kumar, 89 Chaudhari, N.J., 13, 33 Chinchorkar, Sachin S., 179 Chokshi, Rina, 154

Das, Jew, 165 Gaur, M.L., 140, 199 Gedam, S.S., 59 Goyal, Rohit, 122 Greeballa, A., 97

Jayesh, Nakum K., 140 Jha, Shekharendu, 70 Joshi, M.B., 93

Kansal, M.L., 89 Karande, B.I., 13, 33 Krishnaveni, Muthiah, 190 Kuldeep, 122 Kumar, Gopal, 212 Kumar, M. Dinesh, 104 Kurothe, R.S., 212

Lunagaria, M.M., 13, 33

Paradava, Dhaval M., 128 Parekh, F.P., 135 Parmar, P.K., 13, 33 Parmar, Shilpa C., 187 Patel, H.R., 13, 33 Patel, Hiren P., 18, 128, 170 Patel, Neha, 26, 41, 147, 154 Patel, P.L., 64 Patel, Rajni J., 128 Porey, P.D., 64 Praveen, Dhanya, 190

Ramachandran, Andimuthu, 190 Ranade, Vidyanand Mahadeo, 190 Rank, Harji D., 18, 128, 170 Rao, B. Krishna, 212 Rathod, Pankaj J., 18, 128, 170

Sankhua, R.N., 59 Sayyad, F.G., 179 Shah, Devang, 147 Sharma, Ankit, 122 Shekh, A.M., 199 Shete, Dilip T., 187 Shete, Dilip, 26, 41, 147, 154 Shrivastava, Prashant Kumar, 220 Srivastava, Rishi, 70 Suryanarayana, T.M.V., 135 Suryawanshi, R.K., 59

Tamilmani, D., 50

Mishra, P.K., 212

Thirumurugan, Perumal, 190 Timbadiya, P.V., 64 Tiwari, Mukesh K., 140

Murumkar, A., 97 Muthuchamy, I., 50

Umamahesh, N.V., 165

Namdeo, N.P., 79

Vaidya, V.B., 3, 179

Neelakanth, J.K., 50 Nema, R.K., 206

Varshneya, M.C., 3 Vekariya, Popat B., 128, 170 Venkataraman, S., 10

Panchal, Ratan A., 135 Pande, V.C., 212 Pandey, J.S., 112 Pandey, Vyas, 13, 33, 179 Pandya, Vandana, 154

Wandre, Sarika Santu, 220 Yadav, S.B., 13, 33

A Comparative Study of KPan Models for ET0 Computations P.K. Singh Assistant Professor, PAE, AAU Campus, Dahod, Gujarat [email protected] Saswati Ray [email protected] PG student, NIT Durgapur S.K. Patel Assistant Professor, KVK, AAU Campus, Dahod, Gujarat [email protected]

ABSTRACT Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. Recent studies on climate change have also focused ET as the third most important climatic factor controlling energy and mass exchange between terrestrial ecosystems and the atmosphere, including temperature and precipitation. There are several models which are widely used to determine pan coefficient (KPan), using wind speed, relative humidity and fetch length conditions for reference evapotranspiration (ET0) computations. This paper analyses two exiting pan coefficient models, namely Allen & Pruitt (1991) and Snyder (1992) to estimate KPan values and consecutively ET0 for a semi-arid climatic region of Gujarat, India, and a comparison has been made with Food and Agricultural Organization (FAO)-Penman-Monteith (FAO56-PM) (Allen et al., 1998) method as a reference for ET0 computations using 33 year weather dataset. The goodness-of-fit (GOF) was evaluated in terms of Root mean square error (RMSE), Percentage error of estimate (PEE), and Mean bias error (MBE). The RMSE values are found to vary from 0.15 to 2.82 and 0.16 to 2.71 with an average of 0.81 and 0.85; PEE values from 7.62 to 22.68 and 8.19 to 21.59 with an average 14.58 and 15.63; MBE values from -0.138 to 1.368 and 0.129 to 1.292 with an average value of 0.491 and 0.587, respectively for Allen & Pruit and Snyder model. Therefore, based on GOF and visual comparison as well, ET0 values computed using Allen and Pruitt model have very close agreement with the FAO56-PM method for daily, monthly, and annual estimates as compared to Snyder model, and therefore, Allen & Pruitt model should be naturally a preferred choice as compared to Snyder model for ET0 computations for semi-arid climatic conditions

KEYWORDS: Climate

Change; Reference evapotranspiration; Food and Agricultural Organization; Pan Coefficient models; Evapotranspiration

INTRODUCTION Accurate estimation of evapotranspiration is required for efficient irrigation management. Evapotranspiration is a complex process because it depends on several weather factors, such as temperature, radiation, humidity, wind speed and type and growth stage of the crop. The concept of reference evapotranspiration (ET0) was introduced to avoid need to calibrate a separate evapotranspiration equation for each crop and stage of growth. Allen et al. (1998) defined reference evapotranspiration as “the rate of evapotranspiration from a hypothetical crop with an assumed crop height (0.12 m) and a fixed canopy resistance (70 s m−1) and albedo (0.23) which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water”.

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Numerous models, classified as temperaturebased, radiation-based, pan evaporation- based and combination-type, have been developed for estimating reference evapotranspiration (ET0). The International Commission for Irrigation and Drainage (ICID) and Food and Agriculture Organisation of the United Nations (FAO) have proposed using the FAO-56 PM equation as the standard equation for estimating reference evapotranspiration, and for evaluating other equations (Allen et al. 1994a, b). Many studies have indicated the superiority of this equation (Ventura et al. 1999; Pereira and Pruitt 2004; Lopez-Urrea et al. 2006; Gavilan et al. 2007; and Singh et al., 2013). Various studies such as Jensen et al. (1961), Pruitt (1966), Doorenbos and Pruitt (1975) have shown that a high correlation exists between Epan and ET0, when evaporation pans are maintained properly. Since the evaporation rate from the Class A pan U.S. Weather Bureau (Epan) and the ET0 rate from the vegetated surface differ (Snyder, 1992), the two rates are related by a coefficient, Kpan, (Irmark et al., 2002) as:

P. K. Singh

ET0 = Epan Kpan

(1)

226

The local environments (Pruitt, 1966; Doorenbos and Pruitt, 1977; Burman et al., 1980) in which the evaporation pans are located are critical to the proper interpretation of evaporation pan data (Howell et al., 1983). METHODOLOGY

models of Allen and Pruitt and Snyder model (Eqs. 2 & 3), because the comparative studies (Jensen et al., 1990; Itenfisu et al., 2000) have confirmed the superior performance of FAO56-PM method. Moreover, the method has also been accepted as a standard method for estimating ET0 by the ASCE Task Committee on standardization of ET0. The FAO56-PM method (Allen et al., 1998) can be expressed as:

Site location and Data Daily weather data for a period of 33-year (1975-2008) was obtained from the Agricultural Meteorological Department (AMD) of Anand Agricultural University (AAU), Anand, Gujarat, India. The study area is situated between 220 06´ to 220 43´ N latitude and 720 2´ to 730 12´ E longitude, having semi-arid climate with an average annual rainfall of 858.8 mm, and approximately 75% of which occurs during June through September. The mean maximum and minimum temperature ranges from 27.9 to 39.2 0C and 9.5 to 23.1 0C, respectively. Daily mean temperature ranges from 19 to 30.2 0C and relative humidity from 38 to 76%. In the present study, the value of upwind fetch distance of low-growing vegetation (F) was taken as 100 m for computing Kpan values.

ET

∆

Allen and Pruitt Model Allen and Pruitt (1991) proposed a simple model to compute Kpan as a function of daily wind speed (U2), daily mean relative humidity (RH), upwind fetch distance of low-growing vegetation (F) as: Kpan =

0.108-0.000331U2+0.0422 ln(F)+0.1434 ln(RH) -0.000631[ln(F)]2 ln(RH) (2)

where, U2 = daily mean wind speed measured at 2 m height (km/day), RH = daily mean relative humidity (%), and F = upwind fetch distance of low-growing vegetation (m). Snyder Model Snyder (1992) proposed a simple model to compute daily Kpan values as a function of U2, RH, and F. The expression of the model can be expressed as:

U

e

4

4.87 ln 67.8z 5.42

U

5

where, U2 = wind speed at 2 m above ground surface (m/s); Uz=measured wind speed at z m above ground surface (m/s); and z = height of measurement above ground surface (m). Finally, Eq. (4) was used to calculate daily ET0 using 33-year weather dataset and then averaged to obtain a long-term daily average. Kpan values obtained using Eqs. (2) & (3) with the given meteorological condition were multiplied with 33-year record of Epan values to get corresponding ET0 values on a daily basis and then averaged over the 33 years to obtain a long-term daily average. Daily and monthly ET0 values calculated using Eqs. (2) & (3) were compared with ET0 values using Eq. (4). The goodness-of-fit (GOF) statistics in terms of Root-mean-square error (RMSE), Percent error of estimates (PEE) and Mean biased error (MBE) were used as indicators of efficacy, accuracy and reliability of Kpan equations for ET0 computations. The expressions for RMSE, PE and MBE can be expressed as: Root Mean Square Error (RMSE): N

RMSE = Kpan = 0.482 + [0.24 ln (F)]-(0.000376 U2) + (0.0045 RH) (3)

∑ (X i =1

− Yi )

2

i

N

(6)

Percentage Error of Estimate (PEE):

Penman-Monteith (FAO56-PM) Model The Penman-Monteith (FAO56-PM) (Allen 1986; Allen et al., 1994a&b, 1998) ET0 method has been used to compare the performance of ET0 estimated from Kpan

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900 U e T 273 γ 1 0.34U γ

where, ET0 = reference crop evapotranspiration (mm/day); T = mean daily air temperature measured between 1.5 and 2 m height (0C) [= (Tmax + Tmin)/2]; Rn = mean daily net radiation (MJ m–2 day–1); G = soil heat flux density (MJ m–2 day–1); U2 = wind speed at 2 m height (m s–1); es = saturation vapour pressure (kPa); ea = actual vapor pressure (kPa); (es – ea) = vapour pressure deficit (kPa); ∆ = slope of vapour pressure curve (kPa 0 –1 C ); and γ = psychometric constant (= 0.067 kPa 0C–1). The daily wind speed measured at z m above ground can be converted to 2 m height using the relationship given by Allen et al. (1998), expressed as:

MODEL DESCRIPTION AND APPLICATION A brief description of the two Kpan estimation models along with Food and Agriculture Organization (FAO)Penman-Monteith (FAO56-PM) has been discussed here as follows.

G

0.408∆ R

PE =

P. K. Singh

(Xi − Yi ) 100

(7)

Yi

227

with the computed values of ET0 using FAO56-PM and Allen & Pruitt and Snyder model are given in Table 1. It can be observed from Table 1 that the RMSE values vary from 0.15 to 2.82 and 0.16 to 2.71 with an average of 0.81 and 0.85; PEE values from 7.62 to 22.68 and 8.19 to 21.59 with an average 14.58 and 15.63; MBE values from -0.138 to 1.368 and 0.129 to 1.292 with an average value of 0.491 and 0.587, respectively for Allen & Pruit and Snyder model. It can be also observed from Table 1 that the average annual values of RMSE, PEE and MBE for Allen and Pruitt (1991) model are lower than Snyder (1996) model, and therefore, Allen & Pruit (1991) model has better agreement to the FAO56-PM method as compared to Snyder model, and hence wider applicability for ET0 predictions in semi-arid climatic conditions. Allen&Pruitt

Dec

Allen&Pruitt

Snyder FAO56-PM

11 10 9 8 7 6 5 4 3 2 Jan

Snyder FAO56-PM

0.95

ET0 (mm/day)

Daily Kpan values computed using Allen Pruitt and Snyder model (Eqs. 2 & 3) were compared with FAO56-PM Kpan (Allen et al., 1998) values and have been graphically represented in Figure 1. The computed Kpan values were found to vary from 0.75 to 0.89, where the minimum and maximum values correspond to FAO56-PM and Snyder model. It can also be observed from Fig. 1 that the computed values of Kpan by Allen & Pruit are almost similar to FAO56-PM model as compared to Snyder model.

Oct

The analysis was carried out using daily, monthly and annual ET0 as discussed here. Computation of Daily ET0

Nov

RESULT AND DISCUSSION

Sep

where, Xi and Yi are the ET0 values based on Kpan and FAO56-PM, respectively, and N is the number of observations.

Aug

(8)

Jul

− Yi ) N

Jun

i

May

i =1

Apr

∑ (X

Mar

N

MBE =

Feb

Mean Bias Error (MBE) (Sabziparvar et al., 2012):

Kpan

Month 0.85

Figure 2: Comparison of calculated daily ET0 using Allen & Pruitt and Snyder model and FAO56-PM model

0.75 Table 1: Monthly and Annual Average of ET0 (mm), RMSE, PEE and MBE Estimates

Month Figure 1: Daily Kpan values computed using Allen & Pruitt and Snyder model as compared to FAO56-PM model. Each data point represents an average of 33 measurements per day Computation of Monthly and Annual ET0 The Kpan values estimated using Eqs. (2) & (3) were further used to compute daily ET0 using Eq. (1) and were then compared with ET0 computed by FAO56-PM model as shown in Figure 2. It can be observed from Fig. 2 that the daily ET0 values computed using Allen & Pruitt have better agreement with FAO56-PM model as compared to Snyder model. The Goodness-of-fit statistics was further performed for monthly and annual ET0 computations for all the three models taken in this study, i.e., Allen & Pruitt (1991) model, Snyder model (1996) and FAO56-PM (Allen et al., 1998). The monthly mean estimated values of RMSE, PEE, and MBE along

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Month

Dec

Oct

Nov

Sep

Aug

Jul

Jun

May

Apr

Feb

Mar

Jan

0.65

Allen & Pruitt Model

FAO 56-PM Model

Snyder Model

ET0 (mm)

ETK (mm)

RMSE

PEE

MBE

ETK (mm)

RMSE

PEE

MBE

3.16

3.58

0.27

15.58

0.43

3.65

0.33

17.61

0.49

4.12 5.33 6.86 7.70 5.79 4.58 4.26 4.12 3.89 3.23 2.81 4.65

4.67 6.32 8.05 9.07 6.19 4.62 4.12 4.15 4.08 3.61 3.27 5.14

0.54 1.36 2.82 2.74 0.56 0.19 0.15 0.19 0.25 0.29 0.31 0.81

16.73 20.76 22.68 19.45 11.29 7.62 7.99 9.43 10.81 13.89 18.70 14.58

0.56 0.99 1.19 1.37 0.40 0.05 0.03 0.19 0.38 0.46 0.49

4.69 6.25 7.94 8.99 6.41 4.89 4.38 4.41 4.22 3.71 3.35 5.24

0.57 1.31 2.71 2.68 0.79 0.30 0.16 0.28 0.30 0.35 0.38 0.85

17.07 20.28 21.59 18.96 13.56 9.54 8.19 11.54 11.81 15.98 21.48 15.63

0.57 0.92 1.08 1.29 0.61 0.32 0.13 0.29 0.33 0.48 0.54 0.59

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg.

CONCLUSIONS The study was conducted to evaluate the existing pan coefficient models of Allen and Pruitt (1991) and Snyder (1992). Using a 33-year class A pan daily evaporation data (EPan) and the estimated KPan coefficients, reference

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crop evapotranspiration (ET0) were predicted for the period of study. The deduced ET0 values were compared to the corresponding ET0 values which obtained from the standard FAO 56 Penman–Monteith (FAO 56-PM) method. The goodness-of-fit (GOF) statistics was evaluated in terms of Root mean square error (RMSE), Percentage error of estimate (PEE), and Mean bias error (MBE) to test the efficacy of the models used in this study. The average annual values of RMSE, PEE and MBE were found to vary from 0.15 to 2.82 and 0.16 to 2.71 with an average of 0.81 and 0.85; 7.62 to 22.68 and 8.19 to 21.59 with an average 14.58 and 15.63; -0.138 to 1.368 and 0.129 to 1.292 with an average value of 0.491 and 0.587, respectively for Allen & Pruit and Snyder model. Therefore, based on GOF statistics and visual comparison, ET0 values computed using Allen and Pruitt model have very close agreement with the FAO56-PM method, and therefore, Allen & Pruitt model should be naturally a preferred choice as compared to Snyder (1992) model for ET0 computations for semi-arid climatic conditions. ACKNOWLEDGMENTS: The authors wish to thank the Department of Agricultural Meteorology, Anand Agricultural University Anand, Gujarat, India for providing the necessary weather data to carry out this study. REFERENCES Allen, R. G. (1986). “A Penman for all seasons”, J. Irrig. Drain. Eng., Vol. 112(4), pp. 348–368. Allen, R. G., and Pruitt, W. O. (1991). “FAO-24 reference evapotranspiration factors”, J. Irrig. Drain. Eng., Vol. 117(5), pp. 758–773. Allen, R.G., Pereira, L.S., Raes, D., Smith, M. (1998). “Crop evapotranspiration: guidelines for computing crop water requirements”. FAO Irrig. and Drain. Paper No. 56, Food and Agricultural Organization of the United Nations, Rome. Allen, R.G., Smith, M., Pereira, L.S., and Perrier, A. (1994a). “An update for the calculation of reference evapotranspiration”, ICID Bull. Vol. 43(2), pp. 35– 92. , R.G., Smith, M., Perrier, A., and Pereira, L.S. (1994b). “An update for the definition of reference evapotranspiration”, ICID Bull. Vol. 43(2), pp. 1–34. Burman, R.D., Nixon, P.R., Wright, J.L., and Pruitt, W.O. (1980). “Water requirements”. In: Jensen ME (ed) Design and operation of farm irrigation systems, Monograph No. 3, American Society of Agricultural Engineers, St. Joseph, Mich. pp. 189–232. Doorenbos, J., and Pruitt, W.O. (1975). “Guidelines for prediction of crop water requirements”. FAO Irrig. and Drain. Paper No. 24, Rome.

Gavilan, P., Berengena, J., and Allen, R.G. (2007). “Measuring versus estimating net radiation and soil heat flux: impact on Penman–Monteith reference ET estimates in semiarid regions”, Agric. Water Manage., Vol. 89(3), pp. 275–286. Howell, T.A., Phene, C. J., and Meek, D.W. (1983). “Evaporation from screened Class A pans in a semi-arid climate”, Agric. Meteorol. Vol. 29, pp. 111–124. Irmak, S., Haman, D. Z., and Jones, J. W. (2002). “Evaluation of Class A Pan Coefficients for Estimating Reference Evapotranspiration in Humid Location1”, J. Irrig. Drain. Eng., Vol. 128(3), pp. 153-159. Itenfisu, D., Elliot, R. L., Allen, R. G., and Walter, I. A. (2000). “Comparison of reference evapotranspiration calculations across a range of climates”, Proc., National Irrigation Symp. Jensen, M. C., Middleton, J. E., and Pruitt, W. O. (1961). “Scheduling irrigation from pan evaporation.’’ Circular 386, Washington Agricultural Experiment Station Jensen, M. E., Burman, R. D., and Allen, R. G. (1990). “Evapotranspiration and irrigation water requirements.’’ ASCE manuals and reports on engineering practices No. 70., New York. Lopez-Urrea R., Martín, de Santa Olalla, F., Fabeiro, C., Moratalla, A. (2006). “Testing evapotranspiration equations using lysimeter observations in a semiarid climate”, Agric. Water Manag., Vol. 85, pp. 15–26. Pereira, A.R., and Pruitt, W.O. (2004). “Adaptation of the Thornthwaite scheme for estimating daily reference evapotranspiration”, Agric Water Manag., Vol. 66(3), pp. 251–257. Pruitt, W. O. (1966). “Empirical method of estimating evapotranspiration using primarily evaporation pans”. Proc., Conf. on Evapotranspiration and Its Role in Water Resources Management, American Society of Agricultural Engineers, St. Joseph, Mich. Sabziparvar, A-A., Tabari, H., Aeini, A., and Ghafouri, M. (2012). “Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates”, Water Resour. Manage., Vol. 24, pp. 909–920. Singh, P.K., Patel, S.K., Jayswal, P., and Chinchorkar, S.S. (2013). “Usefulness of Class A Pan Coefficient Models for Computation of Reference Evapotranspiration for a Semi-arid Region”. MAUSAM, IMD New Delhi, (article in press). Snyder, R. L. (1992). “Equation for evaporation pan to evapotranspiration conversions”, J. Irrig. Drain. Eng., Vol. 118(6), pp. 977–980. Ventura, F., Spano, D., Duce, P., and Snyder, R.L. (1999). “An evaluation of common evapotranspiration equations”, Irrig. Sci. Vol. 18, pp. 163–170.

Doorenbos, J., and Pruitt, W.O. (1977). “Guidelines for prediction of crop water requirements”, FAO Irrig. & Drain. Paper No. 24 (revised), Rome.

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Assessment of water availability and to device interventions for matching water supply with Climate change and Agricultural production system demands Neelakanth, J.K., E-mail Id:[email protected]

Rajkumara,S., Ashok, P. Gundlur, S.S, Dasr, G.V Water Management Research Centre , Belvatigi, UAS, Dharwad ,Anand- 388 110 Email: [email protected] ABSTRACT Attempts were made to intervene through improved technologies developed irrigation management strategies to educate farmers to avoid excess irrigation. Excess irrigation to crops favors leaching and flushing of salts into the lower horizons, affects nutrient balance in the soil profile and causing changes in the fertility of the soil. This also leads to soil erosion in to the drains and finally choking them. The objective of the research on the farmers fields/ sites is to prevent excessive irrigation, maintain proper nutrient balance, check the process of soil erosion and reduce head tail difference so that farmers are educated on proper on–farm water resource management. On–farm water management research was conducted for two years ( 2010-11 and 2011-12) in the 12-L of 13th Distributary of Malaprabha Right Bank Canal (MRBC) at Hebsur village, Hubli taluka of Dharwad district (Karnataka state). The study area comes under the Malaprabha command with an area of 1585 ha with 13 sub-distributaries with design discharges ranging from 0.0377 cumecs to 0.19 cumecs. This area is situated in the head end of the Command and farmers are assured of getting irrigation water in the cropping season. The study was aimed to find out the crop water demand and availability of canal water supply in the selected area in order to plan and adopt improved water management technologies on the farmer’s fields. Enhancing water productivity by adopting water saving technologies for close growing as well as broad spaced crops demonstrated on farmers field.

design, analysis and interpretation of data for ‘On-Farm Water Management research’. Also some water saving techniques in crop production to be adopted in On-Farm water management research has been suggested (Alien. 1998)

KEY WORDS: On farm water management, Water management, 80% cutoff for border strip, (AAF) alternative to alternative furrow method irrigation

INTRODUCTION On-Farm Water Management Research is necessary to improve the operation and management of Irrigation management is not only essential for irrigation project with limited water supplies but also in those, which have adequate or abundant supplies. In the latter, waterlogging and degradation of physical and physicochemical characteristics of irrigation soils have often been encountered, besides wastage of costly water. In India, during the last three decades, considerable effort has gone into the development of suitable irrigation water management practices. Most of the research has, however, been conducted on experimental research station under controlled condition. Also, the findings have not been tested on a large scale under farmers’ field conditions which impose their own operational constraints. Appropriate modifications are needed when these are implemented in a live irrigation system. This paper deals with guidelines for site description, data collection, statistical

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On-farm water management research In order to obtain research results relevant to farmers needs, it is necessary to gather information on efficient and optimal utilization of production inputs and physical environment under farmers fields conditions. The on-farm research is probably the answer to rapidly transfer the improved water management technology to the farmers and irrigation system managers. The On-Farm Water Management research is to be conducted in a live irrigation system under the operational constraints of farmers coming under the command of a full hydraulic unit such as an outlet, subminor, minor or distributor . The major objectives of onfarm water management research are (Rajput,R.K 1993)

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230

a)

To diagnose and analyse operational constraints in efficient use of water and to increase agricultural production. b) To test & demonstrate the improved water management technology developed at the experimental station in farmers fields. c) To assess the impact of improved water management technology on agricultural production, efficiency of water use and economic condition of farmers. d) To get feedback on problems of farmers and irrigation system hindering efficient water use and increase in agricultural production. e) To generate more effective, location-specific, soil and water management technology for direct local adoption to facilitate efficient use of production resources.

Water delivery characteristics of irrigation system To assess the water delivery characteristic of the irrigation system and to analyse the total water availability in the distributory command in respect of spatial and temporal variations, detailed information on opening & closing of the distributory and minors (Selected ones only) be made for the preceding 3-4 years. The volume of water delivered and area irrigated in the sub-command of selected irrigation minors may be recorded indicating the frequency of irrigation given to different crops. Water availability from other resources such as tanks, wells and tube wells for conjunctive use may also be identified indicating the quantity and frequency of water availability from these sources. (Rajput, 1986)

Assessment of improved water management technology impact The implementation of On-Farm Water Management Research (OFWM) may comprise (a) Intervention with full package of improved water management practices along with all other production inputs at optimum level and water use efficient cropping system and (b) Component Technology Trials.

different reaches of the distributory. The impact of technology package intervention in the study outlet(s) as measured by crop yields, water use, net economic returns and other parameters may be compared with the same parameters monitored in the adjoining outlet in which traditional water management and crop cultivation practices have been adopted (control or check). In the outlets where single crop has been grown in the entire sub-command, the difference in crop yield in study and control outlets may surface but if more than one crop are cultivated in the outlets, then assessment of net monetary returns from study and control outlets may have to be resorted to besides difference in respective crop yields. Yield variation for the same crop at the input use between different outlets of the distributory may have to be analyzed and correlated to physical factors such as soil type, irrigation water availability, drainage situation etc. For estimating the crop yields in the outlets, adequate number of sample (12-15 No.) may have to be drawn randomly from fields of different farmers by adopting standard crop cutting technique. For determining the saving in irrigation water use in the study and control outlets, complete account of input of water (irrigation + rainfall + seepage from adjoining area) and outflow of water (surface runoff from the converging point and seepage if any) from the outlet command, may have to be kept. MATERIALS AND METHODS Command area and discharge details The distributaries selected for the study was 12-L distributaries of Malaprabha Right bank Canal (MRBC) and it was having design discharge rate of 163.0 cusecs with a total command area of 10287 ha, length 24 km and it consists of 29 sub-distributaries with design discharges ranging from 1.0 to 3.0 cusecs covering villages of Hebbal, Alagawadi, Gobbaragumpi, Belvatagi, Amaragol, Shanawad and Navalgund of Dharwad district, Hanchinal and Chulki villages of Belgaum district and Kanakikoppa, Siddapur, Jagapur and Hunsikatti villages of Gadag District were also covered. The selected study area at village Hebsur has a command area of 1196 ha. The site location of Operational Research Project (ORP) Hebsur area is Latitude 150 49’ Longitude 750 70’ Dharwad, Karnataka, India.

Intervention with full package The package interventions are to be made in the entire sub-command of one or two outlets selected in different reaches of the minor i.e head, middle & tail and intern as per location of minor-head, middle and tail reaches of the selected distributory. Thus, in nine outlet of the distributory, water management technology may be superimposed to account for the variability occurring in

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231

IIrrigation. Durring year 20100-11 and 20111-12 there waas s slight deviationn with respect to t normal rainffall.

R RESULTS A AND DISCU USSIONS

Karnaataka Statte

Operationall research prooject (ORP) studyy area at Malaprabhaa command

The expperiment was conducted foor two years from 2010 and 2011 by b All India Co-ordinated Research Project on Water W Manageement, Water Managem ment Research Centre, C Belvattagi. The soil was Chromusterts with clay in structuure. The soil pH was 8.3 with EC 0.1 18 dS/m and orrganic carbon was 0.55% %. The availaable P2O5 was 29 kg/ha and available K2O 857 kg/haa. The field caapacity of the soil was 40.0% 4 and wiltting point 21.6 per cent. The data obtaiined on variou us characters were w subjected to vÀhe data d was analy yzed with spliit plot design replicatedd thrice analysis and interpreetation of the data was carried out in n accordance to Panse and Sukhatmee (1985). Po otential evapootranspiration (ET ) wass determined from f pan evapporation data.

Table 1. Croop water dem T mand and canal water s supply detaills of 13-R su ub distributary of Malaprbha Command during d khariff and rabi 2 2010-11 and 2011-12. Refer paage 234

0

U. S. classs A pan was installed nearr the research site to reecord daily pan p evaporatioon. The pan evaporatioon method meaasures the evapporation from the openn water surfaace, taking into i account cumulativve effect of rad diation, wind, humidity and temperatuure. (Jensen, M.E.1980).

The results r of the study revealeed that, excesss water applied during 2010-11 kharif seassons for maize w a onion cropps was 9.05 percentage and p andd rabi 2010-11 c chickpea, wheaat and sunflow wer 13, 15 and 16 percentagee. T excess waater applied during The d 2011-122 kharif maizee, c cotton and onion o was 9.00, 9.1 and 9..0 percent andd w whereas in 20111-12 rabi chiick pea, wheat and sunfloweer f 9,16 and 18 for 1 percent resppectively. Thhe excess wateer c irrigate ann additional over all area 6.27 ha and 10.377 can h in kharif annd rabi seasonns of 2010-11 and 6.5 ha andd ha 1 12.2 ha duringg kharif and rabi seasons of 2011-12. 2 (Khoot e et.al 2011)

Crop waater demand d The

relationship

between

ET T

0

and

pan

evapooration with, K values baseed on percent c

growiing season werre use to determ mine ET ET T =K×K ×E c

c

p

Thee crop water demand d in thee selected ORP P aarea was estim mated and Depicted in Table-11. The seasonaal w water requirem ment of variouus crops grow wn. The wateer d demand for thee selected cropps was estimateed based on the e evapotranspirat tion of the crop c ( ET crop). The totaal a amount of water w required for the variious crops foor i irrigation durinng the kharif and rabi (2010-11) waas 1 1,64,859 and 1,35,236 1 cum. The total am mount of wateer r required for thhe various croops for irrigation during the k kharif and rabi r and (2011-12) 1,677,374 cum andd 1 1,29,629cum r respectively. T water requuirement duringg The k kharif season was w higher com mpared to the rabi r season due t cultivation of to o high water requiring r cropss like maize byy m most of the faarmers . The gross canal supply duringg k kharif and rabbi (2010-11), was w nil due to flood failure a and 2,85,000 cum. During kharif and rabi (2011-122) g gross canal suppply was 2,266,935 cum andd 2,55,456 cum m r respectively. Net canal suppply during khharif and rabbi ( (2010-11), wass nil, 2,42,2500 cum, and khharif and rabbi ( (2011-12) w was 1,07,895 cum and 2,17,138 cum m r respectively. Itt was revealedd from the abbove two yearrs r results that on an average exxcess quantity of 76,967 cum m i irrigated water given by the farmer. f

C.

p

Where, W E = Pan ev vaporation (mm m/day) p

K = Pan co o-efficient. p

Kc = Crop co oefficient.

Table 2: Irrigation waterr applied, irrrigation T r recommende ed and excesss application n of water i the commaand of 12-L sub in s distributtaries duringg k kharif and raabi (2010-12)).

Climate Wheeatear update will be recorded by Heebsur substation of Natural Disaster Monitoring M Ceentre, Bangalore. During everry Kharif andd rabi seasonn the weather data d also considered for the t schedulingg of

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J. K. Neelakkanth

Refer paage 235

232

Hence, the percent excess irrigation given by famers compared with recommended water requirement was observed that 9.05, 14.60, 9.05 and 14.33 during kharif and rabi (2010-12) depicted in Table-2. Overall excess irrigation given by famers over recommended water requirement was observed that 11.75 percentage.

SUMMERY AND CONCLUSIONS On-Farm Water Management Research on farmer’s field is the only answer for rapid transfer of improved water management technology to farmers and to irrigation managers for ensuring efficient water use and increasing agricultural production with sustained soil productivity in the irrigation commands. The furrow irrigation and border strip irrigation method is the popular method of irrigation for wide spaced crops and close growing crops respectively. 30 percent of irrigation water can be saved by adopting AAF (alternate to alternate furrow) for wide spaced crops like Maize crops. 20percent of water can be saved by adopting the 80 percent cutoff for border strip irrigation method of irrigation. With improvement in the irrigation system management, the water users associations which are not working properly have to be oriented for water management strategies in the command area. Maintenance and operations in the canal off take systems canal regulators etc., are to be upkeep by farmers associations under the technical guideline of site engineers and scientists. This may result in higher yields of individual crops as well as of crop sequence thereby resulting in increased water-use-efficiency

workshop on potassium nutriation in enhancing yield and quality of crop, January 17-18 at UAS, Dharwad, pp. 142-143. Panse VG, Sukhatme, PV (1985). Statistical Methods for Agricultural Workers. Indian Council of Agricultural Research, New Delhi, India. Rajput, R.K.,1986. Water Management in rice. Paper presented in the Workshop of ICAR Rive Improvement Project, held at N.D. Univ.Agric..& Tech.Fezabad, April 14-17. Rajput, R.K 1993 Methododlogy For on-farm Water Management Research –An Approach paper, J. Water Management, 1(2) : 79-85 (1993)

REFERENCES Alien, 1998. Irrigation efficiencies in the farm irrigation system: Water as a constraint. Univ. of Agric. Faisalabad. Jensen, M.E.(1980). Design and operation of farm irrigation systems. Monograph 3. Amer. Soc. of Agric. Engg. Michigan, USA. Khot, A.B, Prabhakar, A.S., Chetti, M.B and Janawade, A.D., (2011), ‘Performance of cotton to varied water supply situations & water saving techniques in vertisols’. Proceedings of National

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Table 1. Crop water demand and canal water supply details of 13-R sub distributary of Malaprbha Command during kharif and rabi 2010-11 and 2011-12.

Particulars 1. Net crop water demand, m3 2. Gross canal supply, m

3

3. Net canal supply, m3 4. Effective rainfall, m

3

5. Contribution from Ground Water, m3 3

6. Balance, m

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Kharif 2010-11

Rabi 2010-11

Kharif 2011-12

Rabi 2011-12

164859

1,35,236

2,67,374

1,29,629

nil

2,85,000

2,26,935

2,55,456

nil

2,42,250

2,07,895

2,17,138

134663

-

1,30,020

44,611

-

-

-

-

(-)30196

(+)1,07,014

(+)70,541

(+)1,32,120

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Table 2: Irrigation water applied, irrigation recommended and excess application of water in the command of 12-L sub distributaries during kharif and rabi (2010-12).

Sl. No

Crop

Area (ha)

Irrigation numbers (No)

58.50

2

Irrigation Method

Applied (m3)

Quantity of water Recom- Excess Per cent mended (m3) Excess over (m3) recommended (%)

Kharif 2010-11 1. 2.

Maize Onion Total Rabi 2010-11

6.38 64.88

2

Chick pea

45.50

3

Wheat 17.38 Sunflower 2.0

4

1. 2. 3.

Total Kharif 2011-12 1. 2. 3.

Border strip Border strip Furrow

77220

70200

7020

9.1

8,166

7,656

510 mean

9.0

81900

12285

13

49359 5760

41712 4800

7647 960

15 16 14.60

mean

2

Cotton

74.88

4

75.74

9.05

94185

64.88

489.14

Onion

Border strip

4

Maize

Total

Furrow

2

Furrow Furrow Border strip

645664

586968

58696

9.0

197683

179712

17971

9.1

8,166

7,656

510

9.0

mean

9.05

639.76

Rabi 2011-12 1. 2. 3. Total

Chick pea

19.46

3

Wheat

12.96

4

Sunflower

32.44 64.88

4

Border strip Border strip Furrow

38511

35010

3501

9

37382

31152

6230 18172

16

96052

77880

18 mean

Over all excess

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J. K. Neelakanth

14.33 11.75 percentage

235

Long Range Forecast of South-West Monsoon Rainfall for 2013 for different Regions of Gujarat Prof.Sachin.S.Chinchorkar Polytechnic in Agricultural Engineering,Anand Agricultural University,Dahod- 389 151 Email: [email protected] Prof.V.B.Vaidya Department of Agricultural Meteorology,BACA, ,Anand Agricultural University,Anand- 388 110 Email: [email protected] Prof.Vyas Pandey Department of Agricultural Meteorology,BACA, ,Anand Agricultural University,Anand- 388 110 Email: [email protected] ABSTRACT The large spatial variability in monsoon rainfall over India demands for regional models for predicting the seasonal rainfall. Hence, models were developed for predicting seasonal (June-September) rainfall of three regions (north, middle and south) of Gujarat using multiple regression technique. The monthly weather data of 30 years of Anand (1980-2009), 22 years (1987-2009) of Navsari and 27 years (1983-2009) of SK Nagar were used. The models were validated with independent data set of four year (2006-2011). The best models were selected based on higher R2 and lower model error. Four models were obtained; 2 for Anand (middle Gujarat) and one each for SK Nagar (north Gujarat) and Navsari (south Gujarat). Anand (Model-1) has showed 8.5% error (2006 to 2011) while, model-2 shows -0.16% error. S K. Nagar station (North Gujarat) has shown the -3.8% error. Navsari (South Gujarat) station has shown -5.6% error. Since all models have shown less than 10% error, hence above operational models can be used for the rainfall forecast of monsoon. Results suggested that for Model 1 (November to March) is predicting 1054.0 mm (Seasonal normal rainfall is 796.0 mm) higher rainfall than the normal by 32.4%. Model 2 (March to May) is predicting 1454.7mm is higher than normal by +82.7 % (Seasonal Normal 796 mm). For north Gujarat SK Nagar station is predicting 778.5mm rainfall which is more by 31.5% than normal (591.1 mm) . In south Gujarat Navsari station is predicting 1646.6 higher (normal rainfall is 1363.0 mm) by 20.8%

KEY WORDS: Multiple regression, rainfall forecasting, rainfall analysis, statistical model.

BACKGROUND South-west monsoon rainfall (received during June to September) determines the fate of dry land farmers as well as the status of national food security in India almost every year. The need for information about south-west monsoon rainfall is great in these areas. An accurate longrange forecast can help farmers increasing agricultural productivity in good rainfall years and negate the sudden downturns in agricultural production during anticipated drought years by giving farmers sufficient time to adopt drought resistant crop varieties and appropriate crop, soil and water management practices. The Indian meteorological Department (IMD) is now able to make all- India long- range forecasts of south-west monsoon rainfall accurately using power regression model based on 16 regional and global parameters from 1988 onwards. The India Meteorological Department (IMD) has been issuing operational long- range forecasts for summer

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monsoon rainfall for more than one century. Since 1988, the operational forecasts have been issued using the 16 Parameter Power Regression and Parametric models for the summer monsoon rainfall over the country as a whole (Gowariker et al., 1991). For review of these operational forecasts and other related research efforts and problems, Thapliyal and Kulshrestha (1992), Krishna Kumar et al. (1995) and Hastenrath (1995) may also be referred. These forecasts have provided useful information on rainfall fluctuations and abnormalities which have been helpful to the planners. However for a country with inherent spatial variability of monsoon rainfall there would always be some areas of deficient rains even in the best monsoon years or some areas of flood even in worst monsoons (Parthasarathy et al., 1993). Walker (1924), Shukla (1987) and Gregory (1989) suggested that rainfall over several subdivisions of India should be grouped together to deduce area averages for large homogeneous regions. They further showed that the consideration of the local distribution characteristics of seasonal rainfall in dividing the country into homogeneous regions yielded

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better formulae for forecasting than when India was treated as one unit.Indian meteorological department (IMD) was giving long-range rainfall forecast every year on the basis of 16 parameters and now reduces 8 parameters since 2003. IMD’s 8 parameters were 1. Arabian sea (SST), 2.Eurasian Snow Cover, 3. NW Europe temperature, 4. Nino 3 SST Anomaly (Previous year), 5. South Indian Ocean (SST Index), 6. East Asia Pressure, 7. Northern hemisphere 50Hpa wind pattern, 8.Europe Pressure Gradient and July South Indian Ocean 50 Hpa zonal wind and Niño 3.4 SST Tendency are also considered (Rajeevan et al., 2004). The northern districts have a rainfall varying from 51 to 102 cms.As the Tropic of Cancer passes through the northern border of Gujarat, the state has an intensely hot or cold climate. But the Arabian sea and the Gulf of Cambay in the west and the forest covered hills in the east soften the rigors of climatic extremes. Now, Prof. M.C. Varshneya, Ex-Vice-Chancellor, AAU, Anand has given valuable guidance for developing New Operational Models based on multiple regression technique for longrange rainfall forecast based on 25 years meteorological data has made to forecast the rainfall of Anand Location (40 km Periphery) with the help of Department of Agricultural Meteorology, Anand Agricultural University, Anand (Gujarat). The Long Period Average (LPA) of monsoon rainfall for Anand was calculated form 1980 to 2005 (25 years) for this purpose. The present investigation is based on seasonal (June to September) rainfall data for the years 1880 to 2005 (25 years) for the location Anand (Gujarat). The seasonal rainfall data for the same period for Anand have also been considered to examine the extent to which the long-range rainfall forecast was relevant to micro level. Anand Agricultural University has developed models for predicting seasonal rainfall of three regions (North, Middle and South) of Gujarat using Multiple Regression Technique. The monthly weather data of 30 years of Anand (1980-2009), 22 years (1987-2009) of Navsari and 27 years (1983-2009) of SK Nagar were used. The models were validated with independent data set of four year (2006-2009). The best models were selected based on higher R2 and lower model error. Four models were obtained; 2 for Anand (middle Gujarat) and one each for SK Nagar (north Gujarat) and Navsari (south Gujarat). Different models explained 74 to 93% variability in seasonal rainfall with model error ranging between -2.5 to 5.1%. During the validation period the performance of model was quite satisfactory with model error ranging between -12.6 to 2.6%. All the models were used to predict the rainfall for 2010 season. The model gave an idea about the possibility of getting deficient, normal or excess rain in mm which gives an idea about drought. From 2010 onwards, AAU has been using the following statistical models for preparing quantitative and

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probabilistic forecasts of the south-west monsoon rainfall (June – September) a) A 16- parameter statistical forecasting system requiring data up to Nov. to March, for the first forecast using model – I while, a 15 parameter statistical forecasting system requiring data up to March for Anand station. b) A 15- parameter statistical forecasting system requiring data up to Nov to March, for SK Nagar station. c) A 15- parameter statistical forecasting system requiring data up to Nov to March, for Navsari station. Although at many times the SW- monsoon rainfall of a country as whole had been normal but there have been quite a large variation in regional rainfall distribution. IMD has predicted 828 mm rainfall in 2006 for a country as whole (93% of normal), but Gujarat received 1072.8 mm rainfall which was 151.2 % as compared to the normal. While, in 2009 IMD predicted 881.1 mm (99% of normal.), rainfall but Gujarat received 613.7 mm (86.4% of normal) rainfall which was a deficit year. So, it was felt necessary to give regional forecast/ prediction/ local station by using statistical techniques. Thus, there is need to develop models for predicting regional rainfall. 1.

Methodology :-

In 2010, Anand Agricultural University (AAU), Anand has introduced multiple regression parametric models for the Long-range forecast of the south-west monsoon rainfall explored for Anand (Middle Gujarat), S.K. Nagar (North Gujarat) and Navasari( South Gujarat). With this, it has become possible to issue the Long-range forecasts in two stages (i.e. Preliminary and Final). The Preliminary Forecast is issued on 2nd April giving its users an extra lead time of about 40 days. On 22nd may, AAU issues a final forecast for the Anand region. We have developed two models for Anand. In view of its importance for agriculture, for the first time, the development of the new 16 parameters multiple regression parametric models are discussed in this article. The daily data of three stations were converted into weekly means( Meteorological week).This weekly data is then converted into monthly data from November to May (As per the IMD norms) were used to develop for the 3 stations (i.e. Anand, Navsari, and SK.Nagar) by trial and error method. (Varshneya et al., 2010). The model 1 for Anand uses predictors (monthly weather data) from November to March while Model 2 for Anand uses predictors (monthly weather data) from March to May period. The total number of predictors in model 1 was 16 while in Model 2, 15 in number. For Navsari the best model obtained has 13 predictors from March to May period only. For SK Nagar with 15 predictors were from November to March period only. Model 1 for Anand has

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237

shown +8.5% average error when validated with actual rainfall (2006-2011) whereas Model 2 has shown only – 0.2% average error. For SK Nagar average error was 3.5% while, for Navsari it was -5.5% when validated with actual rainfall during 2006-2011 (Table 2). 4. Average Error: - Anand (Model-1) has shown 8.5% Error (2006 to 2011). Model-2 showd -0.16% average error. S K. Nagar (North Gujarat) has shown the -3.8% error. For Navsari (South Gujarat) the error was -5.55%. Since all models have shown less than 10% error, hence above operational models can be used for the rainfall forecast of monsoon. 5. Forecast for the Year-2013 : The validated models were used to forecast the seasonal rainfall i.e. JuneSeptember for year 2013. It is seen that both the models for Anand predicted higher than the normal rainfall (Table 1). The Model 1 (November to March) is predicting 1054 mm rainfall (Seasonal normal rainfall is 796 mm) which is higher than the normal by 32.4 % (Above Rainfall). Model 2 (March to May) is predicting 1454.7mm is higher than normal by 82.7% (Season Normal 796 mm). For north Gujarat, SK Nagar station the rainfall prediction is 778.5 mm which is higher rainfall i.e. +31.5% more than normal rainfall (591.1 mm). In south Gujarat Navsari station is predicting 1646.6 mm (higher than normal rainfall is 1363.0 mm) +20.8% more than normal rainfall.

Parthasarathy, B., Rupakumar, K., Munot, A. A., (1993) Homogeneous Indian monsoon rainfall: Variability and prediction. Rajeevan, M., D. S. Pai, S. K. Dikshit and R. R. Kelkar. (2004) IMD’s new operational models for long-range forecast of southwest monsoon rainfall over India and their verification for 2003, Current Science, Vol. 86, No. 3, (10 February 2004), pp. 422-431. Shukla, J., (1987): Inter-annual variability of monsoons. In Monsoon, Fein, J. S. and Stephens, P. L. (eds.), 523548. Thapliyal, V., Kulshrestha, S. M., (1992): Recent models for long-range forecasting of southwest monsoon rainfall in India. Mausam, 43, 239-248. Varshneya, M.C., S.S.Chinchorkar., V.B.Vaidya and Vyas Pandey.( 2010) Forecasting Models for seasonal rainfall for different regions of Gujarat. Journal of Agrometeorology 12 (2): 202-207. Walker, G. T., (1924) Correlation in seasonal variation of weather IX: A further study of world Weather. Mem. India Meteorol. Dept (IMD Mem), 24, 275-332

References:Gowariker, V; V.Thapliyal; S.M.Kulshrestha; G.S.mandal; N.Sen Roy; and D.R.Sikka.(1991). A Power regression model for long- range forecast of South-West monsoon rainfall over India. Mausam 42(2):125-30. Gregory, S., (1989): Macro-regional definition and characteristics of Indian summer monsoon rainfall:1871-1985. Int. J. Hastenrath, S., 1995: Recent advances in Tropical climate prediction. J. Climate, 8, 1519-1532. India Meteorology Department (IMD). (2010). Long Range Forecast For 2010 South-west Monsoon Season Rainfall. (Press release), IMD website, p. 1-3.

Krishnakumar, K., Soman, M. K., Rupakumar, K., (1995) Seasonal forecasting of Indian summer monsoon rainfall: A review. Weather, 150, 449-467.

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3 Operational Statistical Forecast System:Statistical Forecasting system for June to September rainfall forecast, the following predictors are used. Sr. No

Name of the station

1.

Anand (Model 1)

2.

3.

4.

Anand (Model 2)

S.K. Nagar ( North Gujarat)

Navsari ( South Gujarat)

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Period Used for the months

R2

Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed and Bright Sun Shine Hour.

March

0.74

Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed and Bright Sun Shine Hour.

April

Maximum temperature, minimum temperature, relative humidity afternoon, Wind Speed and Bright Sun Shine Hour.

May (up to 22nd May)

Relative humidity Afternoon, Wind Speed, Maximum Temperature , Bright Sun Shine Hour, Wind Speed.

November

Maximum Temperature, Relative humidity afternoon, Minimum Temperature.

December

Maximum Temperature, Relative humidity afternoon, Bright Sun Shine Hour, Wind Speed, Minimum Temperature.

January

Maximum Temperature, Bright Sun Shine Hour, Minimum Temperature.

February

Maximum Temperature, Bright Sun Shine Hour, Minimum Temperature.

March

Predictor

Wind Speed, Relative Humidity-II

November

Wind Speed, Maximum Temperature, Relative Humidity-I, Relative Humidity-II

December

Wind Speed, Pan Evaporation, Relative Humidity-I, Relative Humidity-II

January

Pan Evaporation, Relative Humidity-I, Relative Humidity-II

February

Wind Speed, Relative Humidity-II

March

Pan Evaporation, Bright Sun Shine Hour, Relative Humidity-I, Relative Humidity-II

March

Bright Sun Shine Hour, Minimum Temperature, Relative Humidity-I, Relative Humidity-II

April

Bright Sun Shine Hour, Minimum Temperature, Relative Humidity-I, Relative Humidity-II,Maximum Temperature

May (up to 22nd May)

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0.85

0.84

0.93

239

Table 1. Validation of models and prediction for 2013 Sr. No

Station

Rainfall (mm)

2006

2007

2008

2009

2010

2011

1

Anand (model-1) 45-13 mw

Observed Predicted

1358.2 1266.6

1140.7 1079.5

957.4 1251.6

380.9 338.4

922.3 1363

Deviatio n (%) Observed

-6.7

-5.4

30.7

-11.1

1358.2

1140.7

957.4

Predicted

1050.7

1004.9

Deviatio n (%)

-22.6

S K. Nagar

Observed

( North Gujarat)

2

3

4

Anand (model-2) 10-20 mw

Navsari (South Gujarat)

2012

2013

877.8 916

Average Error (%) 939.6 522.2

882.7 813.0

47.7

-4.1

8.5

-7.8

1054.0 mm Normal (796 mm) +32.4%

380.9

922.3

877.8

939.6

882.7

804.5

492.9

1294

1102

479.0

1009.5

1454.7 mm

-11.9

-15.9

29.4

40.3

-20.3

-0.17

14.3

Normal (796 mm) +82.7%

1094.9

585

574

391.6

947

915.3

751.3

368.7

Predicted

1001.7

680.8

546.1

403.9

770

818.6

349.8

743.0

778.5 mm

Deviatio n (%)

-8.5

16.4

-4.9

3.1

-18.6

-10.5

-3.8

-50.3

Observed

1916.2

1852.6

2063.2

1638.7

2033

1507.8

1835.3

1244.0

Normal (591.9 mm) +31.5% 1646.6 mm

1381.0 mm -9.9

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Predicted

1657.5

1913.8

2170.5

1732.3

1529

1367

861.4

Deviatio n (%)

-13.5

3.3

5.2

5.7

-24.7

-9.3

-5.55

Sachin Chinchorkar

Normal (1363) +20.8%

240

Reliability of APHRODITE Reanalysis data used in climate studies for Tapi basin Sadhana Singh PG Student, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

Nishi Bhuvandas Research Scholar, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

P V Timbadiya Assistant Professor, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

P L Patel Professor & Head, Civil Engineering Department, SV National Institute of Technology-Surat, Gujarat, India [email protected]

ABSTRACT In climate change studies, observed values of atmospheric predictors (observed station variables) for a sufficiently long duration is required for efficient future projections. It is difficult to get the observed climatological data in developing countries. In the absence of adequate observed climatological data, the reanalysis data are used in climate change studies. Out of many currently available reanalyses data set, the present study has been carried out to check the reliability of the gauge based high quality Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) database for climate change studies for western part of India mainly the Tapi basin. The APHRODITE annual average and maximum temperature data are compared with corresponding observed India Meteorological Department (IMD) data for three gauging stations in the Tapi basin. The study revealed that APHROODTE data of annual average temperature at Akola and Nandurbar is consistently lower than corresponding IMD data while the same is in good agreement with IMD data at the Surat station. The annual maximum temperature data of APHRODITE are consistently lower than IMD data for all the three gauging stations.

KEY WORDS: Climate studies, Observed data (IMD data); Reanalysis data (APHRODITE data); Tapi basin; Temperature INTRODUCTION In climate impact studies the most commonly focused climatic parameters are temperature, precipitation, wind velocity and relative humidity of region under consideration. For any particular location, both GCM as well as observed set of data are compared while performing the bias correction in climate change study. However, in absence of observed data, the reanalysis databases are used for bias correction. It is important to

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note that the reanalysis data should be reliable enough to carry out the climate change study of the area. Few currently available reanalyses data are from the National Centers for Environmental Prediction (NCEP)(2.5°x2.5°) (Kanamitsu et al., 2002), the European Centre for Medium Range Forecasting (ERA40) (Uppala et al., 2005), the North American Regional Reanalysis (NARR) project, the Japanese 25- year Reanalysis (JRA25) project (Onogi et al., 2007) and the newly available gauge-based high-quality dataset for Asia is APHRODITE (Yatagai et al., 2009). The APHRODITE project develops state-of-theart daily precipitation datasets with high resolution (0.5° and 0.25°) grids for Asia. Due to better grid resolution,

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241

APHRODITE data are used in major climate studies at present. Rajeevan and Bhate (2009) recently compared APHRODITE v0804 (based on about 2000 stations observations over the Indian subcontinent) with the rain gauges from the India Meteorological Department (IMD) (comprising more than 6000 stations). The study revealed that APHRODITE data have high correlation with the IMD rainfall data over the India. Krishnamurti et al. (2009) also recommended the use of APHRODITE, as an improvement to the Tropical Rainfall Measuring Mission (TRMM), over India. Further, the reliability of data from APHRODITE was demonstrated further by Yatagai and Xie (2006), Yatagai et al. (2005; 2009) in climate change study undertaken by them. In present study, the most significant variable for climate change, i.e. temperature, has been chosen to check the reliability of APHRODITE data by comparing it with the observed IMD temperature data of the Tapi basin in India. STUDY AREA The Tapi is the second largest westward draining interstate river basin of India. The Tapi basin covers a large area in the state of Maharashtra besides smaller areas in the states of Madhya Pradesh and Gujarat. It is spread over an area of 65,145 km2, which is about 2% of the total geographic area of the country. It is the northern - most basin of the

Deccan plateau and is situated between latitudes 20°5' N to 22°3' N and longitudes 72°38' E to 78°17' E. The average rainfall in the Tapi basin is 830 mm and the maximum being 2030mm (Bhuvandas et. al, 2013). Owing to topographical characteristics, the climate of the basin is a variable parameter. In winter, the minimum temperature varies from 5°C to 14.5°C. However, lower temperature has also been observed in several areas. The month of May is the hottest and maximum temperature varies from 38°C to 48°C. Fig.1 shows the index map of the present study area. The IMD weather stations are as marked and numbered as 1 to 8. Of these 3 stations, namely, Akola (2), Nandurbar (7) and Surat (8) have been considered for the present study being better continuity of the data for these stations. The IMD observed weather temperature data were procured through Nationally Co-ordinated Project on ‘Development of Water Resources and Flood Management Centre at SVNIT-Surat.’ The vertical and horizontal lines in the Fig.1, represent longitude and latitude forming the grid of resolution 0.25° x 0.25°. After comparing the IMD weather stations bearing no. 2, 7 and 8 with aforesaid grids, the grids considered for analysis are namely S7, H5 and B5 respectively, see Fig.1.

Fig.1 Location of the study area and weather stations

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242

DATA AND METHOD Data Source The temperature data of the three weather stations, as enumerated above, were procured from IMD, Government of India which is the major weather monitoring agencies in the country. The data selected for the present study is based on availability of IMD daily temperature data. The APHRODITE data of daily temperature is available from 1951 to 2007 for the Asia. The brief summary of the point temperature within the Tapi basin for the three stations are included in Table 1. The version V1204R1 of APHRODITE gridded database for the Asian continent has been utilized for the region of India with 0.25° x 0.25° resolution of geographical longitude/ latitude. It is available online from the site http://www.chikyu.ac.jp/precip/. The daily gridded data has been averaged to obtain the annual average temperature values. Similarly, the daily IMD data has been averaged to obtain point annual temperature values. Table 1 Details of IMD temperature data

Station

Longitude (οE) 77º04'

Frequency

Akola

Latitude (οN) 20º42'

Nandurbar

21º20'

74º15'

Daily

Surat

21º12'

72º50'

Daily

Daily

Data period 19902000 19751984 19982006

APHRODITE and IMD annual average temperature data at the Akola gauging station from 1989 to 2001. In 1990, the annual average temperature of both data sets are in close proximity to each other, since then the value consistently increases till the year 1997. The temperature of APHRODITE at the same station for the said period is lower than that of IMD, see Fig. 2(B). Fig. 3(A) depicts the variation of annual maximum and annual average temperature respectively at Nandurbar station during 19982006. Fig. 3(A) and (B) shows that annual maximum temperature data of for aforementioned period is lower than corresponding IMD data at Nandurbar station. Fig. 3 (C) and (D) also indicate that annual average temperature at Nandurbar for 1975 to 1984 are higher than corresponding APHRODITE data. Figs. 4(A) and (B) represent the time series of annual maximum temperature for period 1998 to 2006 extracted from APHRODITE and observed IMD data for the Surat station. Figs.4 (A) and (B) clearly depict that APHRODITE data for period 1998 to 2006 are consistently lower than corresponding IMD data sets. Fig.4 (C) and (D) represent close proximity of APHRODITE and IMD annual average temperature data for period 1998 to 2006 for the Surat gauging station. The annual average temperature range of APHRODITE data is between 32 to 35 ºC where as it is 40 to 44 ºC for IMD data. It can be observed from the same that APHRODITE data bears low values than the IMD data. RESULTS AND DISCUSSION

For the daily temperature data locations of IMD which have been converted to annual average values, the corresponding gridded values of the annual average time series of temperature are extracted from the APHRODITE database for S7, H5 and B5. This is performed by programming in MATLAB software v2013(a). The reliability of the APHRODITE data, have been checked by comparing annual maximum temperature and annual average temperature with corresponding IMD data. The assumption made in this analysis is that, for all IMD point temperature data, the value remains uniform throughout the grid.

From Fig. 2(A), Fig. 3(A) and Fig. 4(A), for all stations considered for study, show that annual maximum temperature of APHRODITE data are consistently lower than corresponding IMD data. From Fig. 2(C) and Fig. 3 (C) it is apparent that annual average temperature data of APHRODITE is consistently lower than the IMD data. However, at the Surat station, annual average temperature from APHRODITE and IMD data are reasonably in close agreement. The divergence of annual average temperature data from both the sources, i.e. APHRODITE and IMD data, at the Ankola and Nandurbar stations may be due to the hilly topography of the region. The closeness in the annual average temperature for both the data at the Surat city may be attributed to the flat region in the vicinity of the gauging station. The Surat area is almost flat while Akola and Nandurbar is having undulating topography.

ANALYSIS

CONCLUSIONS

In the subsequent paragraphs, the time series analysis graph is plotted between IMD (annual maximum and average) temperature values and APHRODITE temperature data. It provides a view to the variation in time series of APHRODITE data with respect to IMD data for the concurrent time. Fig. 2(A) and (B) clearly show that APHRODITE annual maximum temperature falls lower than IMD for about 6°C at the Akola station during 19902000. Fig. 2(C) provides comparison between

The temperature data from the two sources, viz. APHRODITE and IMD, are compared for the three IMD gauging stations in the Tapi basin. The results from the foregoing analyses can be summarized as follows:

Methodology

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1) The annual maximum temperature data of APHRODITE, have been consistently found to be lower than IMD data for all three gauging stations. 2) The annual average temperature data for

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APHRODITE are consistently lower than IMD data for the Nandurbar and the Akola stations. 3) The average annual temperature series of IMD data and APHRODITE data are in good agreement for the Surat city 4) The analyses show that compatibility of IMD data and APHRODITE data are dependent on the topography in the vicinity of IMD gauging stations. 5) Consistent over prediction of IMD data vis-à-vis APHRODITE data may help in applying suitable bias corrections for making data suitable for climate change study. REFERENCES Bhuvandas, N, Timbadiya, PV, Patel, PL, Porey, PD (2013). "Analysis of trends and variability in time series of extreme daily rainfall in Tapi basin, India", Proceedings of 35th IAHR World Congress, A11949, Chengdu, China, Tsinghua University Press, 2013. Kanamitsu, M, Ebisuzaki, W, Woollen, J, Yang, SK, Hnilo, JJ, Fiorino, M, Poter, GL (2002). "NCEP-DOE AMIP-II Reanalysis

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(R-2)". Bulletin of the American Meteorological Society 83: 1631–1643. Krishnamurti, TN, Mishra, AK, Simon, A, Yatagai, A (2009). "Use of a dense rain-gauge network over India for improvingblended TRMM products and downscaled weather models".Journal of the Meteorological Society of Japan 87A: 393–412,DOI:10.2151/jmsj.87A.393. Onogi, K, Tsutsui, J, Koide, H, Sakamoto, M, Kobayashi, S, Hatsushika, H, Matsumoto, T, Yamazaki, N, Jamahori, H, Takahashi, K, Kadokura, S, Wada, K, Kato, K, Oyama, R, Ose, T, Mannoji, N, Taira, R, (2007). "The JRA-25 Reanalysis". Journal of the Meteorological Society of Japan 85: 369–432. Rajeevan, M, and Bhate, J (2009). "A high resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies". Current Science 96: 558–562. Reanalysis". Quarterly Journal of the Royal Meteorological Society 131: 2961–3012. Uppala, SM, Kallberg, PW, Simmons, AJ, Andrae, U, Da, Costa Bechtold, V, Fiorino, M, Gibson, JK, Haseler, J, Hernandez, A, Kelly, GA, Li, X, Onogi, K, Saarinen, S, Sokka, N, Allan, RP, Andersson, E, Arpe, K, Balmaseda, MA, Beljaars, ACM, Van, De, Berg, L, Bidlot, J, Bormann, N, Caires, S, Chevallier, F, Dethof, A, Dragosavac, M, Fisher, M, Fuentes, M, Hagemann, S, Holm, E, Hoskins, BJ, Isaksen, L, Janssen, PAEM, Jenne, R, Mcnally, AP, Mahfouf, J-F, Morcrette, J-J, Rayner, NA,

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Saunders, RW, Simon, P, Sterl, A, Trenberth, KE, Untch, A, Vasiljevic, D, Viterbo, P, Woollen, J. (2005). "The ERA-40 Yatagai, A, Arakawa, O, Kamiguchi, K, Kawamoto, H, Nodzu, MI, Hamada, A (2009). "A 44-year daily precipitation dataset for Asia based on a dense network of rain gauges". SOLA 5: 137–140, DOI:10.2151/sola.2009-035. Yatagai, A and Xie, P (2006). "Utilization of a rain gauge-based daily precipitation dataset over Asia for validation of precipitation derived from TRMM/PR and JRA-25". SPIE 2006, 6404–53, DOI:10.1117/12.723829. Yatagai, A, Xie, P, Kitoh, A (2005). "Utilization of a new gaugebased daily precipitation dataset over monsoon Asia for validation of the daily precipitation climatology simulated by the MRI/JMA 20-km mesh AGCM". SOLA 1: 193–196, DOI:10.2151/sola.2005-050.

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