CC-WARE - South East Europe Programme

CC-WARE

Mitigating Vulnerability of Water Resources under Climate Change WP3 - Vulnerability of Water Resources in SEE

Report Version 4.0

CC-WARE – Mitigating Vulnerability of Water Resources under Climate Change SEE Project, supported by the means of the ERDF (European Regional Development Fund) & by the Instrument of Pre-Accession Assistance (IPA)

Lead Partner: Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, Forest Department (AT) Hubert Siegel, Head of Subdivision and Project Coordinator Contact: www.ccware.eu

Editor of the WP3 report: Barbara Čenčur Curk University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Geology [email protected]

Authors of particular chapters: Part II 1 Climate and climate change Sorin Cheval Part II 2.1.1 2) Water demand, 3) Local water explotation index, 4) Local water surplus in the future Barbara Čenčur Curk, Petra Vrhovnik, Timotej Verbovšek Part II 2.1.1 4) Seasonal variability of local water exploitation index Mathiew Herrnegger, Hans Peter Nachtnebel Part II 2.2 Water quality Prvoslav Marjanović

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WP3 report 30.6.2014

Partners and Persons working in WP3 within the CC-WARE project LP

Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, Forest Department (AT) Hubert Siegel Elisabeth Gerhardt Associated Organisations: University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Forestand Soil Sciences Eduard Hochbichler, Roland Koeck

PP1

Municipality of the City of Vienna, MA31 Vienna Waterworks (AT) Gerhard Kuschnig Associated Organisations: University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Water, Atmosphere and Environment Hans-Peter Nachtnebel, Mathew Herrnegger, Tobias Senoner, Johannes Wesemann University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Forestand Soil Sciences Eduard Hochbichler, Roland Koeck

PP2

Municipality of Waidhofen van der Ybbs (AT) Markus Hochleitner University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Forestand Soil Sciences Eduard Hochbichler, Roland Koeck

PP3

University of Ljubljana (SI) Faculty of Natural Sciences and Engineering, Department of Geology Mihael Brenčič, Barbara Čenčur Curk, Timotej Verbovšek, Nina Zupančič, Petra Vrhovnik, Petra Žvab Rožič

PP4

Public Water Utility Ljubljana JP Vodovod-Kanalizacija d.o.o. (SI) Branka Bračič Železnik

PP5

National Institute for Environment (HU) László Perger, Agnes Tahy, Gyorgy Tóth Associated Organisations: Technical University of Budapest, Department of Sanitary and Environmental Engineering Zoltan Simonffy, Tamas Acs Eötvös Lorand University, Department of Meteorology Istvan Bogardi

PP6

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National Forest Administration (RO) Adam Crăciunescu, Ion Codruţ Bîlea, Petrişor Vică


Associated Organisations: Petroleum-Gas University of Ploieşti – Faculty of Petroleum Refining and Petrochemistry, Department: Engineering of Petroleum Processing and Environmental Protection - Ion Onuţu , Caşen Panaitescu Forest Research And Management Institute Bucureşti - Cristinel Constandache Plobil Consulting Ploieşti -Aurel Bilanici

PP7

National Meteorological Administration (RO) Sorin Cheval

PP8

Executive Forest Agency (BU) Albena Bobeva, Lubcho Trichkov, Denitsa Pandeva Associated Organisations: National Institute of Meteorology and Hydrology Valery Spiridonov, Irena Ilcheva, Krasimira Nikolova, Snejanka Balabanova

PP9

Thessaloniki Water Supply & Sewerage Co sa (GR) Spachos Thomas

PP10

Decentralised Administration of Macedonia and Thrace, Water Directorate of Central Macedonia (GR) Konstantinos Papatolios, Stelios Michailidis, Charicleia Michalopoloy Associated Organisations: Aristotle University of Thessaloniki, Civil Engineering Department Margaritis Vafeiadis

PP11

Regional Agency for Environmental Protection in the Emilia-Romagna region (IT) Marco Marcaccio, Demetrio Errigo, Donatella Ferri, Franco Zinoni Associated Organisations: - University of Modena and Reggio Emilia, Department of Chemical and Geological Sciences Alessandro Corsini, Francesco Ronchetti, Margarit Nistor - University of Bologna, Department of Civil, Chemical, Environmental and Materials Engineering Lisa Borgatti, Federico Cervi, Francesca Petronici

IPA1

Jaroslav Cerni Institute for the Development of Water Resources (RS) Dejan Dimkić, Prvoslav Marjanović Associated Organisations: University of Belgrade, Faculty of Mining and Geology, Department of Hydrogeology Zoran Stevanović

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Acknowledgements We want to express our gratitude to the Lead Partner, to all Project Partners and to the whole CC-WARE project consortium for the project funding, for the spririt of transnational cooperation and for the unbending efforts for accomplishing all project goals. Furthermore we want to express our appreciation to the European Union, to the ERDF (European Regional Development Fund) and to the IPA (Instrument for Pre-Accession Assistance) for their support of CC-WARE.

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TABLE OF CONTENTS PART I – Description of the Workpackage 3 (WP3) ................................................................ 8 1

WP3 Structure ................................................................................................................. 8

2

Partners Involved in WP3 ................................................................................................ 9

3

WP3 meetings ................................................................................................................. 9

4

WP3 Outputs ................................................................................................................... 9

PART II – Vulnerability of Water Resources in SEE ............................................................... 11 1

Climate and climate change .......................................................................................... 14

1.1

Determination of climate indicators ......................................................................... 15

1.2

Homogeneous areas .................................................................................................. 18

2

Water resources sensitivity to CC ................................................................................. 23

2.1

Water quantity .......................................................................................................... 23

2.1.1

Water quantity sensitivity indicators ................................................................. 23

1)

Local total runoff ....................................................................................................... 24

2)

Water demand ........................................................................................................... 25

3)

Local water exploitation index (LWEI) ....................................................................... 32

4)

Seasonal Local water exploitation index (LWEI)........................................................ 34

5)

Overall Water Quantity Sensitivity ............................................................................ 39

6)

Local Water Surplus in the future (LWS) ................................................................... 41

2.2

Water quality ............................................................................................................. 41

2.2.1

Water quality sensitivity indicators.................................................................... 42

3

Adaptive capacity .......................................................................................................... 66

4

Integrated assessment of water resources vulnerability to climate change ................ 68

4.1

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References ................................................................................................................. 70


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PART I – Description of the Workpackage 3 (WP3) 1

WP3 Structure

WP3 is divided into three activities: ACT 3.1 – Climate change as vulnerability indicator ACT 3.2 – Evaluation of water quantity and quality vulnerability ACT 3.3 – Integrated assessment and classification of drinking water risks under CC The WP3 structure is presented in Table 1. Yellow fields illustrate WP and ACT leader. Table 1: The WP3 structure.

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2

Partners Involved in WP3

All partners of the CC-WARE projects were involved in WP3. Partners and persons working on WP3 are listed in the beginning of the report. There is an exception in ACT3.3. where PPT was not invoved.

3

WP3 meetings

For WP3 three meetings were planned in the preparatory phase: - TWG 3.1: 17 April 2013 in Vienna, AT, - TWG 3.2: 14 – 15 October 2013 in Beograd, SRB and - TWG 3.3: 22 January 2014 in Budapest, HU. Regarding experiences from other projects a need for more joint work always arises. Discussion regarding WP3 was held also on: - Plenary Workshop in Thessaloniki, GR (4 - 6 June 2013) and - SC and KT1 in Modena, IT (18 - 20 March 2014) – presentation and discussion of WP3 outputs. There were several workshops among the WP3 and WP4 leader groups: - Belgrade, SRB (12 – 13 October 2013), - Belgrade, SRB (9 – 11 January 2013) and also two additional workshops for finalizing WP3 with broader working group: - Radenci, SI (6 – 7 March 2014), - Radenci, SI (5 – 6 June 2014).

4

WP3 Outputs

Qualitative and quantitative description of the WP3 outputs and results from the Application Form (AF) are listed in Fehler! Verweisquelle konnte nicht gefunden werden..

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According to AF ten outputs should be delivered (Fehler! Verweisquelle konnte nicht gefunden werden.). Important remark: In the AF it was forseen that for each single activity a separate report will be done. All these reports are now incorporated in the final WP3 report, which is presented here. Each activity and subactivity is presented in separate chapter. According to AF there are two additional deliverables – workshops reports form two additional workshops for finalizing WP3, which were held in Radenci, SI (6 – 7 March 2014 and 5 – 6 June 2014). Table 2: Outputs of WP3. ACTIVITY

OUTPUT

CONTRIBUTING

REALIZATION

PARTNERS 3.1

Map of homogeneous reference areas in SEE according to the selected indicators

3.2

Common

methodology

vulnerability

mapping

water

quantity

and

(considering

on

climate

change

quality and

LP,PP1,3,4,5,6,7,8, 9,10,11, IPA1

Chapter 1.2

LP,PP1,3,4,5,6,7,8, 9,10,11, IPA1

Chapter 2

LP,PP1,3,4,5,6,8,9, 10,11, IPA1

Chapter 2

LP,PP1,3,4,5,6,8,9, 10,11, IPA1

Chapter 2

socio¬economic conditions in the present and future) 3.2

Report on a common methodology for selecting indicators (climatic, hydrological, geographical and socio-economic) for water

quantity

transnational

and

SEE

quality

vulnerability

vulnerability map

determination

(quantity,

quality

considering climate change and socio-economic conditions in present and in the future) 3.2

Transnational

SEE

vulnerability

map

(quantity,

quality

considering climate change and socio-economic conditions in present and in the future) 3.3

Joint methodology for integrated vulnerability mapping

LP,PP1,3,4,5,6,7,8, 9,10,11, IPA1

Chapter 3

3.3

Integrated vulnerability map for drinking water resources in SEE

LP,PP1,3,4,5,6,7,8, 9,10,11, IPA1

Chapter 3

region

Maps are also available separately as pictures and GIS files.

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PART II – Vulnerability of Water Resources in SEE Concern about the potential effects of climate change on water supply and water demand is growing. Water resources vulnerability is a critical issue to be faced by society in the near future. Current variability and future climate change are affecting water supply and demand over all water-using sectors. Consequently, water scarcity is increasing. The objective of WP3 is to assess present and future vulnerability of water resources based on a jointly elaborated methodology. In particular the work package will focus on the identification of drivers influencing vulnerability, the evaluation of the vulnerability of water resouces as well as the assessment and classification of drinking water risks under climate change. Vulnerability of freshwater resources is characterised by several indicators: describing water availability and increasing demand and the future qualitative state of the system compared to drinking water standards. Land use may significantly influence the quantity of the water resources, water demand and overall water quality. Methodology for determining water resources vulnerability regarding quantity and quality shall take into account also extreme natural events and the multiple impact of the land use. By classifying the water resources vulnerability, critical areas can be identified, where water resources stay under risk. The knowledge of the areal distribution of vulnerable water resources is an important prerequisite for sustainable management of the relevant areas. Vulnerability is the degree, to which a system is susceptible to or unable to cope with, adverse effects of climate change (IPCC, 2003). In the light of this definition the climatic, hydrological, geological and socio-economic factors influencing vulnerability need to be identified and appropriate indicators selected. The goal of this activity is to identify drivers influencing vulnerability, resulting in a commonly agreed set of various indicators. The Intergovernmental Panel on Climate Change (IPCC) describes vulnerability as a function of impact and adaptive capacity and 'the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate variation to which a system is exposed, its sensitivity and its adaptive capacity' (IPCC 2007). The methodology applied in CC-WARE builds on this description of vulnerability by examining the exposure (predicted changes in the climate), sensitivity (the responsiveness of a system to climatic influences) and adaptive capacity (the ability of a system to adjust to climate change) of a range of indicators in a SEE region. Exposure, sensitivity, potential impact and adaptive capacity (Figure 1) are all considered in the evaluation of vulnerability to a defined climate change stressor such as temperature increases (Local Government Association of South Australia, 2012).

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Figure 1: Components of Vulnerability (Local Government Association of South Australia, 2012)

Exposure is the changes expected in the climate for a range of variables including temperature and precipitation. Sensitivity is the degree to which systems respond to the changes. For example less precipitation may reflect in substantial reduction of water availability in a small watershed. Adaptive capacity describes how well a system can adapt or modify to cope with the climate changes to which it is exposed to reduce harm. Examples of natural systems with low adaptive capacity are those with a limited gene pool and as a result a limited capacity to evolve, over extraction of ground or surface water, salinity or environmental pollutants that do not have the resilience to adapt. Economic systems that have minimal opportunities to increase income would also struggle to adapt to climate changes. Social systems that are disrupted have poor communication networks etc. are also likely to be limited in their capacity to adapt. When the adaptive capacity of a system is reduced, it is considered to be more vulnerable to the impacts of climate change. By considering adaptive capacity it is possible to avoid attending to impacts that may be reduced by the system itself with minimal outside help, or putting systems that have no capacity to adapt as a low priority with the result that more harm occurs than expected. (Local Government Association of South Australia, 2012) From water resource management perspective, vulnerability can be defined as: the characteristics of water resources system’s weakness and flaws that make the system difficult to be functional in the face of socioeconomic and environmental change (UNEP 2009). Thus, the vulnerability should be measured in terms of: (i) exposure of a water resources system to stressors at the river basin scale; and (ii) capacity of the ecosystem and society to cope with the threats to the healthy functionality of a water system (UNEP 2009). Vulnerability corresponds to changes, which can be compared to a reference situation (e.g. differences between the past/present and future state). However the determination of the changes needs the estimation of the present and the future values of the relevant indicators. Besides, vulnerability cannot be measured, but can be assessed with the help of indicators. During the CC-WARE workshop discussion it was decided to use “Overlay/index method” for assessment of vulnerability on a national scale (FOOTPRINT 2006). This method is easier to

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understand than the more complex physical based models and therefore more suitable to use for none-modelers and also more appropriate to enhance the participatory process. To discriminate between different levels of vulnerability (e.g. three classes low/moderate/high), it is necessary to combine all quantities into a single measure.

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1

Climate and climate change

The climate is the main natural driver of the variability in the water resources, and atmospheric precipitation, air temperature and evapotranspiration are commonly used for assessing and forecasting the water availability. Generally, the precipitation deficit associated with high temperature and evapotranspiration values define meteorological, agricultural and hydrological drought, while the precipitation amounts exceeding the multiannual averages over an area refill the water resources. The main objective is to provide climatic indicators relevant for analysing the water resources vulnerability in the SEE Europe. The data will be available for the activities focused on assessing the vulnerability of the water resources. For climate change data results from CC-WaterS project were used. Climate change data were obtained from three RCMs (RegCM3 – ITCP, Aladin – CNRM, Promes – UCLM), based on A1B scenario. The CC-WaterS data base comprises daily and monthly temperature and precipitations derived from three RCMs, namely RegCM3, ALADIN-Climate and PROMES, extended from 1961 to 2050, at 25-km spatial resolution. RegCM3 is the third generation of the RCM originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. The model is driven by the GCM ECHAM5-r3, it uses a dynamical downscaling, and it is nowadays supported by the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy (Elguindi et al. 2007). ALADIN-Climate was developed at Centre National de Recherche Meteorologique (CNRM), and it is downscaled from the ARPEGE-Climate as a driver for the IPCC climate scenarios over the European domain (Spiridonov et al 2005; Farda et al 2010). PROMES is a mesoscale atmospheric model developed by MOMAC (MOdelizacion para el Medio Ambiente y el Clima) research group at the Complutense University of Madrid (UCM) and the University of Castilla-La Mancha (UCLM) (Castro et al 1993; Gaertner et al 2010), and it is driven from the GCM HADCM3Q0. The initial simulation results of RegCM3, ADALDIN-Climate and PROMES were available from the ENSEMBLES project (Hewitt 2004), and they were selected because (1) their spatial extent covers the full study area of CCWaterS, (2) they provided good performance in the simulation of historic climate conditions, and (3) each of them uses a different driving GCM. A1B Scenario A1B SRES IPCC scenario, which presumes balanced energy sources within a consistent economic growth, into the context of increasing population until the mid-21st century, and rapid introduction of more efficient technologies (IPCC TAR WG1 2001). BIAS Correction The RCMs outputs were bias corrected using the quantile mapping technique (Déqué 2007; Formayer and Haas 2010) based on daily observations extracted from the E-OBS data base v2.0 (CCWaterS 2010). E-OBS is European 25 km-spatial resolution gridded temperature and precipitation data set compiled from weather station daily measurements. Their ability to reproduce the temperature and precipitation was tested both locally (Busuioc et al. 2010) and at European scale (CCWaterS 2010), and the results showed that differences between both observations and model control runs and results of different RCMs may be significant

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especially in the mountain areas (CC-WaterS 2010). The quantile mapping technique was used to calibrate each RCM for the control period 1951-2000. The correction method is based on using the differences of the empirical cumulative density functions (CDF) of each model and observation data (E-OBS; Haylock et al 2008)) and it is applied to the model data such that the statistics of the observations are retained. For the scenario period, the CDFs were calculated for the periods 2001-2025, 2026-2050, 2051-2075 and 2076-2100 and applied in a way, that allows the production of continuous bias corrected time series from 1951-2100 (1951-2050 for PROMES) (CCWaterS 2010). It has to be stressed that in CC-WARE the periods analysed were slightly different (1961-1990; 1991-2020; 2021-2050), but this should not affect the results. The use of updated E-OBS data sets (e.g. v10.0, released in April 2014) are likely to improve the bias correction in some areas, but at regional the general pattern would remain very much similar. Ensemble Considering the objectives of the project, the outputs of the three models were aggregated for each season by arithmetic mean, and the results were further used as an ensemble data set. Data time intervals are following: - 1961-1990 (baseline climate); - 1991-2020 (present climate); - 2021-2050 (future climate). Far future period 2071-2100 was not selected for study due to large uncertainties.

1.1

Determination of climate indicators

Main climate variables are: 

precipitation (RR),



temperature (T) and



potential and actual evapotranspiration (PET and AET).

Additional climate indicators, which were used for description of climate, are: 

UNEP Aridity Index



De Martonne’s Index of Aridity

Precipitation (RR) and temperature (T) data were obtained from ensemble data set from three RCM models (RegCM3, ALADIN-Climate and PROMES), as described in introduction to this chapter.

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Potential evapotranspiration (PET) The potential evapotranspiration (PET) is the maximum possible amount of water resulted from evaporation and transpiration occuring from an area completely and uniformly covered with vegetation, with unlimited water supply without advection and heating (Dingman 1992; McMahon et al 2013). The potential evapotranspiration is calculated using the Thornthwaite approach (1974), utilising solely temperature data of the regional climate models. We used the R-Package SPEI (Beguería and Vicente-Serrano 2010; Vicente-Serrano et al. 2010) to calculate the PET using the Thornthwaite's formula (Thornthwaite 1948): (1)

where PET = potential evapotranspiration; L = average day length (hours) of the month being calculated; N = number of days in the month being calculated; Ta = average daily temperature (°C; if negative, use 0) of the month being calculated; I = heat index which depends on the 12 monthly mean temperatures; α = (6.75*10-7)I3 - (7.71*10-5)I2 + (1.792*102 ) I + 0.49239. Actual evapotranspiration (AET) The actual evapotranspiration (AET) is a key component for catchment and water balance studies, representing the real evapotranspiration occurring over a certain area in a specific period. The AET was calculated with the Budyko's original equation (Budyko 1974, Gerrits et al. 2009) according to annual PET and precipitation: (2) where RRa denotes mean annual rainfall and φ is Aridity Index: (3) where PETa is annual potential evapotranspiration. The Budyko framework is frequently applied to assess actual evapotranspiration on a catchment scale (e.g. Oudin et al., 2008; Roderick et al., 2011; Zhang et al., 2008, 2004, 2001) and has showed satisfactory results. The condition of the application to larger regions is met in CC-WARE. The spatial scale the method is applied to is defined by the 0.25° grid of the climate data, resulting in an area of about 625 km² being evaluated. Furthermore long term annual values of rainfall and potential evapotranspiration are used (1991-2020; 20212050) as a basis. Therefore the precondition, that the storage term within an area can be neglected, is also considered Budyko (1974) considered watersheds with area larger than 1000 km2 to minimize the effects of groundwater flows that he assumed to be negligible. Under these conditions he obtained empirically the Budyko curves by plotting the watershed data and fitting with a smooth curve. This is a tool to estimate total runoff from such watersheds. In CC-WARE project AET was calculated for 25 km x 25 km grids (area of 625 km2) and it is assumed that Budyko curves can be applied, since the methodology has been applied also to smaller catchments (Oudin et al. 2008, Zhang et al. 2001, 2004, 2008), where validation using

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observed data show reasonable results. Therefore Budyko curves have to be modified on the basis of runoff observations, for which are not available for the whole SEE region. Additional limitations of AET results are because AET is derived from modelled precipitation data, which were bias corrected with E-OBS data base, which is valid for lowland and not for mountainous regions, therefore in these areas results have to be additionally interpreted. UNEP Aridity Index Aridity is usually expressed as a generalized function of precipitation, temperature, and/or potential evapotranspiration (PET). An Aridity Index (UNEP 1997) can be used to quantify precipitation availability over atmospheric water demand. According to the UNEP Aridity Index (AI), aridity is classified in four categories (Table 3) based on the precipitation availability over atmospheric water demand and it is evaluated with the ratio between precipitation (P) and potential evapotranspiration (PE) (UNEP 1992): (4) Table 3: UNEP aridity classiﬁcation (UNEP 1992). Climate classification Hyperarid Arid Semi-arid Dry subhumid Humid

AI 0.65

De Martonne’s Index of Aridity At almost 90 years since its creation, de Martonne Aridity Index (MA) still proves its utility for evaluating the water availability in an area (Baltas 2007; Maliva and Missimer 2012). The annual value of the index was calculated by the equation (5) (De Martonne 1926), while the corresponding precipitation amounts and climatic classification can be followed in the Table 4 (Baltas 2007). (5) Table 4: De Martonne index aridity classification and corresponding precipitation amounts (Baltas 2007). Aridity classification Dry Semi-dry Mediterranean Semi-humid Humid Very humid Extremely humid

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MA 55.0


Precipitation (mm) 800.0

1.2

Homogeneous areas

For variables and indices homogenous regions were elaborated based on grids and interpolation. Spatial resolution is 25 km (0,25o). Due to many local coordinate projected systems (e.g. Gauss-Krueger D48 used in Slovenia, another local Gauss-Krueger projected system for Serbia etc.) it was decided to use the most common geographic system WGS1984. Units of this geographic system are latitude and longitude degrees. Consequently, cell size of all raster data was fixed to 0.25x0.25 degrees to be consistent with other raster data and snapping of the raster cells was set in ArcGIS Environmental settings. For some layers, data was received or calculated in geographic system ETRS89, using slightly different ellipsoid (GRS80 ellipsoid) than WGS84 system (WGS84 ellipsoid), but the differences in ellipsoid is less than a milimetre in the polar axis, leading to maximum half of the metre in projection, and is as such thus completely neglegible for the purpose of the project data, having cell size of 0.25x0.25 degrees (approximately 25 km when projected). In ESRI grid data, the first six lines indicate the reference of the grid, followed by the values listed in "English reading order" (left-right and top-down). Normalization of maps In order to compare maps actual absolute values were normalized by scaling between 0 and 1 with formula: . For all parameters (PP, AET, LTR) the lowest minimum and the highest maximum value was chosen among all min and max values for all periods, so that all maps within the same parameter can be compared. Temperature Differences in the temperature (oC) according to ensemble of RegCM3, ALADIN and PROMES models for baseline, present and future period are presented in Figure 2. The data retrieved by the ensemble models show that the air temperature will increase in all the seasons, and in all the regions of the SEE area. Comparing the 2021-2050 and 1991-2020 mean temperatures, one can remark that the highest differences occur during the summer, when the Balkan Peninsula may be with 2.0-2.5°C warmer, while the temperature could generally increase with 1-2°C. The increasing trend is present in the other seasons, but at lower rates (1.0-1.5°C in autumn, 0.5-1.5°C in spring and winter). Annual precipitation amount The ensemble precipitation varies between annual amounts of 300 - 400 mm in the southern part of the Balkan Peninsula and Italy, and over 1.700 mm in the Alps. There is very likely that the precipitation amounts are highly underestimated in the northern part of the Eastern

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Carpathians, probably due to the flaws in the E-OBS input used in CC-WaterS. However, the general pattern is consistent with the scientific literature. Differences in annual precipitation amount (mm) according to ensemble of RegCM3, ALADIN and PROMES models for baseline, present and future period are presented in Figure 4. The differences (Figure 4) between the future period (2021-2050) and present (1991-2020) reveal that the analysed region is at the edge between the northern areas expecting increasing amounts, and the southern ones where decreasing is likely to occur in the next decades.

o

Figure 2: Differences in annual temperature values ( C) between future and present period for fall, winter, spring and summer according to ensemble of RegCM3, ALADIN and PROMES models.

Figure 3: Normalized annual precipitation amount (mm) for baseline, present and future period according to ensemble of RegCM3, ALADIN and PROMES models.

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Figure 4: Differences in annual precipitation amount (mm) between future and present period according to ensemble of RegCM3, ALADIN and PROMES models.

Annual actual evapotranspiration Normalized annual actual evapotranspiration values according to ensemble of RegCM3, ALADIN and PROMES models for baseline, present and future period are presented in Figure 5. The annual AET decreases from the western to the eastern part of the SEE area. The highest values occur in the southern part of the Alps, and in Greece. The present AET pattern will be preserved in the future, but some fluctuations in the absolute values can be predicted. Thus, the annual AET will increase with 10-25 mm in the northern part of the SEE area, mainly in the mountains, and will probably decrease slightly in lowlands.

Figure 5: Normalized values of annual actual evapotranspiration (mm) according to ensemble of RegCM3, ALADIN and PROMES models for present and future period.

Differences in annual AET (mm) according to ensemble of RegCM3, ALADIN and PROMES models for baseline, present and future period is presented in Figure 6.

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Figure 6: Differences between future and present annual actual evapotranspiration (mm) according to ensemble of RegCM3, ALADIN and PROMES models for present and future period.

UNEP Aridity Index UNEP Aridity Index according to ensemble of RegCM3, ALADIN and PROMES models for present and future period is presented in Figure 7. Some relevant changes in the aridity can be expected in the eastern part of the SEE area. According to the UNEP Aridity Index, significant territories from the eastern parts of Romania and Bulgaria could become dry sub-humid in the next decades, and the semi-aridity will be more extended in the eastern parts of Greece. The general pattern of the territorial distribution will remain unchanged.

Figure 7: UNEP Aridity Index (mm) according to ensemble of RegCM3, ALADIN and PROMES models for present and future period.

De Martonne’s Index De Martonne’s Index of Aridity according to ensemble of RegCM3, ALADIN and PROMES models for basline, present and future period is presented in Figure 8. The values of de Martonne’s Index of Aridity illustrate that substantial changes are likely to

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occur over the eastern part of the Balkan Peninsula in the next decades, leading to shifting from semi-humid to semi-aridity. In the rest of the SEE area, the shifting from one aridity category to another is less evident.

Figure 8: De Martonne’s Index (mm) according to ensemble of RegCM3, ALADIN and PROMES models for present and future period.

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2

Water resources sensitivity to CC

2.1

Water quantity

According to UNEP methodology (2009), vulnerability is a function of water availability, use and management parameters. The latter will be discussed in WP4. One of the parameters is water exploitation index (WEI) or water stress, which is the ratio of total water demand (domestic, industrial and agricultural) to the available amount of renewable water resources that consists of surface water and groundwater safe yield (river discharge or runoff and groundwater recharge). Values from 0,2 to 0,4 indicate medium to high stress, whereas values greater than 0,4 reflect conditions of severe water limitations (Vörösmarty et al. 2000). Water demand is estimated as water withdrawal by sectors. Future water demand can be estimated regarding population growth (domestic water use), GDP changes (industrial water use) and land use changes (agricultural water use). Nevertheless, all these are also subject to policy. Future water demand will be assessed applying different scenarios. Uncertainty can be expressed as differences among min, plausible and max values. 2.1.1

Water quantity sensitivity indicators

Indicators for water quantity sensitivity to CC are presented in Table 5. For present water quantity sensitivity present time period (1991-2020) was took into consideration. Sensitivity was not calculated for baseline time period (1961-1990), because there are no relevant data for land use (CLC). Furthermore, after 1990’s major political changes happened in SEE area, strongly influencing on water demand parameters. For climate change data results from CC-Waters project were used (see chapter 1). Climate data from the following periods were used: 1991-2020 (R = recent) and 2021-2050 (F = future). Table 5: Indicators for water quantity sensitivity. INDICATORS Precipitation Actual evapotraspiration Water demand - total Water demand - population Water demand - agriculture Water demand - industry Local Total Runoff Local Total Runoff Index

SYMBOL P AET WD WDp WDa WDin LTR LTRI

UNITS 2 mm/yr = (l/m )/yr 2 mm/yr = (l/m )/yr 2 mm/yr = (l/m )/yr 2 (l/m )/yr 2 (l/m )/yr 2 (l/m )/yr 2 mm/yr = (l/m )/yr ND

DATA SOURCES & FORMULAS CC-WaterS SEE Project Budyko formula WD = WDp + WDa + WDi EUROSTAT, Partner Countries Partners countries, FAO, Eurostat EUROSTAT, Partner Countries LTR=P-Eta LTR normalized 0-1

Local Water Exploitation Index

LWS

ND

LWS=WD/LTR

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1) Local total runoff Water availability was calculated as a simplified water balance: Q = P – AET, where Q is total runoff (surface and groundwater). Calculations of total runoff were elaborated based on grids with spatial resolution of 25 km (0,25o). Inflowing and outflowing runoff to and out of the cell was not taken into consideration. It is assumed that the change is buffering looking in larger scale (NUTS3 regions). Basically only direct runoff recharge (from precipitation) was taken into consideration. Because of that the indicator was named LOCAL TOTAL RUNOF (LTR) instead of water availability. Precipitation values were obtained from ensemble of selected RCM’s and actual evapotranspiration, which has some bias (see chapter 1). In order to avoid misleading figures absolute values of LTR were normalized by scaling between 0 and 1 obtaining LOCAL TOTAL RUNOFF INDEX (LTRI):

For all parameters (PP, AET, LTR) the lowest minimum and the highest maximum value was chosen among all min and max values for all periods, so that all maps within the same parameter can be compared. Figure 9 presents baseline, present and future local total runoff index. In all periods it is obvious that in the Alps and Charpatian total runoff is high, whereas in all other parts it is relatively low, which means less water is available. Differences among periods are very small, therefore relative change of absolute values of local total runoff (ΔLTR) was calculated.

Figure 9: Local total runoff index (LTRI) (mm) according to ensemble of RegCM3, ALADIN and PROMES models for baseline (B), present (P) and future (F) period.

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For estimation of impact of climate change on local total runoff, relative change of absolute values of local total runoff (ΔLTR) was calculated as: -

,

where F index means future (i.e. 2021 – 2050), while P index means present period (i.e. 1990 – 2020). ΔLTR is presented on Figure 10 and it can be seen that in mountanous areas of the Alps and Charpatians there is a slight positive change, which meas that there local total runoff might be higher in the future. On the other hand in western and eastern part of Greece, NE Bolgaria and SE Romania scenarios show that local total runoff would diminish.

Figure 10: Relative change of Local total runoff (ΔLTR) (mm) according to ensemble of RegCM3, ALADIN and PROMES models for baseline, present and future period.

2) Water demand Present water demand Water demand was evaluated as water uses by different sectors: domestic (DWD), agriculture (AGRWD) and industry (INDWD), so total water demand is: WD = DWD + AGRWD + INDWD. For present period data sets for water demand (WD) are available for SEE region and were collected from each particular project partner. All WD data have units m3/year; for further calculations these data were transformed to mm/year. Data sets of WD were provided on NUTS 3 level (where data were available) or on country level for individual countries by the project partner. Agricultural water demand was not easy to estimate since most of counties do not have georeferenced water use data. Moreover it is not easy to get industrial water use data with separation of water use for hydro power plant and thermal and nuclear PP.

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Water use for hydro power plant is in some countries very high, but this water does not present significant water loss. Not all countries have avalibale data on NUTS3 level; in such cases country data was used. In this case weights were defined for particular WD in order to allocate country water demand value to NUTS3 level (Table 7). For the calculation of weights for domestic water demand data about population density (population number for each NUTTS 3 respectively) were collected from the EUROSTAT web portal, except for Republic of Serbia, for which data from the Serbian statistical office were provided. For agricultural water demand a percentage of agricultural areas in particular NUTS 3 was calculated. For industrial water demand a percentage of industrial areas in particular NUTS 3 was calculated (Table 6). Possible levels of water demand data and methodology for allocation of country level data to NUTS 3 regions is presented in Table 7. In case of Italy, we collected only data for eastern part of a country which belong to SEE region, all other data were excluded from the further analyses. In case of Republic of Serbia, which is not involved into EUROSTAT nomenclature system, all data were collected on municipality level. Thus they also provided shape files for further analyses. In table 5 is presented an overview of data levels and collected data sets obtained by CC-WARE partner countries. Table 6: Methods for estimation of water demand for different sectors

Level of data sets

WD domestic - DWD

WD agriculture - AGRWD

WD industry - INDWD

COUNTRY

3

Domestic water use [m /yr] NUTS3

3

for each NUTS 3

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3

Industrial water use [m /yr] for

[m /yr] for each NUTS 3

each NUTS 3

Domestic water use [m /yr]

Agricultural (irrig.) water use

Industrial water use [m /yr] for

for each Municipality

[m /yr] for Municipality

3

Municipality

Agricultural (irrig.) water use


3

3

each Municipality

Table 7: Overview of available and collected data from CC-WARE partner countries

Country

WD domestic DWD

WD agriculture AGRWD

WD industry INDWD

AT

NUTS 3 level

NUTS 3 level

NUTS 3 level

ITA

NUTS 3 level

HU

NUTS 3 level

NUTS 3 level

NUTS 3 level

RS

Municipalities

Municipalities

Municipalities

BG

country

Irrigation by stations

country

RO

GR

NUTS 3 level (lack of data)

NUTS 3 level

NUTS 3 level

*



REMARKS

NUTS 3 level



Country level was used (WDa-NUTS2) Country level was used due to lack of data Country level was used due to lack of data Country level was

SI

country

Country (+NUTS 3)

country

used due to lack of data

*Data was extracted from NUTS 2 data based on the % of all agricultural lands in corine land cover in each NUTS 3 with respect to the total all agricultural lands in corine land covein the nuts 2. This is certainly not perfect: for instance, the sum area at NUTS 2 derived form CLC does not match with the irrigated area value of FAO: We've actually tried different combinaıons (i.e. selected subsets of land use codes in CLC) but never managed to match it.

Water demand data for different sectors were gathered and unified in one large MS Excel Spreadshit, from where they were transformed into GIS environment. Data was collected on NUTS3 statistical level from each country, with two exceptions. For Italy, only selected NUTS3 regions were included in the project (not all of the Italy), and these regions were used in the mask. For Serbia, municipalities were used instead of NUTS regions, as this country is not in the statistical EU NUTS region. One must note that the exact borders of Serbia do not match exactly the country borders of other NUTS3 regions, but the gaps on the border are small and were disregarded in the rasterization process. To assure the best quality of data they were also compared with data adopted by FAO (available at FAO online database), EUROSTAT database and with WD data from World Bank database. All data was saved into a vector shapefile (SHP format) with a file name SEE_NUTS3_WD_final_ITA.shp. Please note that in the GIS model picture (Figure 11), the file name is shortened to NUTS3_SEE for the increased readability.

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Figure 11: A GIS model of creating maps.

Shapefile contains following attributes: FID and Shape, STAT_LEV for NUTS level, NUTS_ID and NUTS3 for NUTS3 identification, AGRWD for agricultural water demand, DWD for domestic water demand, INDWD_tot for industrial water demand, WD_tot for total water demand (WD_tot = AGRWD + DWD + INDWD) and DWD_summer as a correction factor (Figure 12).

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Figure 12: Attribute list for SEE_NUTS3_WD_final_ITA.shp file.

This shapefile was then transformed into several water demand raster layers by ArcGIS (Feature to raster tool). Total water demand was rasterized into WD_tot layer, agricultural water demand into AGRWD, domestic water demand into DWD, and industrial water demand into INDWD layer. WD maps were prodouced on NUTS 3 level in vector format. When all WD maps were transformed from vector to raster, “Feature to Raster (Conversion)” was applied. This tool always uses the cell center to decide the value of raster pixel. Thus at the country borders empty cells can be observed.

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Future water demand For future water demand four simple scenarios of decreasing, not changing or increasing water demand have been applied. To account an increase/no change/decrease of WD in future scenarios a water demand factor ∆WD was introduced, as follows: , where ΔWD is -10 %, 0%, +10%, +25% for four water demand scenarios in the future. An increase or decrease in water demand was calculated in ArcGIS with ArcToolbox Raster Calculator. A factor ∆WD used to multiply the original water demand raster data in future scenarios was:    

0.9 for 10 % decrease of WD, 1.0 for 0 % change (no change), 1.1 for 10 % increase of WD, 1.25 for 25 % increase of WD.

Figure 13 presents total water demand for present and future scenarios for CC-WARE countries within SEE area.

Figure 13: Water demand for present and future scenarios for CC-WARE countries within SEE area.

Figure 14 presents domestic water demand for present and future scenarios for CC-WARE countries within SEE area. It can be clearly seen that data was gathered on NUTS 3 level. In mountanous regions there is very low population density therefore domestic water demand

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is very low, whereas in plain areas population density is high and domestic water demand is higher.

Figure 14: Domestic water demand (DWD) for present and future scenarios for CC-WARE countries within SEE area.

Figure 15 presents agricultural water demand for present and future scenarios for CC-WARE countries within SEE area. There is a very high water demand in the Black sea area in Romania, which is misting all other agricultural water demand in the area.

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Figure 15: Agricultural water demand (AGRWD) for present and future scenarios for CC-WARE countries within SEE area.

Figure 16 presents industrial water demand for present and future scenarios for CC-WARE countries within SEE area. There is a very high industrial water demand in the area north of Sofia and along Marica river (Plovdiv) in Bolgaria.

Figure 16: Industrial water demand (INDWD) for present and future scenarios for CC-WARE countries within SEE area.

3) Local water exploitation index (LWEI) From WD maps and LTR maps, LOCAL WATER EXPLOITATIO INDEX (LWEI) was calculated as a ratio between WD and LTR for all periods and scenarios, presented in GIS model in Figure 11:

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where LWEI is Local Water Exploitation Index, WD is Water Demand and LTR Local Total Runoff. Considering annual values and different sectors contributing to water demand above equation is than: , with WDa ... Annual water demand, LTRa ... Annual local total runoff, ΔWD ... Factor for change of WD in future scenarios (0.9, 1., 1.1, 1.25), DWD ... Domestic water demand, AGRWD ... Agricultural water demand, INDWD ... Industrial water demand, RRa ... Mean Annual rainfall, AETa ... Mean Annual actual evapotranspiration. The name LOCAL Water Exploitation Index is because total runoff was calculated as direct runoff, not taking into consideration inflowing and outflowing runoff to and out of the 25x25 grid cell. These operations were performed with Raster Calculator tool in ArcGIS toolbox. In GIS model (Figure 11) these operations are marked with numbers Raster Calculator to Raster Calculator (3), (4) and (5). Using described procedure in Arc GIS software maps LWEI maps for present and four future water demand scenarios were elaborated (Figure 17). Local Water Exploitation Index values were classified into five stress/sensitivity clasess: < 0.2 very low water stress 0.2 – 0.4 low water stress 0.4 – 0.6 medium water stress 0.6 – 0.8 high water stress > 0.8 very high water stress Figure 17 shows that there is a high water stress on annual level in the SEE region already in the present state (P), except in mountanous regions.

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Figure 17: Local Water Exploitation Index (LWEI) for present and future scenarios of water demand for CC-WARE countries within SEE area.

4) Seasonal Local water exploitation index (LWEI)

Demand - Availability [e.g. m³]

Assessing the WEI on an annual basis neglects seasonality and extremes in demand and availability. These factors are however a frequent cause for water scarcity and need to be addressed. Figure 18 and Figure 19 illustrate this problem.

Jan

Feb Mar Apr May Jun

Jul

Aug Sep

Okt Nov Dec

Agricultural water demand

Domestic water demand

Industrial water demand

Mean water demand per month

Mean available water per month

Available water resources

Figure 18: Hypothetical example of monthly water demand and availability.

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Demand/Availabilty [-]

3.50 3.00

2.50 2.00 1.50 1.00 0.50 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec Demand/Availabilty

Annual mean

Figure 19: Demand to availability ratio of a hypothetical example

Assessing the LWEI on an annual basis would show no substantial deficits, as the mean water demand is lower than availability (solid and dashed line in Figure 18). This fact is also visible in Figure 19, where the annual mean ratio between demand and availability is lower than 1. The hypothetical example in Figure 20 however shows, that in single months the demand is higher than the availability, leading to ratios between demand and availability larger than 1 (Figure 19). For this reason it was decided to evaluate the WEI for three different time periods: (i)

annual basis,

(ii)

summer period and

(iii)

winter period.

As a basis for further assessments within CC-WARE, the LWEI of the different time periods was combined to an “Overall Water Exploitation Index”. The methodology for the assessment of the summer and winter LWEI is described in the following sections. The procedure for estimating actual evapotranspiration for summer and winter period, which is needed for the water availability term, is described beforehand. LWEI for summer season The Water Exploitation Index for summer season (WEIs) is estimated as the ratio between water demand and availability (total runoff) in summer months. The months of April to September are thereby included. Similar to the annual WEI, a multiplicative factor ΔWD for considering water demand change in future is also used, which is set to 1 for the recent period (1991-2020). To account for an increase in domestic water demand in summer months, e.g. due to tourism, a water demand seasonality index (α sD) is introduced and provided by project partners. It is defined as the ratio between domestic water demand in summer with regard to winter season, so the domestic water demand is: )

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For agricultural water demand in summer season, annual value of agricultural water demand was taken into account. For industrial water demand it is assumed that it is the whole year more or less constant, therefore in summer season industrial water deman is a half of annula industrial water demand. The Summer Water Exploitation index is calculated as

with - Water exploitation index for summer season (Apr, May, Jun, Jul, Aug, Sept) - Water demand in summer season - Water availability in summer season - Factor for change of WD in future scenarios (0.9, 1.0, 1.1, 1.25) - Domestic water demand - Agricultural water demand - Industrial water demand - Domestic water demand seasonality index (Ratio between domestic water demand in summer months with regard to winter months).

with - Water availability in summer season - Mean annual actual evapotranspiration for summer season - Mean summer rainfall The Budyko formula only estimates mean annual AET values. To estimate summer AET s, annual AETa was multiplied with a scaling factor (αsA). It is the ratio between PET in summer months and on an annual basis. Furthermore AETs was limited to the amount of summer rainfall and is calculated as follows:

AETs =min(

sA,

)

– Scaling factor for actual evapotranspiration for summer season - Mean annual potential evapotranspiration - Mean summer potential evapotranspiration The approach for estimating summer AET assumes, that the ratio between summer and annual AET is similar to the ratio between summer and winter PET. This approach is feasible, since the seasonal distribution of AET is similar to (scaled) PET. However water availability may limit the AET value, which was explicitly considered in the above equation.

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Figure 20 shows that there is a very high water stress in summer months in the SEE region already in the present state (P), except in the Alps and some very small parts of Charpatian.

Figure 20: Summer Local Water Exploitation Index (LWEIs) for present and future scenarios of water demand for CCWARE countries within SEE area.

LWEI for winter season The winter Local Water Exploitation Index LWEIw for the months October to December and January to March is calculated in similar manner compared to the summer value:

with - Water exploitation index for winter season (Jan, Feb, Mar, Oct, Nov, Dec) - Water demand in winter season - Water availability in winter season - Factor for change of WD in future scenarios (0.9, 1., 1.1, 1.25) The water demand in winter is calculated as the difference between the annual value and the summer water demand:

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with – Annual water demand - Water demand in summer season - Domestic water demand - Agricultural water demand - Industrial water demand - Domestic water demand seasonality index (Ratio between domestic water demand in summer months with regard to winter months) The water availability (local total runoff) - is calculated as the difference between winter precipitation and AET in winter months:

with – Local total runoff in winter season - Mean annual actual evapotranspiration for winter season - Mean winter rainfall Winter AET is calculated as the difference between annual and summer AET:

AETw =

- AETs

Figure 21 shows that there is much lower water stress in winter months than in summer in the SEE region. None the less there are some areas with high water stress in southern Italy, Greece, along Marica river in Bolgaria, at the Black sea, eastern Romania and Panonian basin (NE Hungary and N Serbia).

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Figure 21: Winter Local Water Exploitation Index (LWEIw) for present and future scenarios of water demand for CCWARE countries within SEE area.

5) Overall Water Quantity Sensitivity For the further evaluation of water resources in the context of CC-WARE a single annual value resembling of the water quantity sensitivity is needed. After the intersection of winter and summer LWEI to a single seasonal value, a matrix is used to derive the Overall Water Quantity Sensitivity, utilising the seasonal and annual LWEI values. To combine the winter and summer LWEI to a seasonal value (LWEIseason), the following procedure is applied, assuming that the more critical value in respect to water exploitation is relevant: The overall quantitative vulnerability is derived with the annual and seasonal values as basis using the table 8. The classification in Table 8 reflects the fact, that higher annual sensitivity lead to high overall sensitivity values, since the overall water budget is limited. Higher seasonal values can on the other hand be compensated by lower annual sensitivity values, as technical measure, e.g. dams and reservoirs, can enable a seasonal redistribution of water resources.

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Seasonal sensitivity

Table 8: Overall Water Quantity Sensitivity as a function of annual and seasonal vulnerability

very low low medium high very high

A B C D E

very low [0-0.2] 1 A1 B1 C1 D1 E1

low [0.2-0.4] 2 A2 B2 C2 D2 E2

Annual sensitivity medium [0.4-0.6] 3 A3 B3 C3 D3 E3

high [0.6-0.8] 4 A4 B4 C4 D4 E4

very high [>0.8] 5 A5 B5 C5 D5 E5

very low

low

Overall sensitivity medium

high

very high

Figure 22: Overall Local Water Exploitation Index (LWEIo) for present and future scenarios of water demand for CCWARE countries within SEE area.

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6) Local Water Surplus in the future (LWS) Annual local surplus of water resources is calculated as the difference of local total runoff and water demand. Similarly to LWEI, it is calculated for all scenarios of Water Demand (WD). LWSi = LTR_F – WDi i can be: no change, F-10, F+10, F+25 Figure 23 depict annual local surplus of water resources (LWS) for baseline, present and future with present water demand data for CC-WARE countries within SEE area. It could be seen that water surplus is in the northern part in the Alps, whereas in the soutern part of SEE there is a water deficit.

Figure 23: Annual local surplus of water resources (LWS) for baseline, present and future with present water demand data for CC-WARE countries within SEE area.

2.2

Water quality

Quality problems may occur due to pollution caused by human activities or natural conditions (geological settings). The indicator “water quality sensitivity” describes the tendency or likelihood for pollution to reach water resources. An important driver for water quality vulnerability is land use. CORINE data base provides information necessary for the evaluation of the existing land use and estimation of potential

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pollution load for water resources, which is essential for determining critical areas and consequently for prioritising activities needed for the sustainable management of water resources in the SEE area. 2.2.1

Water quality sensitivity indicators

Main indicator for water quality sensitivity is land use (Table 9). Data set is Corine Land Cover (CLC2006). Present land use impact on water quality is reflecting in existing water quality. Water resources at risk are defined for water bodies by each Member State. Future land use scenarios (% changes – storylines) were evaluated in accordance with EEA study “Land-use scenarios for Europe: qualitative and quantitative analysis on a European scale (EEA 2007). Table 9: Indicators for water quality sensitivity. INDICATORS Land use load coefficients

SYMBOL LUSLI

UNITS Non dimensional

Pollution load - PLI Water quality index SW HG factor

PLISW WQISW HG

Non dimensional Non dimensional Non dimensional

Pollution load - GW Water quality index GW

PLIGW WQIGW

Non dimensional Non dimensional

DATA SOURCES & FORMULAS land use load coefficients for particular land use - literature SUM(LUSLIi · CLC AREAi) PLISW normalized from 0 to 1 HG factor according to IHME map categories PLISW · HG PLIGW normalized from 0 to 1

Present potential pollution load The core data set for the calculation of WQI Index is the CORINE land use data set for 2006 except for Greece where CORINE 2000 is used as 2006 data set is not available. For each CORINE land use class at LEVEL 3 an overall water pollution load index is assumed to be proportional to nutrient export coefficients from a given land use in CORINE. Nitrogen and Phosphorous export coefficients have been widely used in the assessment of nonpoint sources of pollution in the past. For the purposes of CC-WARE project much of this literature has been reviewed and on the basis of this review and expert knowledge for each CORINE land use class an appropriate pollution Load index has been assigned (See Table 10). To evaluate concept the CORINE Land uses for which export coefficients have been published in the literature were used to compare the relative ranking after normalization of the assigned pollution load Index and the Published export coefficients. Such a procedure is documented in Figure 26 which shows a plot of the Normalized pollution load index (WQISW) and the normalized phosphorous export coefficients for a given CORINE land use classes from literature; only those CORINE Land uses are shown, for which literature data is available. The data used and its source (Wochna et al. 2011) is shown in Table 11.

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Table 10: CORINE Land use and land use load coefficients.

CLC CODE

CLC Description

version 1 - Upper range of values from literature *Expert interpretation of literature data

version 2 - Lower range of values from literature *Expert interpretation of literature data

*Adopted for CC WARE Version 2 - Normalized between 0 and 1

PLIj, Relative index of pollution Load_2006 (or Nitrogen Export Coefficients)

PLIj, Relative index of pollution Load_2006

WQIj, (Normalized Index of pollution Load_2006)

111

Continuous urban fabric

7

6

0.400

112

Discontinuous urban fabric

6.3

5.5

0.367

121

Industrial or commercial units

8

5

0.333

122

Road and rail networks and associated land

5.5

7.5

0.500

123

Port areas

7

7

0.467

124

Airports

7

7

0.467

131

Mineral extraction sites

9

9

0.600

132

Dump sites

14

14

0.933

133

Construction sites

7

7

0.467

141

Green urban areas

3.5

3.5

0.233

142

Sport and leisure facilities

4

4

0.267

211

Non-irrigated arable land

12

12

0.800

212

Permanently irrigated land

15

15

1.000

213

Rice fields

13.5

13.5

0.900

221

Vineyards

6

6

0.400

222

Fruit trees and berry plantations

5

5

0.333

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CLC CODE

CLC Description







223

Olive groves

4.5

4.5

0.300

231

Pastures

3.5

3.5

0.233

241

Annual crops associated with permanent crops

9

9

0.600

242

Complex cultivation patterns

8.3

8.3

0.553

243

Land principally occupied by agriculture, with significant areas of natural vegetation

4

5.5

0.367

244

Agro-forestry areas

3

3

0.200

311

Broad-leaved forest

3.6

3.6

0.240

312

Coniferous forest

2.5

2.5

0.167

313

Mixed forest

2.8

2.8

0.187

321

Natural grasslands

2.5

2.5

0.167

322

Moors and heathland

2.7

2.7

0.180

323

Sclerophyllous vegetation

2.5

2.5

0.167

324

Transitional woodlandshrub

2.6

2.6

0.173

331

Beaches, dunes, sands

2.5

2.5

0.167

332

Bare rocks

1.5

1.5

0.100

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CLC CODE

CLC Description







333

Sparsely vegetated areas

2

2

0.133

334

Burnt areas

5

5

0.333

335

Glaciers and perpetual snow

0.1

0.1

0.007

411

Inland marshes

2.3

2.3

0.153

412

Peat bogs

2.3

2.3

0.153

421

Salt marshes

2.3

2.3

0.153

422

Salines

2.3

2.3

0.153

423

Intertidal flats

3

3

0.200

511

Water courses

3

3

0.200

512

Water bodies

3

3

0.200

521

Cooastal Lagoons

3

3

0.200

522

Estuaries

3

3

0.200

523

Sea and ocean

3

3

0.200

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Table 11: Relationship between assigned values of land use load coefficients and literature data on P export (Wochna et al. 2011).

CLC CODE

CLC Land use

Values from different sources and expert judgement

Values from literature. all values single source

Normalized TN

Normalized TP

TN Export Coefficient

TP Export Coefficient

Normalized TN

Normalized TP


111

5

1.2

0.417

0.246


121

6

2.5

0.500

0.512


122

5.5

1.2

0.458

0.246

Port areas

123

7

2.5

0.583

0.512

Airports

124

7

2.5

0.583

0.512

Construction sites

133

7

2.5

0.583

0.512

Green urban areas

141

3.5

0.83

0.292

0.170


142

4

1.2

0.333

0.246


211

12

4.88

1.000

1.000

Pastures

231

3.5

0.83

0.292

0.170


242

8.3

2.33

0.692

0.477

Land principally occupied by agriculture. with significant areas of natural vegetation

243

4

0.49

0.333

0.100

Broad-leaved forest

311

3.6

0.26

0.300

0.053

Coniferous forest

312

2.5

0.36

0.208

0.074

Mixed forest

313

2.8

0.26

0.233

0.053

Natural grasslands

321

2.5

0.62

0.208

0.127

Moors and heathland

322

2.7

0.13

0.225

0.027

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CLC CODE

CLC Land use

Values from different sources and expert judgement

Values from literature. all values single source

Normalized TN

Normalized TP

TN Export Coefficient

TP Export Coefficient

Normalized TN

Normalized TP

Transitional woodland-shrub

324

2.6

0.26

0.217

0.053

Beaches. dunes. sands

331

2.5

0

0.208

-

Inland marshes

411

2.3

0.23

0.192

0.047

Peat bogs

412

2.3

0.23

0.192

0.047

Water courses

511

3

0.5

0.250

0.102

1.20

1.00

Normaliyed PLI

0.80

0.60

0.40

0.20

0.00 0.00

0.20

0.40

0.60

0.80

1.00

1.20

Normalized P Export Coeff.

Figure 24: Relationship between Normalized Pollution Load Index (WQISW) and Normalized Phosphorous export coefficients for a particular CORINE land use from literature.

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For those CORINE Land uses for which literature data is not available, expert judgment assignment of appropriate values was used. The complete data set used for the determination of Surface Water Quality Index (WQISW), which is the Normalized Pollution Load Index (PLI) is shown in Table 10. Table 10 is joined to CORINE Land use shape file attributes and WQI is mapped for the baseline year 2006. Table 12: Adopted values for the WQI for the baseline year 2006.

CLC CODE

CLC Description

PLIj. Relative index of pollution Load_2006

WQIj. (Normalized Index of pollution Load_2006)

111


6

0.400

112


5.5

0.367

121


5

0.333

122


7.5

0.500

123

Port areas

7

0.467

124

Airports

7

0.467

131


9

0.600

132

Dump sites

14

0.933

133

Construction sites

7

0.467

141

Green urban areas

3.5

0.233

142


4

0.267

211


12

0.800

212


15

1.000

213

Rice fields

13.5

0.900

221

Vineyards

6

0.400

222


5

0.333

223

Olive groves

4.5

0.300

231

Pastures

3.5

0.233

241


9

0.600

242


8.3

0.553

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CLC CODE

CLC Description

PLIj. Relative index of pollution Load_2006

WQIj. (Normalized Index of pollution Load_2006)

243


5.5

0.367

244

Agro-forestry areas

3

0.200

311

Broad-leaved forest

3.6

0.240

312

Coniferous forest

2.5

0.167

313

Mixed forest

2.8

0.187

321

Natural grasslands

2.5

0.167

322

Moors and heathland

2.7

0.180

323


2.5

0.167

324


2.6

0.173

331


2.5

0.167

332

Bare rocks

1.5

0.100

333


2

0.133

334

Burnt areas

5

0.333

335


0.1

0.007

411

Inland marshes

2.3

0.153

412

Peat bogs

2.3

0.153

421

Salt marshes

2.3

0.153

422

Salines

2.3

0.153

423

Intertidal flats

3

0.200

511

Water courses

3

0.200

512

Water bodies

3

0.200

521

Cooastal Lagoons

3

0.200

522

Estuaries

3

0.200

523

Sea and ocean

3

0.200

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Future potential pollution load The methodology for the evaluation of future WQI follows on the methodology used for the present evaluation. Again the basis for the analysis is the land use. Land Use scenarios for EUROPE from the EEA study on qualitative and quantitative analysis of Land use scenarios for Europe (EEA 2007) were adopted as approprioate for the 2050 time horizon in this project. However. this study does not cover the whole CC WARE project area (data for Serbia is missing) and in order to overcome this problem it was decided to use the scenarios from the above study but to apply them to a CORINE data set that is available for the whole CC-WARE project area. Since land use categories from the EEA (2007; table 13) are not the same as CORINE land use categories a corespondance table (table 14) for land use classification between CORINE and EEA had to be prepared. Table 13: EEA classification of landscape types (EEA 2007). LAND USE ID

Landscape type according Landscape characteristics (based on land cover classes to EEA 2007 in Table 12)

001

Urban landscapes

Urban land use is dominant. All other land cover classes are not dominant

002

Landscapes with urban character

Urban land use is dominant but any other land use could be dominant as well

003

Landscapes with agricultural character

Cropland is dominant. any other land use is dominant

004

Rural landscapes with grassland dominance

Grassland is dominant. any other land use is not dominant

005

Rural mosaic landscapes with agricultural character

With majority of agricultural land. i.e. cropland and grassland > 50% of model cell area

006

Rural mosaic landscapes with semi-natural character

With majority of semi-natural land. i.e. other land. surplus land and forest > 50% of model cell area

007

Landscapes with seminatural to natural character

Other land category is dominant

008

Semi-natural landscapes with abandoned character

Other land category in combination with surplus land is dominant

009

Semi-natural landscapes with grassland character

Grassland in combination with other land and surplus land is dominant

010

Forest Landscapes

Forest is dominant

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Table 14: Comparisson of CORINE and EEA (2007) land use types. CORINE CODE. j

CORINE LAND COVER - CLC_3. j

CORRESPONDING EEA 2007 LAND USE

111


Urban landscapes

112


Urban landscapes

121



122



123

Port areas


124

Airports

131


132

Dump sites

133

Construction sites

141

Green urban areas

142


Landscapes with urban character Semi-natural landscapes with abandoned character Semi-natural landscapes with abandoned character Semi-natural landscapes with abandoned character Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character

211



212



213

Rice fields


221

Vineyards


222



223

Olive groves

231

Pastures

241


242 243

Complex cultivation patterns Land principally occupied by agriculture. with significant areas of natural vegetation

244

Agro-forestry areas

Landscapes with agricultural character Semi-natural landscapes with grassland character Rural mosaic landscapes with agricultural character Rural mosaic landscapes with agricultural character Rural mosaic landscapes with agricultural character Rural mosaic landscapes with agricultural character

311

Broad-leaved forest

Forest landscapes

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CORINE CODE. j

CORINE LAND COVER - CLC_3. j

CORRESPONDING EEA 2007 LAND USE

312

Coniferous forest

Forest landscapes

313

Mixed forest

Forest landscapes

321

Natural grasslands

Forest landscapes

322

Moors and heathland

Forest landscapes

323


Forest landscapes

324


331


332

Bare rocks

333


334

Burnt areas

Forest landscapes Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character Semi-natural landscapes with abandoned character

335


411

Inland marshes

412

Peat bogs

421

Salt marshes

422

Salines

423

Intertidal flats

No corresponding category Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character Landscapes with semi-natural to natural character

511

Water courses

No corresponding category

512

Water bodies


521

Coastal lagoons


522

Estuaries


523

Sea and ocean


In EEA (2007) study five land use change scenarios (table 15) were developed and for each a separate future land use map was elaborated. For each land use type it was calculated the percentage regarding to the CC-WARE project area. This was done for all five EEA scenarios of land use change.

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Table 15: Percentages of major landscape types in the five scenarios in 2035 and the base year in 2005. Base Year

The Great Evolved Escape Society

Clusters of Lettuce European Surprise U Networks

After the Big Crisis

Urban landscapes

3.0

4.2

3.2

3.0

5.2

4.3


3.2

2.2

3.1

3.4

1.1

2.0


24.4

7.2

24.1

15.5

0.8

17.1

Rural landscapes with grassland dominance

12.9

9.1

10.9

9.2

9.9

10.6

Rural mosaic landscapes with agricultural character

10.3

4.5

10.2

5.4

16.5

13.1

Rural mosaic landscapes with semi-natural character

13.7

25.2

15.7

14.0

30.0

19.3

Landscapes with semi-natural to natural character

8.6

8.4

8.6

8.9

8.8

8.6

Semi-natural landscapes with abandoned character

0.0

15.6

0.4

18.8

3.9

1.1

Semi-natural landscapes with grassland character

0.6

0.5

0.6

0.5

0.7

0.7

Forest landscapes

23.0

23.1

23.2

23.1

23.2

23.2

The data from the table 15 is used to compute the % change in land use for a given CORINE land use category in the correspondance Table 14. The result Scenarios for CORINE land use changes for 2050 are given in Table 16.

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Table 16: CORINE Land Use Scenarios for 2050 expressed in percentage in land use for a given CLC category (CC-WARE Project). % of land use change for a given scenario (% of total land area) CORINE CODE. j

CORINE LEVEL 3. j

111

The Great Escape_S1. k=1

Evolved Society_S2. k=2

Clusters of European Networks_S3 k=3

Lettuce Surprise U_S4 k=4

After the Big Crisis_S5 k=5


1.20

0.20

0.00

2.20

1.30

112


-1

-0.1

0.2

-2.1

-1.2

121


-1

-0.1

0.2

-2.1

-1.2

122


-1

-0.1

0.2

123

Port areas

-1

-0.1

0.2

-2.1

0.22

124

Airports

-1

-0.1

0.2

-2.1

0.22

131


15.6

0.4

18.8

3.9

1.1

132

Dump sites

15.6

0.4

18.8

3.9

1.1

133

Construction sites

15.6

0.4

18.8

3.9

1.1

141

Green urban areas

-0.2

0

0.3

0.2

0

142


-0.1

0

-0.1

0.1

0.1

211


-17.2

-0.3

-8.9

-23.6

-7.3

212


-17.2

-0.3

-8.9

-23.6

-7.3

213

Rice fields

-17.2

-0.3

-8.9

-23.6

-7.3

221

Vineyards

-17.2

-0.3

-8.9

-23.6

-7.3

222

Fruit trees plantations

-17.2

-0.3

-8.9

-23.6

-7.3

223

Olive groves

-17.2

-0.3

-8.9

-23.6

-7.3

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and

berry

-1.2

% of land use change for a given scenario (% of total land area) CORINE CODE. j

CORINE LEVEL 3. j

231






Pastures

-3.8

-2

-3.7

-3

-2.3

241


-5.8

-0.1

-4.9

6.2

2.8

242


-5.8

-0.1

-4.9

6.2

2.8

243


-5.8

-0.1

-4.9

6.2

2.8

244

Agro-forestry areas

-5.8

-0.1

-4.9

6.2

2.8

311

Broad-leaved forest

0.1

0.2

0.1

0.2

0.2

312

Coniferous forest

0.1

0.2

0.1

0.2

0.2

313

Mixed forest

0.1

0.2

0.1

0.2

0.2

321

Natural grasslands

-3.8

-2

-3.7

-3

-2.3

322

Moors and heathland

11.5

2

0.3

16.3

5.6

323


11.5

2

0.3

16.3

5.6

324


0.1

0.2

0.1

0.2

0.2

331


-0.2

0

0.3

0.2

0

332

Bare rocks

-0.2

0

0.3

0.2

0

333


-0.2

0

0.3

0.2

0

334

Burnt areas

15.6

0.4

18.8

3.9

1.1

335


0

0

0

0

0

411

Inland marshes

-0.2

0

0.3

0.2

0

412

Peat bogs

-0.2

0

0.3

0.2

0

421

Salt marshes

-0.2

0

0.3

0.2

0

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% of land use change for a given scenario (% of total land area) CORINE CODE. j

CORINE LEVEL 3. j

422






Salines

-0.2

0

0.3

0.2

0

423

Intertidal flats

-0.2

0

0.3

0.2

0

511

Water courses

0

0

0

0

0

512

Water bodies

0

0

0

0

0

521

Coastal lagoons

0

0

0

0

0

522

Estuaries

0

0

0

0

0

523

Sea and ocean

0

0

0

0

0

To calculate the WQI for the future under different scenarios for the future we use the land use % change for each scenario: 3 Where: S is the modifying coefficient for WQI of future land use change scenarios j identifies the CORINE land use class k identifies the future Scenario % land use change. 4 = corresponding values from Table 16 The values of

are given in the Table 17.

Table 17: The values of Corection factor for future land use scenarios Sjk - Values derived from EEA Land Use Scenarios for Europe CLC CODE

CLC Description s1 (k=1)

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s2 (k=2)

s3 (k=3)

s4 (k=4)

s5 (k=5)

Corection factor for future land use scenarios Sjk - Values derived from EEA Land Use Scenarios for Europe CLC CODE


s2 (k=2)

s3 (k=3)

s4 (k=4)

s5 (k=5)

111


1.01

1.001

0.998

1.021

1.012

112


1.01

1.001

0.998

1.021

1.012

121

Industrial units

1.01

1.001

0.998

1.021

1.012

122


1.01

1.001

0.998

1.021

1.012

123

Port areas

0.969

0.9992

0.9624

0.9922

0.9978

124

Airports

0.9688

0.9992

0.9624

0.9922

0.9978

131


0.9688

0.9992

0.9624

0.9922

0.9978

132

Dump sites

0.9688

0.9992

0.9624

0.9922

0.9978

133

Construction sites

0.9688

0.9992

0.9624

0.9922

0.9978

141

Green urban areas

0.988

0.998

1

0.978

0.987

142


0.988

0.998

1

0.978

0.987

211


1.172

1.003

1.089

1.236

1.073

212


0.885

0.98

0.997

0.837

0.944

213

Rice fields

1

1

1

1

1

221

Vineyards

1.002

1

0.997

0.998

1

222

Fruit trees plantations

1.002

1

0.997

0.998

1

223

Olive groves

1.002

1

0.997

0.998

1

231

Pastures

1.038

1.02

1.037

1.03

1.023

241


1.172

1.003

1.089

1.236

1.073

242


1.058

1.001

1.049

0.938

0.972

243


1.172

1.003

1.089

1.236

1.073

244

Agro-forestry areas

0.999

0.998

0.999

0.998

0.998

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or

commercial

and

berry


Corection factor for future land use scenarios Sjk - Values derived from EEA Land Use Scenarios for Europe CLC CODE


s2 (k=2)

s3 (k=3)

s4 (k=4)

s5 (k=5)

311

Broad-leaved forest

0.999

0.998

0.999

0.998

0.998

312

Coniferous forest

0.999

0.998

0.999

0.998

0.998

313

Mixed forest

0.999

0.998

0.999

0.998

0.998

321

Natural grasslands

1.001

1

1.001

0.999

0.999

322

Moors and heathland

1

1

1

1

1

323


0.885

0.98

0.997

0.837

0.944

324


0.885

0.98

0.997

0.837

0.944

331


1

1

1

1

1

332

Bare rocks

1

1

1

1

1

333


1

1

1

1

1

334

Burnt areas

1

1

1

1

1

335


1

1

1

1

1

411

Inland marshes

1

1

1

1

1

412

Peat bogs

1

1

1

1

1

421

Salt marshes

1

1

1

1

1

422

Salines

1

1

1

1

1

423

Intertidal flats

1

1

1

1

1

511

Water courses

1

1

1

1

1

512

Water bodies

1

1

1

1

1

521

Cooastal Lagoons

1

1

1

1

1

522

Estuaries

1

1

1

1

1

523

Sea and ocean

1

1

1

1

1

With the application of correction factors we get surface water pollution index (Table 16) which is than linked to CLC GIS data base for mapping purposes.

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Table 18: WQISW values for five future scenarios of land use change. FUTURE VALUE OF WQI CLC CODE

CLC Description

111

WQIj_205 0_S1

WQIj_205 0_S2

WQIj_205 0_S3

WQIj_205 0_S4

WQIj_205 0_S5


0.404

0.400

0.399

0.408

0.405

112


0.370

0.367

0.366

0.374

0.371

121


0.337

0.334

0.333

0.340

0.337

122


0.505

0.501

0.499

0.511

0.506

123

Port areas

0.452

0.466

0.449

0.463

0.466

124

Airports

0.452

0.466

0.449

0.463

0.466

131


0.581

0.600

0.577

0.595

0.599

132

Dump sites

0.904

0.933

0.898

0.926

0.931

133

Construction sites

0.452

0.466

0.449

0.463

0.466

141

Green urban areas

0.231

0.233

0.233

0.228

0.230

142


0.263

0.266

0.267

0.261

0.263

211


0.938

0.802

0.871

0.989

0.858

212


0.885

0.980

0.997

0.837

0.944

213

Rice fields

0.900

0.900

0.900

0.900

0.900

221

Vineyards

0.401

0.400

0.399

0.399

0.400

222


0.334

0.333

0.332

0.333

0.333

223

Olive groves

0.301

0.300

0.299

0.299

0.300

231

Pastures

0.242

0.238

0.242

0.240

0.239

241


0.703

0.602

0.653

0.742

0.644

242


0.585

0.554

0.580

0.519

0.538

243

Land principally occupied by agriculture, with significant areas of natural vegetation

0.430

0.368

0.399

0.453

0.393

244

Agro-forestry areas

0.200

0.200

0.200

0.200

0.200

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FUTURE VALUE OF WQI CLC CODE

CLC Description

311

WQIj_205 0_S1

WQIj_205 0_S2

WQIj_205 0_S3

WQIj_205 0_S4

WQIj_205 0_S5

Broad-leaved forest

0.240

0.240

0.240

0.240

0.240

312

Coniferous forest

0.167

0.166

0.167

0.166

0.166

313

Mixed forest

0.186

0.186

0.186

0.186

0.186

321

Natural grasslands

0.167

0.167

0.167

0.167

0.167

322

Moors and heathland

0.180

0.180

0.180

0.180

0.180

323


0.148

0.163

0.166

0.140

0.157

324


0.153

0.170

0.173

0.145

0.164

331


0.167

0.167

0.167

0.167

0.167

332

Bare rocks

0.100

0.100

0.100

0.100

0.100

333


0.133

0.133

0.133

0.133

0.133

334

Burnt areas

0.333

0.333

0.333

0.333

0.333

335


0.007

0.007

0.007

0.007

0.007

411

Inland marshes

0.153

0.153

0.153

0.153

0.153

412

Peat bogs

0.153

0.153

0.153

0.153

0.153

421

Salt marshes

0.153

0.153

0.153

0.153

0.153

422

Salines

0.153

0.153

0.153

0.153

0.153

423

Intertidal flats

0.200

0.200

0.200

0.200

0.200

511

Water courses

0.200

0.200

0.200

0.200

0.200

512

Water bodies

0.200

0.200

0.200

0.200

0.200

521

Cooastal Lagoons

0.200

0.200

0.200

0.200

0.200

522

Estuaries

0.200

0.200

0.200

0.200

0.200

523

Sea and ocean

0.200

0.200

0.200

0.200

0.200

The mapping is done for WQI2006 and WQI2050 for each of the five land use scenarios. Mapping can be done in vector format using CLC Vector data (polygons) and joining Table 16 to it. Once this is done GRIDDING at 1km grid or courser can be carried out for use in further analysis in WP3. i.e. the calculation of the overall Vulnerability index.

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Figure 25 presents potential water pollution index for surface water (WQISW). Since it is based on land use activities, these are reflecting in the water quality sensitivity. Areas with higher water quality index are mostly in lowlands, where there are intensive agricultural activities, industrial areas and large cities. It is not necessary that in area with high potential pollution index qualitative water status is bad. Present land use impact on water quality is reflecting in existing water quality, which has to be checked from the state reports, where qualitative state of water bodies and water resources at risk are defined for each year. In particular area water body status could be good, but is still sensitive to pollution because of the land use. V

V V

V

V

V

Figure 25: Potential pollution load – surface water quality sensitivity (WQISW) for present situation and five land use change scenarios.

GROUNDWATER POLLUTION / QUALITY INDEX The above explained methodology represents surface water quality index or vulnerability. Groundwater vulnerability depends on aquifer type. The basis for spatial determination of groundwater quality index is International Hydrogeological Map of Europe 1 : 1.500.000 IHME1500 (Figure 26), which was kindly made available in digital version by BGR (BGR & UNESCO 2014). For each aquifer type HG factor was applied considering aquifer vulnerability

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and groundwater recharge. HG factor is expressed as effective infiltration coefficient. High coefficient values indicate higher groundwater quality vulnerability; e.g. highly productive porous aquifers are very permeable and therefore more vulnerable to groundwater quality than areas with insignificant aquifers, which have very low permeability. For calculation of groundwater quality vulnerability effective infiltration coefficient (Table 19) was applied to each aquifer type. Additionally, there are some important confined aquifers in Po plain and Panonian basin, which are lying below shallow permeable surface aquifer and confining layer with low permeability. For these aquifers a value of 0.2 was set. Effective infiltration factor map according to IHME is presented in Figure 27.

Figure 26: International Hydrogeological Map of Europe 1 : 1.500.000 (BGR & UNESCO 2014).

By multiplying surface water pollution index (WQISW) with HG factor we obtained groundwater pollution index (WQIGW), which was normalized by scaling between 0 and 1.

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Figure 27: Effective infiltration coefficient as HG factor.

Table 19: HG factor - effective infiltration coefficient.

Effective infiltration coefficient 1 Aquifers in which flow is mainly intergranular 1.1 extensive and highly productive aquifers

0.6

1.2 local or discontinuous productive aquifers or extensive but only moderately 0.3 productive aquifers Confined aquifer

0.2

2 Fissured aquifers. including karst aquifers 2.1 extensive and highly productive aquifers

0.8

2.2 local or discontinuous productive aquifers. or extensive but only moderately 0.4 productive aquifers 3 Strata (granular or fissured rocks) forming insignificant aquifers with local and limited groundwater resources or strata with essentially no groundwater resources 3.1 minor aquifers with local and limited groundwater resources

0.1

3.2 strata with essentially no groundwater resources

0.05

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Figure 26 presents water quality index for groundwater (WQIGW). Since it is based on land use activities and hydrogeological characteristics, these are reflecting in the water quality sensitivit, which is the largest ina agricultural areas in karst regions.

Figure 28: Potential pollution load – groundwater quality sensitivity for present situation and five land use change scenarios.

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3

Adaptive capacity

Adaptive capacity describes how well a system (water resources quantity and quality) can adapt or modify to cope with the climate changes. A low adaptive capacity will result in high vulnerability and vice-versa. Economic status has one of the major roles in adaptation of drinking water supply to climate change and can be measured with indicator GDP (Table 20). Lower the GDP, lower is adaptive capacity and the system is more vulnerable to climate change impacts Table 20: Socio econnomic indicators. INDICATORS

SYMBOL

UNITS

GDP NUTS2

GDP

EURO/capitaYear

Population density Economic status

PD EcSt

capita/km EUR/km

Figure 29: GDP as indicator of adaptive capacity.

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DATA SOURCES & FORMULAS

2

2

STATS NUTS2 STATS NUTS3 GDP * PD

Figure 30: Population density as indicator of adaptive capacity.

Furthermore, natural system play an important role for drinking water sources protection. Therefore ecosystems can be natural indicator for adaptation capacity. Eg. wetlands have high protective value for drinking water protection. Ecosystem services have three functions: Provisioning Ecosystem Service, Water Regulation, Water Quality Regulation. ESS can increase ability of a particular area to provide water supply, or a qualitative rank of potential ability of a particular area to provide excellent (both quantity and water quality) water supply, i.e., areas where ESS are more sensitive, have a higher vulnerability from water supply perspective. Figure 31 presents ES services in water resources perspective.

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Figure 31: ES services in water resources perspective.

4

Integrated assessment of water resources vulnerability to climate change

The vulnerability index (VI) should be able to compare and rank vulnerability over SEE. and form the basis of analyzing mitigation actions (WP4) and developing transnational strategy for national/regional action plans (WP5). The first step of determination of integrated water resources vulnerability is to consider exposure to climate change and the sensitivity of the indicator to those changes. This step provides an understanding of the potential impacts of climate change on water resources. Combining this wit adaptive capacity we ger vulnerability of water resources (Figure 32).

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Figure 32: Determination of integrated vulnerability.

Another way of determination of integrated vulnerability is combining indicators, which are normalized from 0 to 1. Maximum values define vulnerability, whereas mean and range can define uncertainties. VI= max(LWEI, WQGW , ESS, GDP)

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4.1

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