The Information Management System Based on GIS ...

International Journal of Advanced Computer Science, Vol. 1, No. 4, Pp. 169-174, Oct. 2011.

The Information Management System Based on GIS for Evaluating Loss of Flood Hazard Guihui Zhong, Shuguang Liu, Cuiping Kuang, Maohui Zheng, Hongliang Gou, Haoyun Wu, & Hejuan Lin Manuscript Received: 27, June, 2011 Revised: 9, Aug., 2011 Accepted: 25, Aug., 2011 Published: 30, Oct., 2011

Keywords Information Management System, ArcGIS, loss evaluation, flood control, Taihu Basin

Abstract Loss evaluation system of flood hazard is a very important non-engineering measure in recent years. Based on the digital topographic map, the information management system for loss evaluating is established by employing VB based on ArcGIS and SQL server and applied in one area of Taihu Basin, where is vulnerable to flood hazard. With the statistic of each town, various types of land use and different submerged water depth, flood disaster loss is evaluated by this system. It has reasons to believe that the system is useful to provide scientific basis for mastering flood and making decisions on flood prevention and disaster reduction.

1. Introduction Taihu Basin is situated in the south of Yangtze River delta, lying between 119°08’-121°55’ east longitude and 30°05’-32°08’ north latitude. As shown in Fig. 1, the whole area is surrounded by Yangtze River in the north, Hangzhou Bay in the south and East China Sea in the east. This region has a temperate humid climate. The mean annual temperature fluctuates between 15°C and 17°C, while the annual precipitation is 1177mm and decreases gradually from southwest to northeast and every summer, the plum rain will bring abundant rainfall to the region [1]-[2]. This area is one of the regions with the rapid economic growth speed in China. However, flood disaster is the most serious natural disaster in this region which restricts the development of national economy. It is caused by several reasons such as the heavy rain in summer, low flood plain gradient, cyclonic storm surges, low standard of flood control engineering and so on [3]. Over the recent 60 years, Taihu Basin has witnessed severe flood such as in 1954, 1991 and 1999, and regional

flood hazard occurs almost every year. These floods seriously affect agricultural production and infrastructure development and cause immense suffering to local people. So far, the flood loss has been assessed by some measures. Correia assessed the economic loss for flood management [4]; Gerardo applied the historical data for to improve the flood risk estimation [5]; Forte used GIS and remote sensing method for loss estimation in the super-Ruffana-Nociglia Graben [6]; Tawatchai presented a methodology of loss evaluation for the southwest region of Bangladesh [7]; Zhao applied GIS approach to assess the flood loss in the middle part of Inner Mongolia [8]; Md. Monirul Islam used remote sensing data to assess the flood hazard [9]. While, little work has been done in Taihu Basin, for long-term planning, it is essential to establish an information management system to evaluate the flood loss based on probabilistic floods. The objective of this study is to present a methodology for information system development based on ArcGIS in the platform of VB and SQL server. The technological route, database frame, and functions of the system are introduced in this paper in the purpose of providing the use for reference for the establishment of loss evaluation system in other regions vulnerable to flood disasters.

Yan

Taihu Lake

gtze

R iv er

Huangpu River Taipu River

East China Sea

Hangzhou Bay This work was supported by the National Keynote Research Program of Technology (No.2008BAJ08B14) and the Kwang-Hua Fund for College of Civil Engineering, Tongji University Guihui Zhong, Shuguang Liu( ), Cuiping Kuang and Hongliang Gou are with Tongji University, Hydraulic Engineering Faculty ([email protected]). Maohui Zheng are with Shanghai Institute of Disaster Prevention and Relief,Tongji University ([email protected]). Haoyun Wu and Hejuan Lin are with Taihu Basin Authority Faculty ([email protected])

Fig. 1. Study area


170

B.

2. Database Construction A. Data Collection Construction of flood map information is a multifunctional, comprehensive and dynamic system. As a result, the system must consider economy, population and so on. In general, data sources can be divided into 5 main parts, and the detail is in Table 1. (1) DEM data DEM data is the basis for the estimation of submerged situation. In plain area, high precision data should be applied. In the study, the DEM is utilized with the contours of 1.5m, 2m, 2.5m, 3m, 3.5m, 4m, 4.5m and 5m [10]. (2) Geographical background data The original data concludes 17 maps which completely covers the study area. The digital map involves so many layers, some of which does not have necessary connection with the loss evaluation. Through ArcMap, geographical name, transportation, water system, boundary information and other useful data are selected. (3) Social-economic data It is the basis of for disaster loss evaluation. Based on the yearbooks, the social economic data is collected, including GDP, second industrial output, productive value of tertiary industry, population, total area, area of different types of land, property of the residents and so on. (4) Engineering data and rescue information 154 sluices and the main river embankments are collected. What’s more, the rescue materials such as medical organizations, headquarters, and temporary settlements and so on are also included in the database in the purpose of disaster reduction.

Database Structure Based on the analysis of the comprehensive data source used to build the system, the combination of spatial and attribute database and Microsoft SQL Server 2000 is applied. Spatial database comprises two parts, basic layer database and applied layer database, the detail is in Fig. 2. Attribute database is mainly comprised of society economy, land type, historical data, engineering data, loss statics and hydrological data, the detail is in Fig. 3.

3. Simulation of Submerged Area Numerical model is applied to calculate the flood and he submerged situation is simulated. After data transformation, water depth, velocity and flood duration can be queried in the system. The numerical models are as follows: A. t

Continuity Equation +

B.

(d + )u

1 G

G

+

G

(d + )v

1 G

G

G

=0

(Equ. 1)

Momentum Equation

u u + t G

u

v G

G

v2 G

+

v

v G

G

u2 G

+

+

fv =

G

v u + t G

u

v

+ fu =

G

+

G

uv G

G

1 G

P

(Equ. 2) gu

G

uv G

n2 u2 + v2 H 1/3 ( d + )

(Equ. 3)

G

1 G

P

gv

n2 u 2 + v2 H 1/3 ( d + )

Spatial Database

Point Layer

Line Layer

Medical institute

River System

Headquarter

Transportation

Region Layer

Water depth Submerged Situation

Flow field Flood Duration Settlement

Embankment

Land Use

Hydraulic engineering

Boundary data

Administrative District

Fig.2 Structure of Spatial Database of the System

International Journal Publishers Group (IJPG)©

Zhong et al: The Information Management System Based on GIS for Evaluating Loss of Flood Hazard.

171

Attribute Database GDP Society Economy

Primary industrial product Type name

Land Use

Industrial product

Area

Tertiary industrial product

Output value Historical Data

Date

Financial revenue

Location

Household, Population

Disaster loss

Submerged area

Area of land unit

Water depth

Submerged Situation

Velocity & direction Fig.3 Structure of Attribute Database of the System

In the formula, G** and G++ is the coefficient used to transform curvilinear to rectangular co-ordinates, is the free surface elevation above the reference plane; d is the depth below the reference plane; H=d+ is the total water depth; u and v are the depth averaged velocity of water in * and + direction respectively; is the water density; P and P are the pressure gradient in * and + direction respectively; f is the Coriolis parameter; n is the Mann’s coefficient; c is mass concentration; .d is the first order decay process and S is the source and sink terms.

4. Loss Evaluation

A.

Economic prediction of current year As the lack of social economic data of when flood happens, the prediction method of averaged growth rate is employed [11].

)

t

(Equ. 4)

In the formula, t is time; N is growth rate; Pt+1 is the data unknown; P0 is the given data. International Journal Publishers Group (IJPG)©

Flood Disaster Rate Flood disaster rate (FDR) represents the degree of loss caused by flood. It is a coefficient difficult to determine as it depends on lots of parameters such as depth of flooding, duration of flooding, velocity, land types and so on. As a result, considering the characteristic of the study area, three major parameters, namely water depth and duration of flood are chosen for loss evaluation. Also, the industrial types must be taken into account. Water Depth

Submerged Grid

In the social economic database of the system, the data of total land area, population, area of farmland, GDP and so on are applied to evaluate the damage caused by flood. The progress of loss evaluation is shown in Fig. 4. First the submerged grid is calculated according to the flood situation, second, the value of FDR is calculated based on water depth, flood duration and land use, which will be described in detail as follows. Last, formulas are applied to compute the economic loss of flood hazard, including Flood victims, Submerged farmland and so. Finally, the report of loss evaluation will be given in the form of excel.

Pt +1P 0 (1 +

B.

Flood Duration

FDR(β)

Land Use

Flood Disaster Rate L1=AS/AL×P L2=AS/AL×AF L3=β×VA×AS/AL L4=β×VI×AS/AL

Submerged Property Submerged Area

Economic Loss

Fig.4 Progress of loss evaluation

Generally speaking, FDR is usually determined by the analysis of the historical flood and flood duration. Through the study of disaster loss and the corresponding property, the regression equation could be established and FDR can be obtained. Based on the disaster damage in 1999, the value of FDR is in Table 1. The relationship of Flood duration, water depth and FDR is shown in Fig. 5. As shown in this figure, the FDR increases as the water depth and flood duration rises, while, the FDR will keep a constant as last.


172

TABLE 1: FLOOD DISASTER RATE OF TAIHU BASIN

5. Main functions of the system

Water depth

Agriculture

Industry

Tertiary industry

Property

1.0m

0.50

0.06

0.15

0.03

A.

Management of spatial database The different types of spatial data such as river system, transportation, and land application are classified in Geodatabase. The function of browse, selection, query, addition and modification are provided in this system. Management of attribute database All the attribute data such as GDP, population, area is stored in the SQL server 2000. By sending SQL sentences, association between attribute and spatial database is established. And as the human activities and social development, some relative information must be modified in this system.

FDR

B.

C. Flood duration Fig. 5 Flood Duration and FDR

C.

Loss evaluation of flood hazard According to the numerical simulation, the submerged area and the economic condition of the regions are summed up to analyze the flood disaster loss. Take towns as land unit, the flood loss is analyzed as follows: (1) Flood victims

L1 = AS / AL × P

Loss evaluation According to the social-economic data and submerged conditions, including the direction and location of dike burst, flood frequency and so on, the module calls relevant SQL-query sentences and applied program modules to calculate and analyze the disaster loss. Figure 6 shows the loss statistics consisting economic loss, affected people, submerged area under a certain engineering situation. The system allows the user to choose a certain submerged area, and then the table including the information above will be shown automatically.

(Equ. 5)

In the formula, L1 is the number of people affected by flood; AS is the area of submerged region; AL is the land area and P is the population. (2) Submerged farmland

L2 = AS / AL × AF

(Equ. 6)

In the formula, L2 is the area of submerged farmland, AF is the cultivated area. (3) Agricultural loss

L3 = × AS / AL

(Equ. 7)

In the formula, L3 is the agricultural loss; S is loss coefficient; VA is gross agricultural output value. (4) Industrial loss

L4 = ×V I × AS / AL

(Equ. 8)

In the formula, L4 is the industrial loss; VI is total industrial output value. (5) Loss of tertiary industry

L5 = ×VT × AS / AL

(Equ. 9)

In the formula, L5 is the loss of tertiary industry; VT is the gross output value of tertiary industry. (6) Property loss of residents

L6 = L1 ×V P ×

Fig. 6 Loss statistics with a return period of 100 years

D. Thematic map creation The module will create relevant thematic maps for some certain layers according to users’ need. Generally speaking, the maps are very popular in the loss assessment of flood hazard. [12] The themes in the module are water depth, velocity field, duration of flood, flood loss. Also, some other basic layers such as flood control engineering, rescue sites and so on will be added to form a complete thematic map. Figure 7 shows the maps including water depth, velocity field and flood duration under a certain engineering condition. As shown in these figures, the submerge conditions are presented vividly.

(Equ. 10)

In the formula, L6 is the property loss of the residents suffered by flood; VP is per capita income. International Journal Publishers Group (IJPG)©

Zhong et al: The Information Management System Based on GIS for Evaluating Loss of Flood Hazard.

173

(1)

(1) water depth

(2) Fig. 8 Report of flood loss

F.

(2) velocity field

Three-Dimension Simulation of Flood Evolution The 3-D modeling of the Huangpu River Basin is set up utilizing the DEM data and remote sensing data of the region [13]. Based on digital terrain analysis and SceneControl of ArcEngine, the system simulates flood evolution dynamically with user-defined time step. Figure 9 shows the submerged condition 24 hours after the flood.

(3) flood duration Fig. 7 Thematic map

E.

Report creation Based on the flood loss evaluation, the module creates loss assessment report for the inundated towns and cities. And the data is calculated and analyzed to create relevant report for the local administrator for policies decision. These report created in the form of excel are outputted to the system printer, and all the data is stored in the system database. For example, the report of a certain flood situatoin is shown in shown in Fig. 8. As the figure shown, there two towns that suffered by flood disater, which are Jia shan and Wu jiang respectively. The total area, design flood, submerged area, submerged property, FDR and economic loss are listed in the report. In this situation, the submerged area of Jia shan is 242 km2, submerged property is 60,000 RMB, the FDR is 0.55, thus, the economic loss of the town is approximately 330,000 RMB. While, the economic loss of the city Wu jiang is a little lighter, which is only 30,000 RMB in all. This report is very important as it can provide comprehensive information of the flood disaster, which will supply useful suggestions to the decision makers. International Journal Publishers Group (IJPG)©

Fig. 9 3D Simulation of Flood Evolution

6. Conclusions (1) Through the loss assessment above, regions such as Pingwang, Fenhu are the areas vulnerable to flood disaster with a serious economic loss. And the results are agreed with the fact of the flood disaster occurred in history. (2) Based on GIS and second development with VB, the flood loss can be analyzed and assessed more precisely. And the system also helps the researchers to save time and improve work efficiency greatly. (3) As an important non-engineering measure, the system is useful for local government to offer support means for decision-making. It will provide a scientific method for flood management and disaster reduction and it can also offer a guideline for the establishment of evacuation routines. What’s more, it will be helpful for insurance agents to formulate premium policy according to different areas.


174

(4) As the rapid development of economic of the area, the social-economic data is changeable. As a result, the latest data is essential for the precise loss evaluation. And more factors such as vegetation, waste land, etc. should be considered.

References [1]

[2] [3]

[4] [5]

[6]

[7]

[8] [9]

[10]

[11]

[12]

[13]

Wang Lachun, Jiang Nan, Zhou Yinkang, “The evaluated loss model of flooding and waterlogging in Taihu basin”, (2003) Science of Surveying and Mapping, vol. 28(6), pp.35-38. (in Chinese) Wu Haoyun, “Flood hazard and Countermeasures of Taihu Basin”,(1999) Disater Reduction in China, vol. 9(1), pp.15-18. (in Chinese) Wu Haoyun, Jiang Nan, Yu Gaoyu, “The System Based on GIS for Evaluating Losses of Flood Disaster in Taihu Lake Catchment,” (2001) Journal of Lake Sciences, vol. 13 (2), pp. 127-134.(in Chinese) F. N. Correia, M. Fordham, Da Graca Saraiva, “Flood harad assessment and management,” (1998) Water Resources Management, vol. 12 (3), pp. 209-227. Gerardo Benito, Michel Lang, Mariano Barriendos, “Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation,” (2004) Natural Hazards, vol. 31, pp. 623-643. F. Forte, R. O. Strobl, L Pennetta, “A Methodology using GIS, Aerial Photos and Remote Sensing for Loss Estimation and Flood Vulnerability Analysis in the Super sano-Ruffano-Nociglia Graben, Southern Italy,” (2006) Environ. Geol. vol. 50, pp. 581-594. Tawatchai Tingsanchali, Mohammed Fazlul Karim, “Flood Hazard and risk analysis in the southwest region of Bangladesh,” (2005) Hydrological Processes, vol. 19, pp. 2055-2069. Zhao Xia, Wang Ping, Gong Qing, Huang Shifeng, “A GIS-based approach to flood risk zonation area,” (2000) Acta Geographica Sinica, vol. 55 (1), pp. 15-24.(in Chinese) Md. Monirul Islam, Kimiteru Sado, “Development Priority Map for Flood Countermeasures by Remote Sensing Data with Geographic Information System”,(2002) Journal of Hydrologic Engineering, vol.7(5), pp. 346-355. Hongliang Gou, Shuguang Liu, Guihui Zhong. “Research of Flood Risk Map Information Management System Based on ArcGIS”,(2010) Proceedings of the 3rd IEEE International Conference on Advanced Management Science, Chengdu, pp. 235-238.(in Chinese) Wang Lachun, Jiang Nan, Zhou Yinkang, “The evaluated loss model of flooding and waterlogging in Taihu Basin,” (2003) Science of Surveying and Mapping, vol. 28 (2), pp. 35-38. (in Chinese) Shanker Kumar Sinnakaudan, Aminuddin Ab Ghani, Mohd. Sanusi S.Ahmad, “Flood Risk Mapping for Pari River Incorporatin Sediment Transport”, (2003) Environment Modelling & Software, vol. 18, pp. 119-130. C. C Zheng, D. D Liu, and G. T. Liang, “ArcGIS - based 3D Visualization of Dongping Lake Flood Drowning,” (2008) Journal of Zhengzhou University Engineering Science, vol. 29, pp. 88-90.

Guihui Zhong was born in Liaoning, China, in 1971. He received the B.Sc. degree from Hohai University in 1996 and the M.Sc. degree in 2010 from Tongji University. She worked in Research Center for Fluvial and Coastal disasters Disaster Prevention Research Institute, Kyoto University from August 2009 to August 2010 as a visiting scholar. Now she is the lecturer of Hydraulic Engineering Department of Tongji University. Her current research interests include flood simulation, hydraulic engineering, and coastal engineering. Shuguang Liu was born in Jiangsu, China, in 1962. He received the M.Sc. degree in Hydraulic Engineering from Hohai University in 1989. He received Ph.D in Geography from Moscow State University in 1998. He worked in Tongji University from 1999 to 2001 as a Post-doctorial Researcher. Now he is the professor of Hydraulic Engineering Department and the director of the Institute of Harbor, Waterway and Coastal Engineering of Tongji University. He has hold and participated more than 20 research projects supported by national government and published more than 40 papers in recent years. His current research interests include water resources, flood hazard control, Geoscience, resources and environment, hydraulic engineering, coastal engineering. Hongliang Gou was born in Liaoning, China, in 1985. He received the B.Sc. and M.Sc. degrees from Tongji University in 2008 and 2001 respectively. His research interests include flood simulation, management of flood hazard, water resources.

International Journal Publishers Group (IJPG)©