avswat - gis development

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AVSWAT- a Spatial Decision Support System for Land and water management and its application for watershed management in Bankura

district of West Bengal

Debapriya Dutta NRDMS Division, Department of Science and Technology

Technology Bhavan New Delhi- 110016. India.Tel:91-011-6962819(Ext. 202/262), Fax:91-011-6516076

[email protected]

Abstract Decision Support Systems (DSS) are defined as computer-based information systems designedto support decision makers interactively in thinking and making decisions about relativelyunstructured problems. Spatial Decision Support Systems (SDSS) , which are the integration of DSS and GIS was initiated by Densham and Goodchild ( 1988) are emerging as efficient tools for managing natural resources like land and water. AVSWAT ( Arc View- SWAT) , a user- friendlyPC based SDSS tool has been developed at the Black Land Research Center , Temple, Texas,USA integrating Soil and Water Analysis Tool (SWAT) and Arc View GIS version 3.0a softwarealong with Spatial Analyst version 1.1 extension. SWAT is a continuous time river basin or watershed scale model operating on daily time step. In the present study, the tool was applied in

digitally delineating watersheds in a block of Bankura district of West Bengal and then it was usedfor estimating potential water ,silt and crop yield from each of them. This would be helpful inprioritising the watersheds and presenting the results spatially for the district level decisionmakers.

Key Words:Spatial Decision Support System, AVSWAT, Watershed Management.

Decision Support Systems (DSS) are defined as computer -based information systemsdesigned to support decision makers interactively in thinking and making decisions aboutrelatively unstructured problems. Traditionally, DSSs have three major components, a database,a model base and a user interface as depicted in Fig. 1a. An extension of the DSS concept,Spatial Decision Support Systems (SDSS), which are the integration of DSS and GIS (Fig. 1b)was initiated by Densham and Goodchild ( 1988).

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mailto:[email protected]



GIS is a general purpose technology for handling geographic data in digital form, with the abilityto preprocess data into a form suitable for analysis, to support analysis and modelling directly,and to post-process results (Goodchild, 1993).

A significant capability of the SDSS is the ability to use spatial analysis and display tools with thesectoral models and that would form the model base of SDSSs. The modeling capability allowsthe user of the SDSS to simulate changes in objects and attributes. The database component of the SDSS can supply input data for the models. After the models are run, the resulting output canbe written to the database for later display via the user interface , in tabular, chart or map form.For planning purposes, this ability to dynamically change information, forecast and performsensitivity analysis is essential.

Both GIS and DSS have been widely used in natural resources management. Watkins andMckinney (1995) presented a review on DSS in water resources; and Goodchild et al. (1996)described a comprehensive study of GIS in water resources and environmental engineering.Singh and Fiorentino (1996) gave a comprehensive review of GIS in hydrology.

In summary, SDSS provide unique advantages for land and water resources management in thefollowing aspects:

1. spatial representation, that is representing the spatial relations of the real world in avisual and analytical form;

2. comprehensive database, which is the basis for the integration of socioeconomic,environmental and physical components of the real world; and



3. modeling capability, which can integrate simulation/optimization techniques to solvecomplex natural resources management problems.

These advantages make SDSS a proactive tool for sustainable natural resources management.Inthe present study, while selecting an appropriate model for developing Spatial Decision SupportSystem .

for land and water management, the following considerations were made :

• the ability to model at scales ranging from watershed to basin (regional) scales

• the ability to model water quantity and quality, silt production and crop growth

• the ease of use of the model and ready availability of inputs data

• the ability to link to Geographical Information System (GIS)

• the documentation and degree of support available.

Based on the above considerations, the Soil and Water Assessment Tool (SWAT) model wasselected. The SWAT model is a combination of the SWRRB, GLEAMS and ROTO models andhence it is able to model both the hydrology and water quality of a watershed (Arnold et al.,

1995). The model is reported to be able to operate on both a raster and sub-watershed(hydrologic response unit) basis (Arnold et al., 1995).In addition, the model is linked to GISpackages like GRASS (Geographic Resources Analysis Support

System) via the SWAT-GRASS interface (Srinivasan and Arnold, 1993) and ARC-View throughthe SWAT-ARC View interface (Diluzio et al , 1997), thus easing the task of data input and outputdisplay.

The Model

Soil and Water Assessment Tool (SWAT)SWAT is a river basin or watershed scale model developed by the USDA Agricultural ResearchService (ARS). SWAT was developed to predict the impact of land management practices on

water, sediment and agricultural chemical yields in large complex watersheds with varying soils,land use and management conditions over long periods of time. SWAT is a continuous timemodel operating on daily time step. The command structure of SWAT is presented in Fig.2.

Model ComponentsThe sub-basin components of SWAT can be placed into eight major divisions--hydrology,weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and agriculturalmanagement.

1. Hydrology - Surface runoff, Percolation, Lateral Subsurface Flow, Groundwater Flow,

Evapo-transpiration, Snow melt and Transmission Losses

2. Weather - Precipitation, Air Temperature, Solar Radiation, Wind Speed and Relative

humidity.

3. Sedimentation - Sediment Yield.

4. Soil temperature - Daily average soil temperature is simulated at the center of each soil

layer for use in hydrology and residue decay.

5. Crop growth

6. Nutrients - Nitrogen and Phosphorus

7. Pesticides - Gleams technology for simulating pesticide transport by runoff, percolate, soil

evaporation and sediment was added to SWAT.

8. Agricultural Management - Tillage and residue management and Irrigation.



9. Routing component - Channel flood routing, Channel sediment routing, Channel nutrient

and pesticide routing, Reservoir Routing, Reservoir water balance and routing, Reservoir sediment routing, Reservoir nutrient and pesticides.

AVSWAT (ARC View-SWAT Interface)Use of a basin scale model like SWAT needs great amount of time, expertise and cost for

acquiring input data, running the model and analyzing the results. Due to the intrinsic spatial-temporal variability of watersheds, GIS technology is an essential and efficient method of collecting, storing and retrieving input data required for simulation models. GIS can elucidatelandscape characteristics (e.g. topography, soil, climate, land cover and management) andeffects of agricultural activities overlaying intrinsic hydrological attributes.

Considering the above, a user- friendly, PC based tool, AVSWAT has been developed at theBlackland Research Center integrating SWAT and ArcView GIS version 3.0a software along withSpatial Analyst version 1.1 extension.

The SWAT-ArcView interface is a tight coupling between a model and GIS (Burrough, 1995). Theexport of data from GIS to the SWAT model and the return of results for display are accomplishedby Avenue routines that are addressed directly by the interactive tools of GIS (e.g. setting up

parameter values via customized menus) and the exchange of data is fully automatic. Figure 3shows the overview of the tight coupling between SWAT and ArcView GIS. The SWAT ArcViewsystem consists of 3 key components: (1) preprocessor generating subbasin topographicparameters and model input parameters; (2) editing input data set and execute simulation; (3)postprocessor viewing graphical and tabular results.

System/Software Requirements

• Microsoft Windows 95 or NT 4.0 operating system with minimum 100 MB hard diskspace.

• Arc View 3.0a

• Spatial Analyst Extension 1.1

• Dialog Designer Extension 3.0a.

The Study AreaIn the present study, the Chhatna block of Bankura (Fig. 4) has been selected for detailed study.Chhatna lies between 23 Degrees 11 Minutes and 23 degrees 30 Minutes north latitude, andbetween 86 Degrees 48 Minutes and 87 Degrees 2 Minutes east longitude. Total geographicalarea of the block is 448.1 sq km. There are total 288 mouzas in the block. There are five maindrainages or streams within the the block, from north to south they are Gandheswari,



Darakeshwar, Kansachara, Arkasa and Chagalkuta. The district though having an averagerainfall of 1300 mm are considered as drought- prone , because of poor water management.

Information Need Assessment for Watershed ManagementIn order to develop such a SDSS for watershed management , initially a workshop was organizedwith the official ls of the district line departments involved in watershed management activities .The objectives of such interaction were to assess the information need of the user departments inthis sector and sensitise them about such tools. Apart from this, all the schemes pertinent towatershed management were also scurtinised to assess the information need for them. On suchexercise, it emanated that for district-level watershed programmes basic information productsexpected out of such tools are:

• Maps of watersheds with sub-watershed within administrative boundaries.• Maps showing estimates of available water or water yield from such watersheds or sub-

watersheds for planning of proper water management.

• Maps showing estimates of silt yield from such units for prioritization of watersheds or sub-watersheds for soil conservation works.

AVSWAT was implemented with the data collected from different sources to generate the aboveproducts to show its utility in watershed management programmes.

Running SWAT through the Arcview- SWAT interface (AVSWAT) using Chhatna data. Using the input maps and data files as discussed in Fig.3 for the study area AVSWAT was runthrough the interface.Watreshed delineation was started by using the Digital Elevation Model (DEM) grid and then

removing any depression.. Then drainage network was delineated with a threshold value of 100as it was found to be optimum for defining minimum numbers of cells to start delineating astream. After the drainage network was delineated , the Chhatna boundary theme was added tothe view and overlaid on the drainage network (Fig. 5).



The outlet point for delineating each watershed was selected by adding or deleting point(s) on themain stream intersecting the block boundary. Then, sub-basins and watersheds were delineated(Fig. 6) to generate watershed map for the Chhatna block .

The numbers of watersheds delineated for different sub-basins are as follows:Gandheswari - 11Darakeshwar - 15Kansachara - 9Arkasa - 3Chhagalkuta - 3

It is seen that some of the watersheds are beyond the block boundaries as they are a

hydrological unit and not restricted to administrative boundary. Since land use and soil data wasnot available for the present study beyond the block boundary, while delineating the watersheds itwas kept in mind so that each of the watersheds at least partially fall within the block boundary.

Applications developed for Watershed management in Bankura.



Water yield and silt yield maps for the watersheds in Chhatna Block

Water yield is the amount of water that leaves the sub-basin and contributes to stream flow at thesub-basin outlet. it is the sum of surface runoff, lateral flow in the root zone, and ground water flow minus transmission losses in channels within the sub-basin minus any pond abstractions.

Sediment yield is the sediment from a sub-basin that reaches the sub-basin outlet. This is theamount that would be measured at the sub-basin outlet (pond deposition has already beensubtracted).

Sediment yield is computed for each sub-basin with the Modified Universal Soil Loss Equation(MUSLE) (Williams and Berndt, 1977).

Maps Generation

• After watershed delineation, soil and land use grid themes were overlaid andHydrological Response Unit (HRU) were defined on the basis of homogeneity of soil andland use. Then each watershed was linked to the nearest meteorological station.Subsequently, input files required for running SWAT were generated by using AVSWAT

and then SWAT was run through the interface.• For each of the projects, selecting the Read results option of Simulation pull down menu,

brought a tiled result window containing . bsb, . rch, output map and the project window .

• Theme in the output map was converted into silt yield and water yield shape files. Theseshape files were added in two new views named silt yield and water yield and viewproperties were properly set. From legend editor, Unique value legend type was selectedand then WYLD and SYLD fields were selected to generate the water yield and silt yieldtheme for each sub-basin respectively and converted into shape files.

• The water yield and silt yield shape files of five different sub-basins were added in twodifferent new views and the Chhatna block boundary was overlaid on each of them. Thenlayouts were made to prepare the final maps (Fig.7 and 8).



Potential Crop production map for the Gandheswari watershed in Chhatna Block . SWAT utilizes a single plant growth model to simulate all types of land covers. The model is ableto differentiate between annual and perennial plants. Annual plants grow from the planting date to

the harvest date or until the accumulated heat units equal the potential heat units for the plant.Perennial plants maintain their root systems throughout the year, becoming dormant after frost.They resume growth when the average daily air temperature exceeds the minimum, or base,temperature required. The plant growth model is used to assess removal of water and nutrientsfrom the root zone, transpiration, and biomass/yield production. The potential increase in plantbiomass on a given day is defined as the increase in biomass under ideal growing conditions.The potential increase in biomass for a day is a function of intercepted energy and the plant'sefficiency in converting energy to biomass. Energy interception is estimated as a function of solar radiation and the plant’s leaf area index. The potential yield is estimated by multiplying thepotential biomass production with Harvest Index i.e. Economic yield/ Biological yield.

In the present study SWAT was implemented for estimating the potential production of Amanpaddy, which is the main crop of the area.

The Basi.. sbs file which contains HRU wise estimate of potential biomass production and cropyield for a sub-basin or watershed was converted into Basi.dbf and imported into the project Itwas then joined to the attribute table of the water yield map. From legend editor, Unique valuelegend type was selected and then the field for Total Crop production was selected to generatethe crop production theme for each sub-basin respectively and converted into shape files. A mapfor potential production of Aman paddy for the Gandheswari sub-basin is in Fig.9.

Application of the SDSS for Watershed Management. In view of the stress on district level planning in the country, watershed based planning of naturalresources is becoming increasingly important activity in a district. For watershed management,the foremost application of a SDSS like AVSWAT is in digitally delineating the watersheds andsub-watersheds , at present which is entirely dependent on the experience and skill of the districtofficials. The initial investment in digital data generation justifies subsequent lesser investment in

repeated survey for the purpose and improvement in watershed delineation.

In a SDSS like AVSWAT, the SWAT model provides the capability of quantitatively simulatingland and water related processes important for watershed management like potential water andsilt yield . The spatial representation of the results in the form of maps provides added informationto the Decision-makers. The silt yield map presents potential silt production rate from differentwatersheds, thus, depicts the erosion status of them and helps in prioritizing the watersheds for soil and water conservation measures. Similarly, the water yield map presents the amount of water that can be available from each watershed under different management options, thus canhelp in land use planning, water management planning and in deciding proper land and water



management options. A potential crop production map along with the other two maps could be of immense help in crop management and land use planning

Apart of producing these static spatial representations, the SWAT model helps in generatingalternate management scenarios for land and water management by changing the values of inputparameters. Thus, providing a dynamic tool to the decision-makers involved in watershedmanagement programmes in a district.

The SDSS along with the products were demonstrated to the district level decision-makersthrough a workshop organized at Bankura district.

References

• Arnold, J.G., J.R. Williams, and D.R. Maidment. 1995. A continuous-time watershed sediment routing model for large basins. ASCE J. Hydr. Eng.

• Burrough, P.A. 1995. Opportunities and limitations of GIS-based modeling of solutetransport at the regional scale. In: Applications of GIS to the Modeling of Non-Point Source Pollutants in the Vadose Zone, ASA-CSSA-SSSA Bouyoucos Conference,Mission Inn, Riverside, CA, May 1-3, 1995.

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Densham, P.J., and M.F. Goodchild. 1988. Spatial decision support systems: A researchagency. GIS/LIS’88 Proceedings Volume No.2 Bethesda, Maryland: American Congresson Surveying and Mapping. November December 2, 1988.

• Di Luzio, M., R. Srinivasan and J.G. Arnold. 1997. An Integrated User Interface for SWAT Using ArcView and Avenue. Presented at the August 1997 Annual International Meeting of ASAE in Minneapolis. Paper No 972235, pp. 15.

• Goodchild, M.F. 1993. Data models and data quality: Problems and prospects. InEnvironmental modelling with GIS, ed. M.F. Goodchild, B.O.Parks, and Steyaert, 8-15.New York: Oxford University Press.

• Goodchild, M.F., L.T.Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S.Glendening. 1996. GIS and environmental modelling: Progress and research issues, ed.M.F.Goodchild, L.T.Steyaert, B.O.Parks, C. Johnston, D. Maidment, M. Crane, and S.Glendening. Fort Collins, Collins, Colorado: GIS World Publication.

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Nelson, J.E., N.L. Jones and C. Smemoe. 1997. From a Grid or Coverage to aHydrograph: Unlocking Your GIS Data for Hydrologic Applications. Proc of ESRI User Conference 1997, pp. 12.

• Singh, V.P., and M.Fiorentino. 1996. Geographical information systems in hydrology.Dordrecht: Kluwer Academic Publishers.

• Smedema, L.K, and D.W. Rycroft. 1983. Land Drainage -- Planning and Design of Agricultural Drainage Systems. Cornell Univ. Press, Ithaca, NY. 376 pp.

• Srinivasan, R. and J.G. Arnold. 1993. Integration of a basin scale water quality model with GIS. Water Resources Bulletin (Accepted for publication).

• Watkins, D.W., and D.C.Mckinney. 1995. Recent developments associated with decisionsupport systems in water resources. Reviews of Geophysics (Supplement), July, 941-948.

• Williams, J.R. and H.D. Berndt. 1977. Sediment yield prediction based on watershed

hydrology. • Trans .ASAE 20(6): 1100-1104.

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