1. GIS Based DSS for Natural Resources Management N H Rao

2. Agenda  Why DSS  DSS definition and concepts  GIS based DSS  Examples

3. Decision support and DSS Decision Support (DS) is a broad, generic term that encompasses all aspects related to supporting people in making decisions DSS is an interactive computer-based system that aids the process of decision making DSS range from systems that answer simple queries to systems that model complex human decision making process.

4. Why DSS in agriculture? Increasing importance of knowledge as a factor of production in agriculture Significant value addition, from both economic and environmental points of view, results from timely knowledge based decisions Large variety of data, information and knowledge need to be shared and assessed Available data and knowledge are incomplete and uncertain Often decisions are under pressure DSS allow a more knowledge based approach to decision-making in agriculture

5. DSS in agriculture: Who needs them ? Farmers Extension advisers Govt. Departments Scientists Planners Consultants Insurance companies Industry/agribusiness What types of decisions ? Selection of crops and cropping systems Crop management: Land use planning Variety selection Input management Farm management Natural resources management Marketing and distribution

6. DSS improves improve nature and speed of knowledge sharing and management decisions across the agri-value chain

7. Agricultural Decisions – two components Structured component Unstructured component Strategies through computer programs, databases Strategies through analogy, induction, learning, heuristics Models / databases Experience/ Expert systems/ knowledge bases/

8. Even apparently structured problems can have unstructured components because: Models are not universal Models have assumptions Model parameters are uncertain Model data input can be uncertain Leading to: Data or input uncertainty Interpretation or output uncertainty

9. Decision Support Systems - Definition: Interactive computerized tools under user control that help in making decisions Have analytical capabilities (graphical and modelling) Allow combining computer input and output with judgment to produce meaningful information Are custom fit (no general package)

10. DSSAT (Decision Support System for Agrotechnology Transfer): suite of models, data and software that: integrates effects of soil, crop phenotype, weather and management options allows users to ask "what if" questions and simulate results in minutes on a desktop computer Example of an Agricultural Management DSS - DSSAT

11. DSSAT - structure Fig source: DSSAT manual, Version 4; ICASA

12. Information needed for agricultural management is spatial and diverse Current information is to be used in a historical event perspective GIS holds spatial information in independent layers and integrates them GIS provides the medium in which various issues can be framed and resolved Including the Spatial Dimension: GIS GIS as an integrating Technology Fig source: FAO

13. …..leading to fundamental changes in the way we visualize and analyze information for decision-making GIS is a knowledge integrator Bringing together data from different sources, with a common location component, allows -------- vital linkages to be made between apparently unrelated activities, to reveal trends and patterns that are not apparent with tabular databases (knowledge generation)

14. GIS based DSS - Components

15. GIS use in decision support – levels of use with its strong spatial data management, analysis & visualization capabilities, GIS provides a very natural and effective platform for knowledge based decision support in agriculture

16. Level 1 use: NAARM Geospatial Library

17. 17 Level 1 use: NAARM Geospatial Library

18. 18

19. Level 1 use: Monsoon rainfall analysis - 2015

20. 20 Level 2 use: AP and TS - Agroecological regions and districts

21. 21 Level 2 – Use : AP and TS – Soil properties

22. District Name Very Low <50 mm/m Low 50-100 mm/m Medium 100-150 mm/m High 150-200 mm/m Very High >200 mm/m Adilabad 262412122 Anantapur 39128524 Chittoor 20618198 Cuddaph 4874027 East Godavari 1115281328 Guntur 17863426 Karimnagar 15833036 Khammam 2412182711 Krishna 83540 Kurnool 2689249 Mahabubnagar 917112229 Medak 19220145 Nalgonda 15387135 Nellore 9313621 Nizamabad 312211228 Prakasham 32420351 Ranga Reddy 27188531 Srikakulam 3914711 Visakhapatnam 151649183 Vizianagaram 151023483 Warangal 201832234 West Godavari 76143239 Total AP 2213122124 Level 2 – Use : AP and TS – Soils data

23. 23 1971-87 1988-03 Level 2 use: AP&TS and Orissa - Trends in rice productivity growth by AEZ

24. 24 Level 2 use: AP&TS and Orissa - Trends in Fertilizer use efficiency by AEZ 1971-87 1988-03

25. 25 Level 2 use : AP and TS: Agroecosystems – districts, soils, and agroecological subregions

26. Level 3 use: GIS based DSS : Examples/Case studies ……..

27. Case study 1: Groundwater resources assessment in canal irrigated areas Godavari Delta Central Canal Project Problem definition: Estimation of recharge and groundwater flow Recharge = percolation losses from fields + seepage losses from canal network percolation losses = f (rainfall, soil properties, land use, water use (canal water + groundwater) ) seepage losses = f(conditions of flow in water distribution system) All factors (inputs and parameters) influencing recharge vary spatially GIS can map spatial distribution of recharge which then serves as input to regional groundwater flow model for simulating the groundwater levels

28. Design a GIS based framework to integrate data and models Project area is divided into basic simulation units (BSUs) that are homogenous with respect to conditions that influence recharge processes (rainfall, soils, canal system, land use) by overlay operations in GIS For each BSU:  daily field soil water balance model estimates percolation losses  canal flow model (hydraulic model) estimates seepage losses  recharge is sum of percolation and seepage losses Map spatial distribution of recharge Mapped recharge is input to 2-dimensional groundwater flow model on a finite element grid and solved numerically to predict groundwater levels The framework can be used as a decision support system to assess the groundwater resources and evaluate strategies for integrated management of canal and groundwater resources in the project area Process

29. GIS based framework for the assessment of groundwater in irrigation project areas

30. Basic simulation units (BSUs) from overlays of spatial data layers

31. Percolation losses from rice fields and total recharge distribution

32. Spatial distribution of annual recharge rates for the study area (m3/s)

33. Observed and simulated groundwater levels (m) May 1993May 1994 May 1995 November 1995 1992-93 1993-94 1994-95

34. Case study 2 : Assessment of non-point-source pollution of groundwater in large irrigation projects: Godavari Delta Central Canal Project Problem definition: Nitrates from fertilizers, dissolved in percolation losses from rice fields, are a significant source of groundwater pollution Concentration of nitrates in groundwater depends on total recharge, pollution (nitrate) loading, groundwater flow and solute transport within the aquifer Recharge, nitrate loads, and groundwater flow and solute transport vary spatially with weather, soils, land use and aquifer conditions GIS can map spatial distribution of recharge and nitrate loadings in recharge which then serve as input to regional groundwater flow and transport models for simulating the groundwater levels

35. Design a GIS based framework to integrate data and models Project area is divided into basic simulation units (BSUs) in GIS For each BSU:  daily field soil water balance model estimates percolation losses  daily nitrogen balance model estimates nitrate loads in percolate  canal flow model (hydraulic model) estimates seepage losses  recharge is sum of percolation and seepage losses Map spatial distribution of recharge Map spatial distribution of nitrate pollution loads Mapped recharge is input to 2-dimensional groundwater flow model on a finite element grid and solved numerically to predict groundwater levels Mapped nitrate loads are input to 2-dimensional groundwater transport model on a finite element grid and solved numerically to predict nitrate concentrations The framework can be used as an integrated DSS to evaluate alternate strategies for water and fertilizer for sustainability of productive agriculture Process

36. GIS based framework for the assessment of non-point source pollution of groundwater in irrigation canal project areas

37. Spatial distribution of seasonal nitrate pollutant loads (ppm) and Observed and simulated nitrate concentrations in groundwater (ppm) )

38. Case study 3 : Real time water demand estimation in Patna Canal System, Sone Irrigation Project, Bihar Problem definition Water supplies reach fields through a network of main canals, branch canals and distributaries. Distributary is last point of control of main system. Distributaries and canals operate in cycles of 1-3 weeks. Water releases based on demands of individual distributaries, and aggregated for branch canals and main canals after deducting transmission losses. Operational efficiencies depend on extent to which irrigation supplies match water demands Obtaining quick, systematic and realistic estimates of water demands for different distributaries in real time at the beginning of every irrigation cycle is difficult because of spatial variations in weather, soils and crops GIS can be used to obtain real time estimates of irrigation demands of distributaries based on current season information of weather, weather forecasts, crops and soils

39. Design a GIS of the canal system and framework to integrate data and models For each distributary, a daily field soil water balance model estimates field level crop-water requirements The soil water balance model is linked dynamically to GIS of the canal system to estimate irrigation releases for any distributary on- line by simple interactive selection of the distributary in the GIS The selection automatically identifies (from attribute table in GIS) relevant input data files for the soil water balance model (rainfall, soil and crop data files) for the selected distributary The soil water balance model is run at daily time steps in two stages: with current season data of daily weather up to start date of irrigation cycle with forecast data of weather to end of irrigation cycle Process

40. Framework for dynamic user-GIS-model linkages in DSS

41. GIS-model output for selected distributary Irrigation indents (required releases) for distributary in different irrigation cycles

42. 42 Creation of Digital Database of Watershed Digitizing Watershed map for  Boundary  Settlements  Water tanks Stream Network ContoursSoilsLand Use/Land Cover Rainfall Distribution DEM Flow Direction grid Sink grid Watershed delineation Grid to Coverage Conversion Sub Watershed Boundary Hydrological Response Units (CN Unit) Runoff Computation and Thematic Mapping Rainfall data from database OVERLAY Overlay Case study 4: GIS based DSS for Watershed Management –Assessment of runoff distribution

43. 43 GIS based runoff estimation

44. Thank you