Dr N.H.Rao Joint Direcotr - National Academy of Agricultural Research Management

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Dr N.H.Rao Joint Direcotr - National Academy of Agricultural Research Management

  1. 1. GIS based decision support systems in agricultural water management N H RaoNational Academy of Agricultural Research Management Hyderabad, AP, India http://www.naarm.ernet.in
  2. 2. Outline • GIS based DSS • case studies of GIS based DSS in agricultural water management • emerging concerns and way forward
  3. 3. Why GIS based DSS for water management?Nature of decisions:• important for economy and environment• natural and infrastructure water systems with feedbacks• spatially variable data, inputs and processes in both systems• uncertainty (data, weather, resources, processes)• decision making is complex : - partly data & knowledge driven - partly resource driven - partly experience driven (Fig adapted from USGS) Water science• both data and science are incomplete (models)• leads to input and output certainty Coupling of GIS with data and models in a• decisions are under pressure DSS allows a more scientific approach to decision-making
  4. 4. GIS based DSS - Components Problem Decisions USER Spatially Input Information/ Spatially variable data knowledge/ variable model of natural judgment parameters resources, inputs and GIS based Decision Support infrastructure System e r tis e exp Spatial Information/ data in Models Reports Knowledge GIS
  5. 5. Case study 1 Groundwater resources assessment in canal irrigated areas: Godavari Delta Central Canal ProjectRef: Chowdary,V.M et al (2003) GIS based decision support system for groundwater assessment in irrigation project areas,Agricultural Water Management, 62, 229-252
  6. 6. Problem definition• regional groundwater assessment requires estimation of recharge and groundwater flow in the underlying aquifer• recharge occurs both as percolation losses from fields and seepage losses from the water distribution network• percolation losses depend on weather (rainfall), soil properties, land use, and irrigation water use (canal water and groundwater)• seepage losses depend on the conditions of flow in water distribution system• all the factors (inputs and parameters) influencing recharge of groundwater vary spatially• GIS can map spatial distribution of recharge which then serves as input to regional groundwater flow model for simulating the groundwater levels
  7. 7. Process• design a GIS based framework to integrate data and models• divide project area into basic simulation units (BSUs): homogenous with respect to conditions that influence recharge processes (rainfall, soils, canal system, land use) by overlay operations in GIS• for each BSU:  use daily field soil water balance model to estimate percolation losses  use canal flow model (hydraulic model) to estimate seepage losses  recharge is sum of percolation and seepage losses• map spatial distribution of recharge over BSUs• mapped recharge is input to 2-dimensional groundwater flow model on a finite element grid and solved numerically to predict groundwater levels
  8. 8. GIS based framework for the assessmentof groundwater in irrigation project areas Spatial data layers
  9. 9. Groundwater resources assessment in canal irrigated areas:Godavari Delta Central Canal Project Spatial recharge data input to groundwater model
  10. 10. Observed and simulated groundwater levels (m) Pre-monsoon Post-monsoonThe framework can be used as a decision support system to assess thegroundwater resources and evaluate strategies for integrated management ofcanal and groundwater resources in the project area
  11. 11. Case study 2 Assessment of non-point-source pollution of groundwater (from fertilizer nitrate) in large irrigation projects: Godavari Delta Central Canal ProjectRef:Chowdary,V.M.,Rao,N.H. and P.B.S.Sarma (2005) GIS based decision support framework for assessment of non-point sourcepollution of groundwater in large irrigation projects, Agricultural Water Management, 75, 194-225.Chowdary,V.M., Rao,N.H. and Sarma, P.B.S. (2004) A Coupled soil water and nitrogen balance model for flooded rice fields.Agriculture, Ecosystems and Environment, 103, 425-441.
  12. 12. GIS based framework for the assessment of non-pointsource pollution of groundwater in canal project areas
  13. 13. Nitrate pollution loads and impacts on groundwater Spatial distribution of seasonal nitrate Observed and simulated nitrate pollutant loads (ppm) (Kharif) concentrations in groundwater (ppm)
  14. 14. Emerging issues/concerns• climate change – linking the global with the local• sustainable intensification of agriculture and water productivity• water and environmental quality• dealing with uncertainty• urbanization• groundwater depletion/recharge• multiple reservoir management• water governance• increasing data intensity (data–driven science)
  15. 15. climate change: state-of-art Source: Winkler et al, 2011 The fifth phase of the Climate Model Inter- comparison Project (CMIP5), now underway, provides access to state- of-the-art multi model/ multi scenario gridded datasets of climate change for future time periods
  16. 16. Climate change: uncertainty• uncertainty: different values exist for a quantity Source: Dettinger, 2005 at any time• climate uncertainty propagates to water, agricultural and social systems• current studies include statistical uncertainty between climate variables and outcomes (eg. water supplies, agricultural production)• do not include the large degree of climate uncertainty in existing projections of climate change itself• climate change models and scenarios provide a range of estimates of future climate (sampled pdf provide information for distributions) at global and regional scales decision makers to assess• probability density functions (pdf) can be fit to uncertainties and risk, and the sampled distributions of climate variables (T, design water management P, other) over regional grids for different times in policies and structures future
  17. 17. Climate change: dealing with uncertainty • representing uncertainties in future changes in climate as gridded pdf for India • Integrating pdf with state of art models of water resources agricultural productivity • provides improved scientific basis for assessing risk and for water management
  18. 18. Way forward - community of practice Climate scenarios for India (gridded data sets from CMIP 5) shared Data and Models Climate Uncertainties on India grid (pdfs) Capitalize on Monte Carlo simulation for selected technologies regions Crop model Markets modelHydrology model DSSAT/ (IFPRI/other) knowledge - SWAT statistical discovery Assess Uncertainties (pdfs) in water supplies, production, prices priorities and Implications for Designs for water management for Policies and institutions Sustainable intensification of agriculture
  19. 19. Thank You

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