N H Rao

National Academy of Agricultural Research Management

Hyderabad, AP, India

http://www.naarm.ernet.in

GIS based decision support systems in GIS based decision support systems in agricultural water managementagricultural water management

Outline

• GIS based DSS

• case studies of GIS based DSS in agricultural water management

• emerging concerns and way forward

Why GIS based DSS for water management?

Coupling of GIS with data and models in a

DSS allows a more scientific approach to

decision-making

Water science (models)

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

• both data and science are incomplete

• leads to input and output certainty

• decisions are under pressure

(Fig adapted from USGS)

USERDecisionsProblem

Input Information/ knowledge/ judgment

GIS based Decision Support System

Information/ Knowledge

Spatial data in

GISModels Reports

GIS based DSS - Components

Spatially variable data of natural resources, inputs and infrastructure

Spatially variable model parameters

expertise

Groundwater resources

assessment in canal

irrigated areas:

Godavari Delta Central

Canal Project

Case study 1

Ref: Chowdary,V.M et al (2003) GIS based decision support system for groundwater assessment in irrigation project areas, Agricultural Water Management, 62, 229-252

• 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

Problem definition

• 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

Process

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

Spatial data layers

Groundwater resources assessment in canal irrigated areas: Godavari Delta Central Canal Project

Spatial recharge data input to groundwater model

Observed and simulated groundwater levels (m)

Pre-monsoon Post-monsoon

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

Assessment ofnon-point-source pollution of groundwater (from fertilizer nitrate) in large irrigation projects: Godavari Delta Central Canal Project

Case study 2

Ref:

Chowdary,V.M.,Rao,N.H. and P.B.S.Sarma (2005) GIS based decision support framework for assessment of non-point source pollution 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.

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

Spatial distribution of seasonal nitrate pollutant loads (ppm) (Kharif)

Observed and simulated nitrate concentrations in groundwater (ppm)

Nitrate pollution loads and impacts on groundwater

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)

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

• uncertainty: different values exist for a quantity 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 distributions) at global and regional scales

• probability density functions (pdf) can be fit to the sampled distributions of climate variables (T, P, other) over regional grids for different times in future

Climate change: uncertainty

Source: Dettinger, 2005

pdf provide information for decision makers to assess uncertainties and risk, and design water management policies and structures

• 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

Climate change: dealing with uncertainty

Way forward - community of practice

Climate scenarios for India (gridded data sets from CMIP 5)

Climate Uncertainties on India grid (pdfs)

Hydrology model - SWAT

Crop model DSSAT/

statistical

Markets model (IFPRI/other)

Monte Carlo simulation for selected regions

Assess Uncertainties (pdfs) in water supplies, production, prices

Designs for water management for Sustainable intensification of agriculture

priorities and Implications for Policies and institutions

shared Data and Models

knowledge discovery

Capitalize on technologies

Thank You