1. GIS based decision support systems in
agricultural water management
N H Rao
National Academy of Agricultural Research Management
Hyderabad, AP, India
http://www.naarm.ernet.in

2. Outline
• GIS based DSS
• case studies of GIS based DSS in agricultural water management
• emerging concerns and way forward

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. 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. Case study 1
Groundwater resources
assessment in canal
irrigated areas:
Godavari Delta Central
Canal Project
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

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. 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. GIS based framework for the assessment
of groundwater in irrigation project areas
Spatial
data
layers

9. Groundwater resources assessment in canal irrigated areas:
Godavari Delta Central Canal Project
Spatial
recharge
data input to
groundwater
model

10. 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

11. Case study 2
Assessment of
non-point-source
pollution of groundwater
(from fertilizer nitrate)
in large irrigation
projects: Godavari Delta
Central Canal Project
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.

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

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. 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. 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. 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. 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. 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 model
Hydrology 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. Thank You