1. science
2. ecology
3. pollution

TB-ICN: 88/2011
DECISION SUPPORT SYSTEMS IN WATER
RESOURCES MANAGEMENT - A REVIEW
A. Sarangi
D. S. Bundela
Water Technology Centre
Indian Agricultural Research Institute
New Delhi-110012
NAIP Sub project “Decision Support System for Enhancing
Productivity of Irrigated Saline Environment using Remote
Sensing, Modelling and GIS”
TB-ICN: 88/2011
DECISION SUPPORT SYSTEMS IN WATER
RESOURCES MANAGEMENT - A REVIEW
A. Sarangi
D. S. Bundela
Water Technology Centre
Indian Agricultural Research Institute
New Delhi-110012
NAIP Sub project “Decision Support System for Enhancing
Productivity of Irrigated Saline Environment using Remote
Sensing, Modelling and GIS”
ii
Message
Dr H.S. Gupta
Director, IARI, New Delhi-12
Water is the elixir of life and need to be managed judiciously in agriculture for
achieving sustainability in agricultural production. Besides this, use of modern tools and
techniques in data collection, interpretation and analysis within a decision support
framework assists in generation of alternate scenarios for efficient utilization of natural
resources. In this context, it is encouraging that Dr Sarangi and Dr Bundella have
brought this technical report highlighting the use of geospatial tools and modeling
techniques in development of Decision Support Systems (DSS), which is an integral part
of Information and communication technology. In this publication, the concepts used in
development of the DSS and the review of the DSSs developed for management of land
and water resources will not only be useful for the scientific community but also the
results of these can be disseminated to the farmers for solving need based problems. In
present scenarios of innovations in computation technologies, the review of developed
DSS and the protocols of DSS development discussed in this publication will add to
existing knowledge and can be used for development of DSS for enhancing agricultural
productivity using the data acquired from different experiments on agricultural water
management.
(H.S. Gupta)
iii
NATIONAL AGRICULTURAL INNOVATION PROJECT
Indian Council of Agricultural research (ICAR)
Krishi Anusandhan Bhawan-II
Pusa Campus, New Delhi – 110012
Phone: 91-11-25842380
Fax:
91-11-25842380
E mail: [email protected]
Foreward
Land, water and vegetation are important building blocks for socio economic
development of a nation. These precious natural resources are now subjected to over
exploitation and use due to steep population growth and unscientific management. There is no
substitute for water and land resources, so the efforts should be made for scientific
management of these natural resources for sustainable production. sustaining our present level
of production using the integrated agricultural input management practices, genetically
improved seeds, bio-technological and molecular biology related approaches to sufficiently
produce food, fibre, fodder, fuel, fish, forest and flower resources will not be feasible vis-à-vis
this burgeoning population growth. In this context, the application and development of scientific
methods for conservation of soil, water and vegetation resources along with transformation of
degraded lands, wastelands and watersheds under active production process will ensure
sustainable production to meet the demands of ever increasing population of India. In this
scenario, management of natural resources will play a significant role to ensure sustainable
production and attainment of food security and economic stability of our country. Available
estimate reveal that about 121 Mha of land in our country is degraded due to soil erosion and
about 8.4 Mha had soil salinity and water logging problems. Therefore, development of
technologies and best management practices to utilize these degraded lands is the need of the
hour. To accomplish this, the available technologies need to be clubbed together within a
holistic framework and plausible scenarios need to be developed and implemented at different
locations. The geospatial techniques using the GIS, RS and GPS tools are widely used for data
acquisition and analysis leading to generation of a large data base. With the basic premise of
“better information leads to better decision”, these data sets are linked together to develop
Decision Support Systems (DSS) for judicious management of the natural resources. So, the
DSSs’ developed by integration of data, models and interpretive routines plays a significant role
in Information Communication and Dissemination System (ICDS) and are identified as the
frontier science in ICAR Vision 2030 document. In this context, the effort made in this manual to
review a significant number of DSSs’ developed by experts pertaining to hydrology, irrigation
and watershed management in particular and management of water resources in general will be
very useful for the academicians interested in this area of research. The manual developed
under the NAIP sub project entitled “ Decision Support System for enhancing the productivity of
Irrigated Saline Environment using RS, Modelling and GIS” highlights the state-of-art DSSs’
developed and used by different target groups pertaining to judicious management of natural
resources. The manual presents the input data requirement, the system architecture and output
of the DSSs besides the information on its programming language and availability for better
understanding by the reader.
(R.C. Agarwal)
National Coordinator, Component-I, NAIP Project
Room No. 514, Krishi Anusandhan Bhawan (KAB) - II
iv
Preface
Agricultural and environmental scientists face the daunting task of increasing the
productions of food, fodder and fibre without degrading the environment irreversibly in the
production process. This task is imperative due to the ever increasing population growth, which
puts tremendous pressure on the natural resources of any region. Management of the natural
resources has become a contentious and many times a perplexing issue throughout the world.
This manual proposes that a Decision Support System (DSS) has the capability of and a
significant role to play in enhancing the production and productivity by judicious management
of natural resources. Multifarious, interactive and many times competing demands on natural
resources can be effectively addressed through a DSS to meet society’s changing requirements.
Decision support systems (DSS) have evolved over the past four decades from theoretical
concepts into real world computerized applications. Gradually, more sophisticated applications
of computer-based DSS have been evolved and they have been adopted in diverse areas to
assist in decision making and problem solving. DSS architecture contains three key components
namely, a knowledge base, a computerized model, and a user interface. DSS simulates cognitive
decision-making functions of humans based on artificial intelligence methodologies in order to
perform decision support functions. The application of DSS in agriculture covers many domains
such as disaster management based on principles of ecology; biodiversity conservation;
reduction of air, soil and water pollution and judicious exploitation of natural resources. This
DSS provides complex environment management and public dissemination of environmentrelated information. By combining knowledge bases with inference rules, DSSs are able to
provide suggestions to end users to improve decisions and outcomes. The objective of this
manual is to present the novel work on the design and implementation of information systems
in general and Decision Support System in particular for agriculture and environment. The main
goal is to present how the new technologies can improve the construction of these complex
systems. Various case studies cited address issues of analysis and design of DSS under three
heads: Irrigation Water Management at farm level, Natural Resources Management in
watersheds and Integrated Water Resources Management in river basins. Topics discussed in
this manual include various Decision Support Systems on sustainable watershed management,
water quality and ground water level management, land use and land cover analysis for
sustainable development, land reclamation, water demand and supply management in
hydrological basins and for planning and management of irrigation command areas and large
irrigation schemes. This manual presents authentic and fresh reviews on DSS that adds value to
the domain, solves the problem of finding clear sources of information for the DSS researchers
of the future, and provides impetus to pursue the study and development of DSS that helps
policymakers, end users and organizations as they make their decisions.
(A. Sarangi)
Senior Scientist, WTC, IARI,
New Delhi - 110012
v
Contents
Foreward
iii
Preface
iv
Chapter I
Introduction
1
Chapter 2
DSS developed for management of Irrigation water at
farm level for enhancing agricultural productivity
4
Chapter 3
DSS for Integrated Watershed management
16
Chapter 4
DSS for Irrigation and flood water management at
basin level
27
Chapter 5
Conclusions
47
References
48
vi
Chapter 1
Introduction
Management of water resources has become very important due to its increasing scarcity
and rising demand. Availability and development of water resources need to be considered vis-àvis domestic needs, irrigation, recreational needs, cost, global climate change and water pollution.
In general, natural resource development, use and management decisions involve multiple
conflicting objectives and criteria and incommensurable units for measuring goods and services.
Increasing food demands due to high rate of population growth and major changes in the socioeconomic structure have created an urgent need to develop new and revise existing agricultural
systems and practices. India is characterized by a high population with a growth rate of about 2%
per annum. The restless onslaught of demographic pressure on India's natural resources and high
production gains limited only to the well endowed irrigated areas, have however put a question
mark on the stability and sustainability of Indian agriculture (Katyal et al., 1996). Now, more
than ever, decision makers at all levels need an increasing amount of information to help them
understand the possible outcomes of their decisions and develop plans and policies for meeting
the increasing demand of food requirements without damaging the natural resources base. In
India, it is estimated that on an average 16.4 tones of soil per ha in a year are lost through erosion
i.e. more than 5,000 million tons of topsoil is eroded annually. About 29% of the eroded soil is
lost permanently into the sea, 10% gets deposited in the reservoirs reducing their capacity by 1–
2% every year and the remaining 61% gets displaced from one place to the other over the land
surface (Mandal and Sharda, 2011). A close look at the present health of the soil and water
resources reveals their wanton misuse and consequently, a degraded environment. Almost 173.64
mha covering a little over half of the geographical area of the country, are threatened by various
types of degradation such as salinity, alkalinity, water logging, ravines and gullies, areas under
ravages of shifting cultivation, desertification, etc. Our forests and grasslands have been over
exploited. Frequent occurrences of floods and droughts in different parts of the country are
evidence of improper land use in the catchments and inadequate conservation of rainwater. The
problem of land degradation has brought us face to face with the ever increasing depletion of the
productivity and the basic land stock through nutrient deficiencies on the one hand and the ever
growing demand for food, fodder, fiber, fuel, land based industrial raw materials and may nonfarm land uses on the other hand. Soil, water and vegetation are three basic natural resources
essential for economic and social development. Hence management of natural resources has
become a contentious and conspicuous issue throughout the world. The natural environment is the
basis of all economic activity and provides human with food and water, raw materials needed for
the production of consumer goods and services, and ecosystem goods and services. Rapid
increases in the agricultural productivity in the past are not expected to continue in the future due
1
to reduced availability of land and water resources and land degradation, which can adversely
affect human welfare.
Currently, the increasing population and constructions such as roads, dams, reservoirs,
and mining and decreasing forest cover is a major cause of irregularity in rainfall, flooding
hazards in the rainy season and drought in some periods of the growing season. Flooding and
drought have been more significant in the recent years. The fundamental issue is to develop high
productivity and carrying capacity of the catchments or watershed whilst achieving acceptable
environmental quality and protection of the land and water resources. In this context, there is a
need of multidisciplinary and holistic management of soil, water and vegetation resources in a
watershed system to enhance biomass production in an eco-friendly manner. This is better done
through the development and use of of computer based information system for assisting the
decision makers in taking appropriate decisions of natural resource management. The information
system developed is called as Decision Support System (DSS) that provides information in a
given domain of application by means of analytical decision models and access to databases, in
order to support a decision maker in making decisions effectively in complex and ill-structured
tasks. Decision Support Systems (DSS) are "interactive computer based systems that help
decision makers utilize data and models to solve unstructured problems (Turban, 1995). These
tools improve the performance of decision makers while reducing the time and human resources
required for analyzing complex decisions.
DSS development methods
DSSs are developed broadly in four different ways, which are,
1. A spreadsheet and data base software based DSS, which uses the built in macro
programming language to generate alternative scenarios of the data sets entered in the
spreadsheet. The DSS utilizes the standard estimation procedures and the
“if…Then…Else” programming construct coded in the spreadsheet built-in macro
programming languages to generate scenarios as a response to user defined quarries. For
example, the data of different soil, plant, water and climate based parameters obtained
from field experiments can be used to develop a DSS, which can assist in making
decision on “when to irrigate?” and “how much to irrigate?” for crops grown under
specific soil and fertility conditions to achieve maximum yield.
2. DSSs are also developed as independent executable computer programmes using a
variety of data and writing the protocol in any programming language using
“if…Then…Else” programming construct to generate different scenarios. The DSS does
not contain a process based model or geospatial tools for data input, analysis and output
generation.
3.
The DSS uses a component object module compliant programming language to develop
an in-bound and out bound interface with different process based models and links with a
decision generator to generate different scenarios which are based on the predictions by
2
the linked models. The main DSS data frame would link with a variety of modules or
sub-routines, which generally addresses a specific domain of problem. For example, a
DSS can be developed by linking already established SWAT (soil water assessment tool),
and a robust crop model to generate decision on best natural resource management
practices and the crop yield on watershed and field scales.
4. The DSSs are developed as a customized tool within the GIS and remote sensing
software using the built in macro programming languages and the libraries available in
the software. Operation of such types of DSS require the geospatial tools to be installed
in the system and all input and output data and models are linked through the interface
within GIS and RS software.
In this bulletin, various DSSs are reviewed in terms of their development methods, input data
requirement, scenario generation capability, targeted user and availability and accessibility of
source code for further refinement.
The reviews are clubbed under three major categories viz. a) DSSs for management of
Irrigation water at farm level for enhancing agricultural productivity and related studies; b) DSSs
for management of natural resources in watersheds and c) DSSs for integrated water resources
management in river basins.
3
Chapter 2
DSS for farm level Irrigation water management to enhance agricultural
productivity
2.1 Decision Support System for Management of Upland Farming with Special
Consideration of Soil Conservation
Pertiwi et al. (1998) developed a decision support system (DSS) for the management of
upland farming with special consideration of soil conservation in Otoyo town, Kochi. It is
intended for use by farm advisors and others when consulting with a farmer on adoption of
conservation cropping system. After having user input data of certain field and farm practices, the
DSS examines rules and databases and then estimates the potential annual soil loss due to water
erosion. When predicted soil loss is beyond the acceptable limit, the DSS will find some better
alternatives of farm practice for conserving soil with its financial consequences. The DSS will
also suggest the most suitable soil tillage machinery for each derived alternative.
At a time when agricultural efforts are focused on increasing food production, soil
degradation worldwide is increasing. Soil erosion is one of the most serious environmental
Fig. 1. Flow chart of the DSS
4
problems in the world today, because it seriously threatens agriculture and the natural
environment (Pimentel, 1993). It is noted that 84% of the soil degradation in the world is due to
soil erosion (56% for water erosion and 28% for wind erosion), leaving chemical deterioration
(12%) and 4% of physical deterioration (UNEP, 1992 in Takase, 1995). Soil erosion in
agricultural land causes the deterioration in the quality of land that brings about decreased
productivity and increased expenditure on fertilizers to maintain fertility. Besides, the effects of
soil erosion are felt also in the areas down valley or downwind where the ground is covered with
sand and silt deposits, ditches and canals are clogged with sediment and reservoirs silt up. The
siltation of reservoirs and rivers reduces their capacity and creates flood hazard. The chemicals
contained in the sediment wash out from upland agricultural lands are potential pollutants that
deteriorate water quality. Many factors control the working of the soil erosion system. A
considerable number of researches on soil conservation against water erosion have been carried
out. However the researches usually investigate only a partial factor from which a practical
implication for soil conservation is difficult to be derived. The integration of such agricultural
research results into useful software driven systems has potential to aid managers of agricultural
resources. Keeping all these adverse affects in mind a Decision Support System is designed with
which farm management plan, especially in upland farming can be suggested with special
consideration on soil conservation.
DSS is loaded with databases suitable for Japan condition. The Decision Support System
for management of upland farming is developed in the form of computer package program. The
DSS is intended for use by farm advisors and others when consulting with a farmer on adoption
of conservation cropping system. After having the necessary input data from the user, the DSS
will examine rules and databases and then estimates the potential annual soil loss, in terms of soil
water erosion. When predicted soil loss is beyond the tolerable soil loss the DSS will find some
better alternatives of farm practice for conserving soil with its financial consequences. The
program is also able to help the user finding the most suitable tillage machinery. Fig. 1 shows the
flowchart of the computer program.
Programming language used:
The computer program was developed by using Visual Basic programming language
under Windows 95 environment for use on PC with an 80386 processor or higher, a minimum of
16 MB RAM, and VGA or higher-resolution screen supported by Microsoft Windows.
Data used:
The databases were organized by using Microsoft Access. It includes area database,
climate database and crops database, specifically for Japan area. Sylvanmaps, an OCX control,
was also installed to the computer to enable data access through maps visualization as in the
Geographical Information System.
The computer program requires input data such as farm location, percent silt plus very
fine sand and percent sand of the soil, percent organic matter, soil structure, soil permeability,
5
field shape in terms of slope length and slope height, and cropping system as well as conservation
currently practiced. Fig. 2 and 3 shows the examples of input data dialog box. It is assumed that
farmer’s field is an area of uniform soil properties, cropping and support practices.
Fig. 2. Input data dialog frame of the DSS
Fig. 3. data input dialog frame of the DSS
6
Output:
The Decision Support System (DSS) has been developed to assist farm managers making
plan of their conservative cropping system on upland farming. It can be used first, to predict
annual water erosion risk, for both the existing situation as well as for the projected effects of
proposed land use practices, and next to find the more conservative cropping practice with its
financial consequences. Furthermore, the DSS suggested alternatives are as shown in Fig. 4. The
progress that has been made on the DSS development as described above, to make the DSS
available for public use; needs continued effort for improvement. For example, soil loss tolerance
needs to be determined specifically for each region, crop data base needs to be enhanced with
various crops. Besides, economic analysis needs to be carried out more intensively, including
analysis of externality.
Fig. 4. Captured window showing the suggested alternative cropping practices
Application of DSS:
•
It can be used to assist farm managers for making their conservative cropping system on
upland farming plan.
•
It can be used to predict annual water erosion risk, for both the existing situation as well
as for the projected effects of proposed land use practices, and
•
To find the more conservative cropping practice with its financial consequences.
7
2.2. A decision support system to improve planning and management in large irrigation
schemes
Silva et al. (2001) designed a decision support system (DSS) that was developed to
improve planning and management for the large irrigation schemes in the Alentejo region of
Portugal. The system was designed to help in the analysis and evaluation of the crops and
cropping systems that can potentially be cultivated, together with identification of limitations
affecting crop selection and crop yields. It integrates socio-economic and biophysical data at the
field level to analyze
the performance of an
irrigation
scheme
in
terms of the adoption of
irrigation by farmers
and farmers’ incomes.
The
final
output
is
given in the form of
specific
actions
and
policies for the irrigated
areas. The DSS was
designed initially to be
used in the Alqueva
project,
a
large
irrigation scheme that is
under construction in
Alentejo.
The DSS can
be divided into three
main
stages:
Fig. 5 System architecture of developed DSS
Input,
Analysis and Output as shown in Fig. 5 the first stage (Input) consists of the specification of data
required by the DSS to undertake the two following stages. The first two steps of the Input stage
are the characterization of the crop systems and potentially irrigable fields. This includes
formulation of the requirements of crops, crop systems and irrigation methods. In the case of
fields, the information refers to farmers, zones, soils and specific field data. This information is
then used to identify homogeneous fields, which are land units considered to have similar soil,
zone, farmer and field-specific characteristics. Together, these characteristics constitute the
resources inventory that determines the field qualities to be matched with the crops, crop systems
and irrigation method requirements.
The following stage (analysis) is performed by the DSS. It can be divided into three
sequential activities: Matching, Crop budgets and Crop allocation.
8
Database description
Data used by the DSS are stored in a relational database as showed in Fig. 6. For each
crop system, a typical level of inputs, including production factors (e.g. seed, fertilizers), labour
(specialized and non-specialised) and machines (e.g. tractors, reaping-machines, ploughs), is
specified. The crop systems are also defined by the corresponding crop requirements, specified in
the Crops table, by an irrigation method, specified in the Irrigation methods table, and by their
particular requirements, specified in the Crop systems table.
Fig. 6. Database structure with linkages among different data tables
The characterization of a potentially irrigable field includes, besides specific data (Fields
table), information on the farm and farmer attributes (Farmers table), the zone within the
irrigation scheme (Zones table), and the soils characteristics (Soils table). These tables are used to
define the qualities of the land units under analysis (the potentially irrigable fields). This
information is then matched with the requirements of the crops, cropping systems, and irrigation
methods, in order to select the crop systems which can be grown in each field and to estimate
crop yields.
The Crops, Crop systems and Irrigation methods tables are essentially used to
characterise the crops systems and define their requirements. The four Cropping systems
operations tables (Crop systems operations, production factors, mechanical operations and labour)
are used to store information on the sequence of activities that characterize the cropping systems,
thus providing a basis for calculating the crop budgets. The last group of tables (Products,
9
Production factors, Tractors, Other machines and labour) is used to define the costs or prices of
each of the inputs and outputs considered in the Crop systems and the Crop systems operations
tables.
Database interface
The DSS menu has six different header items (Fig. 7). The first three (Database, Fields
and Crops), are used to access and update the database. The Database header has two options
(Open and Exit).The first of these options is used to select a database file and open it. Hence, it is
possible to work with different databases (e.g. different irrigation projects or price scenarios), and
to store them with appropriate names or in selected directories. The second option (Exit) is used
to close the program.
Fig. 7. Captured input data window of DSSIPM
The next header (Fields) provides access to all the tables used to store information about
the potentially irrigable fields (Soils, Farmers, Zones and Fields). The third header (Crops) can be
used to access all the tables that describe the information related to crop systems (Products,
Labour, Production Factors, Tractors, other Machines, Mechanical operations, Irrigation methods,
Crops, Crop systems and Crop systems operations). The last option in this header (Crops,
10
CropSys, CropSysItin) provides access to the Crops table, the Cropping systems table and the
four Cropping systems operations tables.
The Crops, Cropping systems and Cropping systems operations tables are combined so
that the relationships between them can be displayed by a tree view representation (as in Fig. 7).
In this way, it is much easier to navigate between crops, crops systems, and the operations
defined in the Crop systems operations table. Any crop, crop system, or operation can be selected
by double-clicking the corresponding branch on the tree. If an operation is double-clicked, the
Crop systems operations window is displayed. This window provides simultaneous access to all
the Crop systems operations tables (Crop systems operations, production factors, mechanical
operations and labour). The user is not restricted to adding, deleting, or modifying an operation,
but can also add, delete, or modify the tractors, other machines, production factors and labour
used for that operation.
Input data
•
Farmers, zones, soils and potentially irrigable fields
•
Crops, cropping systems and irrigation methods
•
Cropping systems operations
•
Crop products, production factors, tractors, other machines and labour
Output
The first option under the results header (Fig. 8) was used to run the analysis and prepare
the output. This is necessary because the input data is changed whenever the database is modified
Fig. 8. Captured window of DSSIPM displaying results
(Fields and Crops headers) or a new scenario is generated. The development of both the
integrated assessments and the DSS proposed in this research work should be seen as a
continuous process. With appropriate modifications, the method can be easily adapted for use in
11
other regions (e.g. other irrigated areas in the Mediterranean basin) or to incorporate different
objectives (e.g. environment-related issues, such as decreasing soil erosion). Nevertheless, in the
end it still has to be able to integrate biophysical and socio-economic information and indicate
priorities, in terms of specific actions and policies, for the potentially irrigable areas. If the aim is
to improve planning and management in large irrigation schemes, future developments should
enhance the relevancy and applicability of the method rather than just result in increased
complexity.
Application:
The DSS presented here is to be used by regional authorities in the planning and
management of large irrigation schemes. These can be governmental institutions, associations of
farmers or research institutes.
However, such interfaces should be provided to ensure the viability and sustainability of
the software. If the input data can be modified to include changes in the external environmental
conditions (e.g. include a new crop or crop system or change the prices used in the analyses), the
DSS has the potential to evaluate different policy, structural or management scenarios and will
remain effective over a reasonable time period.
The DSS should be viewed as an exploratory tool to help planning and management
rather than a predictive tool. The results obtained are possible scenarios and not actual future
situations. The important outcome is not to predict the future, but to develop a platform to analyze
the possible options available to farmers. It must also be emphasized that the results obtained
depend greatly on user-defined relationships between variables. If these relationships are not
reliable or realistic, the output may only be of limited value. In fact, the identification of variables
and definition of rules to use within the matching process are the major limitations of the DSS.
Future developments of the system will continue to incorporate local knowledge (from farmers,
extension workers and other decision-makers). The confidence in the use and output from this
DSS will improve as it is used in a wider range of irrigation schemes.
2.3 Planning length of long-term field experiments through Decision Support Systems – A
Case Study
Kaur (2008) developed a decision support system (DSS) for planning appropriate length
of long term conjunctive water use experiments on a salt affected farmer’s field. An indigenously
developed decision support system, named IMPASSE (Impact Assessment and management of
Saline/ Sodic Environments), was developed for planning length of long term conjunctive water
use experiments on a rice-wheat growing salt affected farmer’s field in Haryana (India) in 2008.
Long-term field experiments are the conventional means for developing; evaluating and
demonstrating site-specific land/water use plans. The present study attempts to demonstrate
application of one indigenously developed decision support system (DSS) for planning
appropriate length of long term conjunctive water use experiments on a salt affected farmer’s
field. Before application, the proposed DSS was extensively validated on several farmers and
12
controlled experimental fields in Haryana (India). Validation of DSS showed its potential to give
realistic estimates of root zone soil salinity; sodicity and salt stress induced relative crop yield
reductions under local resource management conditions.
IMPASSE is a user-friendly field scale-DSS designed for managing saline/ sodic soils
and waters in freely draining irrigated and rainfed agricultural lands. It comprises a set of well
established subroutines for assessing short / long term impacts of a range of (geo) hydrologic
conditions, water management options and crop rotation schedules on root zone-soil salinity /
sodicity build ups and crop yield reductions. By selecting appropriate time criteria it can even
generate crop-specific irrigation schedules. IMPASSE was designed keeping in mind the relative
simplicity of its operation to promote its use by field technicians and project planners. It contains
a set of default soil/ crop characteristics, which can be selected and adjusted for various soil/ crop
types. It basically requires two types of input-parameters.
Type-I
1. Daily weather data, crop data (viz. crop type and salinity/ sodicity response factors),
2. Soil data (viz. soil type, moisture contents at saturation, field capacity and wilting point,
saturated hydraulic conductivity, initial soil moisture content and initial EC, Na+, Ca+2,
Mg+2 concentrations of soil root zone) and
3. Water data (viz. irrigation depth, application dates and EC and Na+, Ca+2, Mg+2
concentrations).
Type-II
Leaching fractions are determined through calibration procedure.
DSS calibration and validation
The proposed DSS was
validated on several farmers as
well as controlled experimental
fields in Gurgaon and Karnal
districts
of
Haryana,
India.
Before validation, firstly the
calibrated values of leaching
fractions
under
both
actual
farmers’ fields and controlled
experimental
field
conditions
were obtained. For this, soil type
specific-default leaching fraction
values in the DSS were increased
or decreased in steps till good
correlation
between
the
coefficients
(R)
observed
and
Fig. 9. Location of test farms in Sohna block of Gurgaon
simulated EC and ESP values were obtained. Further, the calibrated leaching fraction values,
13
along with the other type-I parameters, were then used for its validation. As per the
recommendations of ASCE [2], both visual (graphical) and statistical comparisons in terms of
correlation coefficient, mean relative error and root mean square prediction difference were used
for this purpose.
Validation results on farmers’ fields For validation on actual farmer’s fields, a detailed inventory
on weather, farming practices, soils and waters of 11-farmer’s fields was prepared. Fig. 9
illustrates the location of these fields in 6-villages of Gurgaon district of Haryana, India. Test area
weather data was acquired from Indian Meteorological Department (IMD) while farming practice
information on crops cultivated, their sowing/ harvest dates, actual/ potential yields and water
management practices was obtained through personal interviews of farmers.
Validation results on controlled experimental fields
The experiment under fixed Wheat-fallow rotation (Nov., 1986 to Aprl. 1989) comprised
of 5-replications of 5-irrigation treatments viz. EC = 0.7 dS/m (Canal water, CW), 6 dS/m, 9
dS/m, 12 dS/m and 18 – 27 dS/m (Drainage water, DW). The first year (i.e. 1986-1987)
experimental data was used for the calibration of the proposed DSS while the remaining 2-years
data (for 1987-89 periods) was used for its validation. For this study, actual soil salinization (EC)
and economic yields under only first four irrigation treatments were considered. The economic
yields obtained under 6, 9 and 12 dS/m - saline water irrigation treatments were divided with
those under canal water irrigation (i.e. no salt stress) treatment to obtain actual relative crop yield
reductions under varying salt-stress levels.
DSS application
The so validated-DSS was then used for planning length of varied conjunctive water use
experiments on a salt affected farmer’s field in Khatrika village of district Gurgaon, Haryana
(India). For this, long-term (10 years) impacts of varied (existing/ alternative) conjunctive water
use strategies on the test field’s root zone soil salinization/ sodification and relative crop yield
reductions were simulated with the proposed DSS. Validation of DSS showed its potential to give
realistic estimates of root zone soil salinity; sodicity and salt stress induced relative crop yield
reductions under local resource management conditions.
Outputs:
Long term impact assessment of varied conjunctive water use strategies, on the test
farmer’s field, with the so validated DSS showed that the time required for achieving stable crop
yields could be treated as a good measure of the minimum length of such experiments. For the
test (rice-wheat growing) farmer’s field, this time ranged between 5.5 – 6.0 years. It was observed
that at shorter time scale (i.e. 2 years), though application of 50% canal waters (CW) blended
with 50% tube well waters (TW), was as productive and superior as cyclic applications of (2CW,
1TW, 1CW); (1CW, 1TW, 2CW) and (CW: TW) during wheat cropping season yet it could not
14
maintain its superior performance at DSS proposed time duration of 6 years or more. Similarly
irrigation practice of (4CW, 5TW) during rice cropping season, though beneficial at shorter time
scale, was much inferior to the cyclic application of canal and tube well waters (i.e. CW: TW) at
longer time scales (i.e. at ≥ 6 years). Hence, limiting the proposed long-term conjunctive water
use experiment to the DSS proposed minimum time duration of 6 years lead to the selection of
the most stable and sustainable irrigation practice(s) for the test farm.
Application:
IMPASSE DSS helps in planning appropriate lengths of various long-term field
experiments. It could clearly demonstrate that different agricultural systems, as generated by
different sets of conjunctive water use treatments, are characterized with different time periods
for achieving stable/sustained crop yields.
Limiting an infinitely long conventional field experiment to this time duration not only
leads to the selection of most appropriate and sustainable agricultural practice(s) but also
increases its cost, time, energy and information-efficiency and hence chances to be planned for
many other diverse locations within the same limited budget.
15
Chapter 3
DSS for Integrated Watershed Management
3.1. Water demand and supply analysis using a Spatial Decision Support System (SDSS)
Manoli et al. (2001) presented a prototype Spatial Decision Support System (SDSS) for
the evaluation of water demand and supply management schemes in hydrological basins. The
hydrological basin is topologically mapped to a network of spatial objects representing the
physical entities and their connections. The SDSS integrates suitable models for demand site
requirements calculation and water allocation on the basis of alternative scenarios.
A Spatial Decision Support System for the evaluation of water demand and supply
management of an island of Syros in Greece was undertaken. Syros is located in the centre of the
Cyclades complex, is the administrative centre of the prefecture and covers an area of 84 km2.
The water basin was topologically mapped to a network of spatial objects representing the
physical entities and their connections. Several GIS functions, which include data input/update,
network derivation from the basin map and network building/modification were incorporated.
The tool integrates suitable models for demand site requirements calculation and water allocation.
Alternative
scenarios
was
constructed, trends
and interactions of
the complex water
system
also
analyzed, strategies
to
solve
water
allocation conflicts
could evaluated and
necessary
infrastructure
interventions can be
planned in advance
in order to meet
water needs.
Fig. 10. System architecture of the Spatial Decision Support System
16
System Architecture of the DSS
The structure of the SDSS is presented in Fig. 10. The central objective in the design of
the system is to integrate data, models and decision analysis processes into a unified software
package.
Users interact with the system via a GIS map based user-interface, which provides the
functionality of inputing information and viewing of results through appropriate maps, diagrams
and tables. A network representation of the hydrological basin is derived from the core database.
Characteristic scenarios can be developed with the use of a network editing tool and future
assumptions that affect demand, supply and hydrology can be specified. Scenarios are evaluated
with the aid of a demand calculation procedure and a water allocation model. Scenarios can then
be planned, simulated and evaluated and the decision-maker can undertake rational actions with
respect to the objectives.
The GIS database is the heart of the spatial and operational information system as well as
the storage system that allows communication and intermediate storage between models and
Fig.11. Central database object model and attribute data
subsequent reporting modules. The object model of the database is presented in Fig.11. The
database has been developed around a geographical hierarchy, which is dictated by the very same
nature of available information. The hierarchy is implemented through a collection of maps
(cartographic representation) and a collection of tables with attribute data and time series (tabular
17
representation), connected through the data-binding protocols supported by the MapX
technology.
For each area identified as demand or supply regions, irrigated areas, industrial plants,
surface and groundwater resources, storage and distribution networks are retrieved from the
database. Each entity is fed with appropriate attribute data, which refer to permanent and seasonal
population, agricultural water requirement, water resource availability, their monthly variation
and their associated economic cost and money flows.
Operational aspects of the SDSS
The developed SDSS consists of three basic modules allowing for a complete
representation of demand/supply scenarios. These are:
 Water demand analysis and supply requirements estimation module;
 Network editing module;
 Water allocation and water shortage estimation module.
Language used: The system was implemented within the computational environment of
Microsoft Visual Basic. The GIS functionality is embedded with objects of the MapInfo MapX
ActiveX component.
Data used:
 Irrigation water needs and consumption rates for the most important crops,
 Monthly variation of water demand for irrigation and domestic use
 Annual water demand.
The disaggregation of water use sectors derived from the database is presented in Fig. 12
Activity level data, month variation, water use rates and projection functions can be modified for
each scenario introduced.
A prototype spatial decision support system for the evaluation of water demand and
supply management schemes has been outlined. The system integrates a spatial database of the
study area and its infrastructure, tools to for network editing and specifying assumptions that
affect demand, supply and hydrology, model to perform demand analysis and water allocation
and components to manage and present the information. The tool was tested for a characteristic
case study that demonstrated its effectiveness in analyzing and supporting decision making.
18
Fig. 12. Different demand parameters and estimation modules
3.2 Geographic Resources Decision Support System (GRDSS) for land use, land Cover
dynamics analysis
Ramchandra and Kumar (2004) developed a Decision Support System named GRDSS
(Geographic Resources Decision Support System) considering temporal multispectral data (1998
and 2002) of the IRS 1C / 1D (Indian Remote Sensing Satellites). Objective of this DSS is to
carry out the land use/land cover and temporal change analysis for Kolar district, Karnataka State,
India.
GIS facilitates systematic introduction of numerous different disciplinary spatial and
statistical data, which can be used in inventorying the environment, observation of change and
constituent processes and prediction based on current practices and management plans. Remote
Sensing helps in acquiring multi spectral spatial and temporal data through space borne remote
sensors. Image processing technique helps in analyzing the dynamic changes associated with the
earth resources such as land and water using remote sensing data. Thus, spatial and temporal
analysis technologies are very useful in generating scientifically based statistical spatial data for
understanding the land ecosystem dynamics. Successful utilization of remotely sensed data for
land cover and land use change detection requires careful selection of appropriate data set.
Therefore, the land use/land cover analysis and change detection techniques using GRDSS
(Geographic Resources Decision Support System) for Kolar district considering temporal
multispectral data (1998 and 2002) of the IRS 1C / 1D was developed.
GRDSS is a freeware GIS Graphic user interface (GUI) based on command line
arguments of GRASS (Geographic Resources Analysis Support System). It has the capabilities to
capture, store, process, display, organize, and prioritize spatial and temporal data. GRDSS serves
19
as a decision support system for decision making and resource planning. It has functionality for
raster analysis, vector analysis, site analysis, and image processing, modeling and graphics
visualization. This help in adopting holistic approaches to regional planning which ensures
sustainable development of the region.
In this regard Open Source GIS such as GRASS (Geographic Resources Analysis
Support System) helps in land cover and land use analysis in a cost-effective way. Most of the
commands in GRASS are command line arguments and requires a user friendly and cost effective
graphical user interface (GUI). GRDSS (Geographic Resources Decision Support System) has
been developed in this regard to help the users. It has functionality such as raster, topological
vector, image processing, graphics production, etc.. It operates through a GUI under LINUX.
GRDSS include options such as Import / Export (of different data formats), extraction of
individual bands from the IRS (Indian Remote Sensing Satellites) data (in Band Interleaved by
Lines format), display, digital image processing, map editing, raster analysis, vector analysis,
point analysis, spatial query, etc. These are required for regional resource mapping, inventorying
and analysis such as Watershed Analysis, Landscape Analysis, etc.
Data used:
• Creation of base layers like district boundary, district with taluk and village boundaries, road
network, drainage network, contours, mapping of water bodies, etc. from the SOI toposheets
of scale 1:250000 and 1:50000.
• Extraction of bands (LISS3 with resolution 23.5 m and PAN with resolution 5.8 m of 1998
and 2002) from the data (in BIL and BSQ format) respectively procured from NRSA.
• Identification of ground control points (GCP’s) and geo-correction of bands through
resampling.
• Cropping and mosaicing of data corresponding to the study area.
• Fusion of LISS3 and PAN data using RGB (Red, Green, Blue) to HIS (Hue, Intensity,
Saturation) and HIS to RGB conversion technique.
• Histogram generation, Bi-spectral plots, Regression analysis.
• Computation and analysis of various vegetation indices.
• Generation of FCC (False Colour Composite) and identification of training sites on FCC.
• Collection of attribute information from field corresponding to the chosen training sites
using GPS.
• Classification of remote sensing data (1998 and 2002): Land cover and land use analyses
(both district wise and taluk wise).
• Change detection analysis using different techniques (Image differencing, Image ratioing,
etc.).
• Detection, visualisation and assessment of change analysis.
• Statistical analysis and report generation.
20
Output:
Holistic decisions and scientific approaches are required for sustainable development of
the region. Change detection techniques using temporal remote sensing data provide detailed
information for detecting and assessing land cover and land use dynamics. Different change
detection techniques were applied to monitor the changes. Vegetation index differencing
technique was used to analyze the amount of change in vegetation (green) versus non-vegetation
(non-green) with the two temporal data. Different change detection techniques such as image
differencing, image rationing, vegetation index differencing and Image regression were attempted
to assess the amount of change in the study area.. The change analysis based on two dates,
spanning over a period of four years using supervised classification, showed an increasing trend
(2.5%) in unproductive waste land and decline in spatial extent of vegetated areas (5.33%).
Depletion of water bodies and large extent of barren land in the district is mainly due to lack of
integrated watershed approaches and mismanagement of natural resources.
3.3 A Decision Support System for Soil and Water Conservation measures on Agricultural
Watersheds
Sarangi et al. (2004) developed a Soil and Water Conservation Decision Support
System (DSS) considered both structural and cropping practices for arresting sediment loss and
validated for a watershed on the Caribbean island of St Lucia and used to suggest measures for a
10º slope under specific soil type, sediment loss and LCC conditions. The measures proposed
included bench terraces, graded contour bunds, conservation ditches, concrete chute spillways,
diversion dams and conservation cropping systems. The measures actually adopted on-site were
conservation ditches, graded contour bunds and conservation cropping systems.
A decision support system (DSS) is useful in generating alternative decision scenarios
for management of natural resources, facilitating the implementation of Integrated Watershed
management (IWM) concepts in an interactive and holistic way. The decision to implement an
appropriate land use coupled with suitable soil and water conservation techniques not only
enhances watershed health but also prevents sediment losses keeping all this in view a Decision
Support System was developed in 2004 to suggest the possible structural and vegetative
measures, along with land tillage and management options to be implemented to prevent the
sediment losses by providing suggestive control measures to reduce the present level of sediment
loss, as measured at the outlet of the watershed on the Caribbean island of St Lucia.
The DSS is intended for use by watershed managers, government policy planners and
those advising farmers on the selection of watershed-wide sediment-control measures. It is a
computer-based information system designed to support decision makers interactively in making
decisions about relatively unstructured problems. The DSS does not contain any mathematical
model, but consists of a decision rule relating the secondary or historical data and interpretive
routines using the published research articles on watershed management. The DSS generates
structural and biological soil and water conservation measures to be adopted on watersheds. It is
developed in the form of a computer program using the interactive controls and algorithms of
21
Visual Basic 6.0 programming language. The nested If . . . Then . . . Else construct is extensively
used as an interpretive algorithm for the generation of alternative decisions using the input
information. The DSS runs on a platform of Windows 95TM, or above, is user-friendly and is
best viewed at a screen resolution of 1024 by 768 pixels. The Graphic User Interface (GUI) of the
DSS is a combination of pop-up windows, pull-down menus and button controls and is mouse
driven. The model uses the set of conservation measures adopted for a given slope and soil type
as discussed under the vegetative and structural control measures section. After collecting the
necessary input information from the user, the DSS will suggest possible structural and vegetative
measures, along with land tillage and management options to be implemented to prevent the
sediment losses reported by the user. The DSS does not predict any sediment loss, but provides
suggestive control measures to reduce the present level of sediment loss, as measured at the outlet
of the watershed. The flow chart of the DSS showing different forms and major flow controls of
the main decision frame is shown in Fig. 13.
Fig. 13. DSS displaying the major flow-control architecture
22
The DSS consists of four Visual Basic (VB) forms (GUI windows) named as ‘welcome frame’,
‘definition of terms used frame’, the ‘main DSS frame’ and ‘picture of conservation measures
frame’. All the VB forms are linked in a VB 6.0 environment. This programme is compiled and
presented as an executable file named WSDSS.EXE (Watershed Decision Support System). The
screen dumps of the ‘main DSS frame’ and ‘picture of conservation structures frame’ are shown
in Figs 14 and 15, respectively.
Fig. 14. Main DSS frame with a sample data and displayed decisions.
Fig. 15. DSS window frame displaying the picture of conservation measures.
23
DSS was developed with graphic user interface (GUI) capability. This provides the user
with an interactive environment to enter input information of slope, soil type, LCC and sediment
loss, and then to obtain structural and vegetative control measures in response to those input
values. These suggested measures displayed in decision boxes vary according to the input values
as per the decision-flow logic used in DSS development. The user selects the soil and water
conservation measures to be implemented in the watershed. Moreover, the availability of pictures
of different conservation measures in a separate user form, but linked with the main DSS window
transmits a sense of visual familiarity to the user on different conservation measures adopted at
diversified watershed locations. Validation of the DSS for the St Lucia watershed revealed that
the measures suggested for different slope, soil type and LCC combinations were in close accord
with the measures actually implemented in the watershed. These implemented conservation
measures reduced soil loss for the study watersheds.
Application:
The DSS is intended for use by watershed managers, government policy planners and those
advising farmers on the selection of watershed-wide sediment-control measures.
3.4 A Decision Support System on Biodrainage for Land Reclamation
Dash et al. (2008) developed a Decision Support System for biodrainage system design
using VB protocols in 2007. These data were used in the DSS for developing alternative decision
scenarios for planting density and reclamation periods corresponding to the input information of
the waterlogged area, rainfall and plant type. The model uses a data structure pertaining to the
seepage losses from canal, percolation loss from irrigation field and recharge due to annual
rainfall to estimate probable net recharge under different soil and hydrological conditions. After
collecting the necessary input information from the user, the DSS will suggest probable time
needed to lower the water table and the quantity of salt removed by plantation towards land
reclamation.
Unscientific use of water along with other natural and man-made causes leads to water
logging, soil salinity and consequently, sub-optimal agricultural production. To tackle this
problem, there exists a number of technologies e.g. surface drainage, sub surface drainage and
conjunctive use of surface and groundwater. But lack of monetary allocation for their installation
and maintenance, the issue of safe disposal of the drainage effluent and control of pollution of
natural water bodies limit their large-scale adoption in India. In this context, biodrainage, i.e.,
removal of excess water from the soil profile through evapotranspiration by certain tree species is
considered. Considering the interplay of a large number of parameters in designing a biodrainage
system, there should be simple and user-friendly approach to decision making in this regard.
Decision support system (DSS) allows arriving at decisions regarding alternative plantation type
and planting density for different water logging situations. It integrates all the data and
information into a more meaningful and effective toolbox for use by the designer of a biodrainage
system.
24
The DSS is intended for use by watershed managers, government policy planners and
those advising farmers on the selection of plantation species for water table and salinity control.
The developed DSS does not contain any mathematical model per se, but consists of decision
rules relating to the secondary or historical data and interpretive routines using published
information and primary data acquired from the pot experiments. It is developed as a computer
program using VB programming language. The nested If...Then…Else construct is extensively
used as an interpretive algorithm for the generation of alternative decisions using the input
information. The DSS runs on a platform with Windows 95 or higher operating systems and is
best viewed at a screen resolution of 1024 by 768 pixels. Its User Interface is a combination of
pop-up windows, pull-down menus and button controls, and is mouse driven. The flow chart of
the DSS showing different forms and major flow controls of the main decision frame is shown in
Fig. 16.
Biodrainage plantation details
Information of the area to be reclaimed
•
•
•
•
•
Area available for plantation
Average annual rainfall
Water table depth (both initial
and final)
Type of soil
•
•
Average evapotranspiration rate
Type of plant
If [Soil type, Water table depth, Area,
Plant type, Salinity etc.]
THEN
Number of plants,
Reclamation period for
removal of water and salt
ELSE
Other
options
Plantation geometry for a given area
Reclamation period to remove water
Salt uptake by the plants (used in pot
experiment study)
Visual Basic button
control and interface
Fig. 16. Flow chart of the DSS showing different forms and major flow controls
25
The DSS consisted of three VB forms namely, ‘main frame’, ‘definitions related with agricultural
drainage and biodrainage frame’ and ‘plantation design frame’. All the VB forms are linked with
each other in a common VB environment. After linking these forms, the programme was
compiled and presented as an execution file named as BDSS.EXE (Bio drainage Decision
Support System). The execution file can be operated without the VB compiler in any computing
system with minimal hardware configuration.
Operation of the DSS
The main DSS frame of the bio drainage, designated as Bio Drainage Support System (BDSS)
consists of both the input and output boxes, allowing the user to input the information needed and
the time required for land reclamation are displayed as output of the DSS in the decision boxes
(Fig. 17).
The BDSS is easy to operate with the highlighted command buttons without any specific
instructions. The user can perform repetitive readings by clicking on the command button
indicated as ‘back’ and can try other sets of input data to generate subsequent outputs. Similarly,
the user can move to the definition frame to have a basic understanding of the terms used in the
BDSS. Therefore the interoperability within the DSS facilitates the user to interact dynamically
with the input and output information.
A develop DSS on bio drainage
incorporating the available secondary
data on water transpiration rate of some
tree species and primary data from pot
culture experiment on salt uptake of a
specific plant. A conceptual decision
flow logic using the water budgeting
concept was formulated to link all the
information
through
the
VB
programming language. The developed
BDSS provided the user with an
interactive environment to enter the
required input information and observe
the possible output scenarios on land
reclamation.
The
DSS
is
flexible
Fig. 17. Captured window with decision frames
enough for subsequent modifications in
terms of inclusion of additional data because of easy amenability of VB programming language.
The incorporation of more data from diversified bio-drainage plant species will expand the
decision-making ability of the DSS for application in integrated land reclamation and integrated
watershed development programmes. The developed BDSS can also be used as a teaching
material to demonstrate the effect of bio-drainage plants on land reclamation.
26
Chapter 4
DSS for Irrigation and Flood Water Management at Basin Level
4.1. Decision Support System for the Hula Project
The Hula Decision Support System (HDSS) is designed to aid Hula site operators in
managing ground water levels in the Hula region in Israel by Nir Naveh and Uri Shamir in 2001.
The HDSS was developed at Israel Institute of Technology. Groundwater levels are managed by
controlling water levels by adjustable dams in a grid of drainage canals and by the timing and
intensity of irrigation. Water levels in the canals are controlled by a set of hydraulic structures.
Groundwater levels are to be maintained within a specified range, to minimize the decomposition
and subsidence of the peat soils, ensure year round green cover of the area, and by avoiding
saturation condition of the crop root zone, allow farmers to continue cultivation of their fields.
The management module for the HDSS performs optimization with the minimum deviation from
the specified groundwater “target-level”, and minimum supply of water from the Jordan River to
the Hula’s drainage canals (water quantity is limited). The DSS determines the dam settings and
irrigation quantity and timing over a period of eight weeks, and is solved again whenever
conditions change.
Different components of the HDSS are shown in Fig. 18. Geographic partition of the
area, from a visual
analysis
of
groundwater maps
that are performed
by a GIS of the
Hula project, one
can identify 4 or 5
sub-areas
in
the
project according to
groundwater
level
behavior. In each
sub-area
the
groundwater
is
uniform,
level
relatively
while
there is a relatively
large
difference
between the sub-
Fig. 18 Schematic diagram of the HDSS
areas. Based on this
27
information, it seems best to divide the entire project area into sub-areas, allowing each sub area
to have its own management module with its geographic/ geometric data and its specific ground
parameters. The modules for the sub-areas will run in parallel. The boundaries of the sub-areas
are clear landmarks (the old channel of the Jordan River, Lake Agmon, topographic differences)
that separate the sub-areas in terms of their hydrological behaviour.
Data Used:
The hydraulic conductivity (k) and porosity (n) of the soil are important parameters in
development of the HDSS.
The optimizer seeks a solution where groundwater reaches its target level as fast as
possible and then keeps the level close to this level until the end of the planning period. This
solution always leads to minimum release of water downstream from the project area, and,
therefore, to minimum water consumption in the project area. The optimization model developed
in this work provides reasonable results that are compatible with accumulated experience of the
Hula project operators. The accuracy of the model matches the operational precision of the dams
in the canal network and of the irrigation system. In plots where this is not the case, it is possible
to run a more precise simulation with Groundwater Modeling System (GMS) to get more detailed
results.
4.2. GIS-based decision support system for real time water demand estimation in canal
irrigation systems
Rao et al. developed a Geographic Information Systems (GIS) based Decision Support
System (DSS) for real time water demand estimation in distributaries in year 2004. It dynamically
links a field irrigation demand prediction model for the area irrigated by a distributary with a GIS
of the canal network. The system allows interactive selection of distributaries and on-line real
time estimation of water demands in each distributary over the entire network. For real time
estimates, the model is used with current season information on weather, weather forecasts and
distributary level information on crops and soils.
In canal irrigation systems in India, water supplies reach the fields through a network of
main canals, branch canals (secondary canals) and distributaries (tertiary canals). The distributary
is the basic unit of irrigation management in large canal systems, as it is the last point of control
in main irrigation systems operation. This study presents a scheme for the development of a
Geographic Information Systems (GIS) - based decision support system (DSS) for real time water
demand estimation in distributaries of Sone irrigation project in Bihar, India for rice crop (which
is commonly grown crop of locality). The Sone project is a river diversion scheme built on the
river Sone. The river is a tributary of the Ganga.
Since the distributary is the unit of operation, the DSS integrates well with the actual
process of decision-making by the operators of canal irrigation systems in India. The availability
of such a quantitative decision-support tool for irrigation systems operation can have a powerful
impact on the overall water management strategy to be adopted in an irrigation project area,
particularly in the event of a shortfall in water supplies.
28
The irrigation managers in the area are required to prepare a ‘water indent’ (release
requirement) for each distributary before the beginning of each irrigation cycle, based on the
anticipated water requirements of the rice crop in its area to the end of the cycle, after accounting
for channel losses through seepage. The indents for individual distributaries are aggregated to
prepare a water indent for releases into the Patna canal of which they form the water distribution
network. Precise estimation of irrigation requirements of the crops for each irrigation cycle at the
distributary level is therefore critical for main system management in the project area.
The irrigation requirements for an irrigation cycle of a distributary depend on the
standing water depth in the rice fields at the beginning of the cycle and the anticipated
evapotranspiration and percolation losses to the end of the cycle. These would vary for different
fields depending on the dates of transplanting. They would also depend on the forecast
information on rainfall. A systematic procedure for assessing the crop water status and irrigation
release requirements in different distributaries in real time, which includes the variations in
transplanting dates and other spatial variations within its total area, can assist irrigation managers
in making more realistic irrigation demand estimates. Incorporating the procedure in a GIS
environment will permit interactive selection of distributaries on a computer screen to estimate
their release requirements. This will provide a powerful decision support tool for the main
irrigation system management in the area.
The decision support system (DSS) has two components. The first component is
essentially a spatial information system of the canal network with the distributary as the basic unit
of information. The second component will enable irrigation managers to decide on the ‘water
indents’ for each distributary, by estimating in advance the irrigation releases required at the head
of the distributary for each biweekly cycle of its operation.
The basis of the DSS is that if a “soil water balance model” is linked dynamically to the
GIS of the canal system with the distributary as the basic unit, the irrigation releases for any
distributary can be estimated on-line by simple interactive selection of the distributary in the GIS.
The selection will automatically identify (from the attribute table in GIS) the relevant input data
files for the soil water balance model (rainfall, soil and crop data files) for the selected
distributary. Since rice is the only irrigated crop in the area in the rainy season, the crop
transplanted on different dates is treated as an independent crop and the soil water balance model
is run separately for each date of transplanting. The releases for each distributary will depend on
water requirements of crops (rice transplanted on different dates) estimated by the model, their
areas and conveyance efficiencies. The soil water balance model is thus run at daily time steps in
two stages: (i) with current season data of daily weather up to the starting date of the irrigation
cycle, and (ii) with forecast data of weather to the end of the irrigation cycle. (For current model
development and demonstration, historical data are used as a perfect forecast)
This two-stage process is repeated for each irrigation cycle of the distributary. In this
way, the GIS-based framework facilitates interactive selection of the distributary, and in
preparing a water indent for the distributary for the next irrigation cycle.
29
Dynamic linkage between model and GIS
The GIS of the Patna canal system and the rice water balance model were dynamically
linked for real time application in any season (Fig. 19). This linkage allows:
(i) Selection of the distributary of interest on screen to identify the corresponding weather station
and soil data files.
(ii) Running the rice field water balance model for each transplanting date in real time up to
current date in any year, after entering the current date in response to screen queries.
(iii) Preparing a report of the current water status in rice fields in the command area of the
distributary transplanted on different dates.
(iv) Preparing a water indent for the irrigation requirements at the head of the distributary for the
next irrigation cycle, after accounting for weather forecasts and conveyance losses.
(v) Proceed to next distributary.
Steps (i) to (v) are carried out sequentially and on-line within the GIS environment. The
user need not at any stage come out of the GIS environment. For steps (i) to (iv), the complete
sequence is run for each distributary with actual rainfall data up to the current date and with the
forecast data of daily rainfall for the next 14 days of the irrigation cycle. At the end of this cycle,
Fig. 19. Dynamic user-GIS-model linkages in a Decision Support System
which is also the beginning of the next cycle, the actual rainfall data for this period would be
available. Before the irrigation indents are prepared for the next cycle, the actual rainfall data of
previous cycle are used to assess the water status at the beginning of the cycle, and the entire
sequence is repeated. For this reason, the model needs to be run twice for any irrigation cycle –
30
first with forecast rainfall for the current cycle and then with the actual rainfall in this cycle, when
the irrigation cycle advances to the next.
Data used:
The DSS dynamically links a field irrigation demand prediction model for the area
irrigated by a distributary with a GIS of the canal network. The system allows interactive
selection of distributaries and on-line real time estimation of water demands in each distributary
over the entire network.
For real time estimates, the model is used with current season information on the following
factors such as,
•
Distributaries
•
Weather (Rainfall, Temperature, Wind)
•
Weather forecasts,
•
Crop grown and their pattern
•
Type of soil and related factors.
Input data for soil water balance model:
•
Identification number of distributary (selection in GIS)
•
Corresponding identification numbers of raingauge stations and soil types (from GIS
attribute table)
•
Daily rainfall for each raingauge station (mm)
•
Forecast rainfall
•
Initial soil moisture and soil moisture content at saturation for each soil type (mm/cm)
•
Reduction factor for hydraulic conductivity because of hard pan (for each soil type)
•
Transplanting date (day and month)
•
Number of transplanting dates
•
Crop duration (days)
•
Maximum root depth (cm)
•
Days to attaining maximum root depth
•
Bund height (mm)
•
Date of start of drainage period and duration
•
Days to cut-off date of irrigation
•
Daily reference evapotranspiration (mm)
•
Daily crop coefficients
Outcomes:
Individual distributaries can be selected by users from the GIS and reports of water status
in fields and indents for water for the next irrigation cycle on any given date can be prepared
quickly on-line and in real time. The results for one cycle are presented in Fig. 20. Once the
distributary is selected, the soil water balance model runs at daily time steps with the soil, rainfall
and crops data up to the starting day of the irrigation cycle for which the water indent for the
31
distributary is to be prepared. For the two week period following this date, the model uses the
forecast rainfall data to calculate the daily water balance to the end of the irrigation cycle. To
illustrate the method, daily historical data for the period of the irrigation cycle are used as a
perfect forecast. The report shown in Fig. 20 is prepared for such a perfect forecast. The report
also lists information on the soil water and crop conditions at the beginning of the irrigation
cycle. The entire process (after the distributary is selected in GIS) is automated and made userinteractive within the GIS. The results of running the model for one distributary with historical
rainfall data of one season, assuming perfect rainfall forecasts for all the irrigation cycles of 14
days beginning July 1 are presented in Fig. 21.
Fig. 20. GIS-model output for selected distributary.
Fig. 21. Irrigation indents (required releases) for Paliganj distributary
32
Application:
The developed DSS can estimate the demand at the head of the distributaries of the canal
network in advance for each irrigation cycle for different crop with due consideration of spatial
variations in weather and different crop growing periods.
The real time water demands for any distributary can be estimated by linking
dynamically the GIS of the canal system with a soil water balance model and current season data
of weather, weather forecasts, and crop and soil conditions.
The database of different canal network can created in the GIS, which also allows to
quick estimation of the variations in irrigation requirement in different distributaries that form the
main canal network and comparisons with the available channel capacities and actual supplies.
Such visualizations, when combined with strong agronomic knowledge and judgment, can have a
powerful impact on the overall water management strategy to be adopted in the command area of
the irrigation project.
4.3 Decision Support System for Integrated River Basin Management
John W. Labadiea in 2005 developed MODSIM a comprehensive Decision Support
System (DSS) for coordinated operation of multipurpose reservoir systems, conjunctive surface
and groundwater management, and water quality management, with full consideration of legal
and administrative mechanisms governing water use. MODSIM is designed for developing basinwide strategies for short-term water management, long-term operational planning, drought
contingency planning, water rights analysis and resolving conflicts between urban, agricultural,
and environmental concerns at San Diego Water County Water Authority in Southern California.
Severe pressures have been placed on water managers world-wide as many river basins
have been plagued by extreme hydrologic condition ranging from severe droughts to catastrophic
flood events. This has been compounded by rapid population and industrial growth that has
placed increased stress on available water resources, creating conflicts between stakeholders for
water use. The importance of sustainable management and operation of existing water projects
and facilities is magnified because of political, economic and environmental obstacles to
authorization of new water projects. Management of complex river basin systems requires
effective decision support tools for analyzing system components in a fully integrated manner.
Therefore a DSS was developed for analysis of long term planning, medium term management,
and short term operations on desktop computers operating under MS Windows 2000/XP.
MODSIM includes a powerful, interactive graphical user interface for creating, locating and
connecting river basin network components, as well as spreadsheet-style data editing in an objectoriented spatial data base management system. Flexible data import and export tools are included
for interaction with external data base management systems.
The most recent version MODSIM 8.0 is developed under the MS .NET Framework and
is comprised entirely of native code written in MS Visual C++.NET. The MODSIM graphical
user interface is developed in Visual Basic.NET, and includes both native code and software
requiring a developer license, but allowing free distribution of runtime applications without
33
imposition of distribution costs or licensing requirements. The graphical user interface (GUI) for
MODSIM as shown in Fig. 22. provides spatially-referenced database capabilities allowing users
to create and link river basin network objects on the display, and then populate data for that object
by right-mouse click to activate the object and open its tabbed database form. GIS raster layers
may be imported into the GUI as background maps for network creation.
Fig. 22. Graphical user interface for MODSIM
Output Control provides an extensive variety of graphical and text output options for any
combinations of network objects and output data types. Retaining output results over several time
steps in main memory generally results in faster execution speed, but also requires larger memory
allocation. After a MODSIM run is executed, right button mouse click on any node or link opens
the context menu, but with an added item: Graph, which allows rapid display of output results.
Any number of additional nodes or links can be selected, providing comparative display of output
results on the same graph, as shown in Fig. 23. Several networks can be opened simultaneously
reflecting various planning and management scenarios, with results easily compared, including
probabilistic flow duration curves and various statistical measures including reliability, resiliency,
and vulnerability.
34
Fig. 23. Graphical plotting of output results for comparative analysis
GEO-MODSIM is a full implementation of MODSIM 8.0 that operates as a custom
extension in ArcGIS (ESRI, Inc.), allowing automatic generation of MODSIM networks from
geometric networks and processing of spatial database information for MODSIM network
features (Fig. 24). GEOMODSIM networks can be developed, edited, executed, and output
results displayed completely within the ArcMAP interface for ArcGIS.
Fig. 24. Display of GEO-MODSIM as implemented extension in ArcGIS (ESRI, Inc.)
35
Application:
MODSIM is applied to identifying opportunities for water conservation in the Imperial
Irrigation District (IID) that can generate up to 370 million m³ per year of transferable flow to the
large urban areas of Southern California. The IID includes over 2000 km² of irrigated farmland
served by 2736 km of canals and laterals distributing flow to 5300 farm delivery gates. The high
efficiency of the MODSIM solver allows fully integrated modeling of the IID network,
comprising over 10,000 nodes and links. MODSIM is an invaluable evaluation tool for
stakeholders, which include IID farmers, the San Diego County Water Authority, and
environmental interests concerned about impacts of the conservation plan on drainage quantity
and quality to the Salton Sea. In addition to conservation planning, MODSIM is also being
configured to provide daily and possibly hourly, real-time regulation guidance for automated
control of gates in the IID system.
4.4 Decision support system for urban flood management
Abebe and Price developed a Decision Support System (DSS) to manage the dynamic
information acquired by telemetry, feed these data along with relevant static data to an array of
modelling tools, forecast
flooding
conditions
and
assist public authorities in
decisions
regarding
emergency
measures
targeted at Liguria Region
in Italy and the Greater
Athens area in Greece in
year 2005.
Parts of both areas have
been
severely
flooded
several times in the past 20
years. The Athens area is
part of one large catchment
of 430 km², whereas the
Liguria Region consists of
several small catchments,
Fig. 25. System architecture of the developed DSS
the largest of which, Entella, has an area of 370 km². The two application areas share similar
topographical and meteorological characteristics. Both include urban areas situated on the coast
and are prone to flash flooding by runoff from mountainous areas.
The DSS was designed in such a way that it can work with any flow-modelling system
only with the help of a postprocessor that can transfer the data in the right format. The operational
flow-modelling system used for the current study areas consists of a detailed conceptual rainfall–
36
runoff model and an associated hydraulic model for the calculation of flood stages. The
TELEFLEUR DSS is designed to work at the centre of other essential components such as the
hydro meteorological forecast models, the telematic network and relational databases. Fig. 25
shows the operational link between the DSS and these components. The study indicates that most
of the uncertainty in the system developed for the application areas discussed in this report seems
to come from the precipitation forecast. It is known that now casting techniques, such as the
Doppler weather radar technique, provide more localized forecasts but the forecast lead-time is
limited. Thus, it is recommended to use both short-range forecasting and radar now-casting
techniques together, in which case the latter technique could be used to localize the forecasts
made by the former as the event approaches. Some reports recommend local calibration of the
rainfall forecasting model with the use of rain fields obtained from rain gauges and radar to
correct the state variables of the models. The DSS is loosely coupled with the associated forecast
models makes it easily applicable to other river systems with similar response time to
meteorological events. That would need meteorological and water level forecast models of that
catchment. It can as well be applied to catchments with slow response. In that case, the
hydrologic models could use observed precipitation and upstream flow data. The use of observed
precipitation will certainly improve the accuracy of water level forecasts. However, the required
effective lead time is a key factor here. Analysis has to be made prior to its application.
Additional improvements can be made to the DSS.
4.5. Decision Support System for Irrigation Water Management
Rao and Anuradha (2005) developed a Decision Support System (DSS) for management
of irrigation water at Centre for Good Governance, Hyderabad, India. It provided the water
management authorities with a well-structured, user-friendly, practical and complete
Management Information System (MIS). It assists the decision makers in taking the right
decisions on the basis of good comparison of different strategies under various scenarios, and
combines the benefits of Geographic Information Systems, expert systems and simulation
models. The scarcity of water has led to competing claims from farmers of different geographical
locations and the cropping pattern devised by the Government to ensure equitable distribution of
water is not practiced by the farmers. This has made it imperative to develop a Decision Support
System (DSS) to enable decision making for rational distribution of water to the command areas.
The water flowing into Andhra Pradesh is not sufficient to serve the command area that
the projects are supposed to serve. The problem has been further compounded due to poor
irrigation efficiency, inadequate system maintenance and lack of accountability. In order to
improve irrigation management in Andhra Pradesh, this project is done to improve the existing
decision support systems for irrigation management, thereby improving the efficiency of the
existing system and thus ensuring judicious distribution of the water distribution services to all
areas.
37
Data used
Accuracy of information and knowledge base has been achieved by utilizing data from
• IRS P-6 Satellite imagery
• Survey of India Topo Sheets
• Existing Canal and Distributary’s network
• Seasonal crops and water requirements
• Details of losses (Evaporation
and
seepage)
Software used
The DSS has been developed by an unique customized assembly of
• Arc IMS
• Oracle 9i
• ASP and
• Arc SDE software
tools.
Structure of the Decision-Making Support System Irrigation Water Management is related to
available water, crop water requirements, command area, cropping pattern, canal and distributary
network etc. During the irrigation season these data change dynamically. Therefore, the Decision
Support System for irrigation water management has been developed with the dynamic features
by collecting, and processing information dynamically. The structure of the decision-making
support system is shown in Fig. 26.
Fig. 26. Structure of the Decision Support System
Major output of this web based application would be a user friendly interface with step
by step guidelines for decision making. This system would enable the officials to plan releases of
water to the canal and distributary network taking into account the water availability, cropping
pattern, crop water requirements and geographical distribution. The output showing the villages
irrigated by releasing water to a particular set of distributaries is shown in Fig. 27.
38
Fig. 27. Output of the Decision Support System
Applications:
This system is used by the officials of Agriculture and Irrigation Departments and senior
government officials in charge of decision making. These officials need to make decisions on the
quantity of water to be released to the canal and the rational distribution of it under each project.
The e-Development cell of the Centre for Good Governance has developed this system for the
Irrigation and Command Area Development (ICAD) Department, Government of Andhra
Pradesh. This is enabling the Government to take decisions based on a scientific basis and thus
ensuring timely release of water for irrigation. The system can be replicated for distribution of
water in the other river basins in Andhra Pradesh and also other states across India.
Thus objective of the DSS developed under the study is to assist the Government in
decision making for the judicious release of water into the canals, distributaries and rationally
distribute it to the command area under each project.
Aspects of the software:

Web based GIS involving spatial data analysis.

Integrating of the age old data of irrigation systems creates a knowledge bank and the
decision support system develops a novel technique in management.

Querying system enables Data analysis through (predictive modeling & forecasting).

The software also facilitates generation of MIS reports (Distributary, Village, Mandal,
District and Constituency wise).

GIS maps indicating the thematic areas of irrigation
4.6 Decision Support System (DSS) and GIS for Sustainable Watershed Management in
Dong Nai Watershed
Loi (2006) developed a Decision Support System (DSS) typically SDSS (Spatial
Decision Support System) for formulating the plans for sustainable watershed management at
Dong Nai watershed, Vietnam. It is a mathematical combination approach consisting of Linear
39
Programming (LP), Goal Programming (GP), and Geographic Information System (GIS). Three
management scenarios i.e., Scenario A “Based on Existing Socio-Economic Trend”; Scenario B
“Land Allocation for Maximizing Income”, and Scenario C “Land allocation for environment”
have been applied to find alternative land – use planning for Dong Nai watershed.
Degradation of watershed is a common phenomenon around the world. There are several
reasons for such degradation, but most important is improper utilization of watershed resources,
among which land use allocation is the most important. Land use allocation has affected
watershed and land degradation. In Dong Nai watershed, large forest area has been replaced by
the expansion of agricultural area, for food subsistence and then, for cash crop production,
especially since the beginning of the "open economy" in 1980s. Traditional management systems
for forest, land and water have been replaced by subsidiary state-run enterprises and agencies,
which were not well motivated to enforce formal regulations and to stop the trend of becoming an
open-access situation. Hence, DSS was developed for Dong Nai watershed in context of
watershed management through the Linear Programming and GIS criteria-DSS (Decision Support
Systems) approach.
The Decision Support System (DSS) techniques namely Mathematical programming,
Linear Programming (LP), Goal Programming (GP), MINMAX formulations, Geographic
information system (GIS), and multi-criteria decision analysis were employed to ranking the
desirable priorities, their potential outcomes, and quantifying their achievement level
respectively. Mathematical programming makes it possible to obtain the optimal solution of the
problem in order to make the objective function maximum or minimum while fulfilling all other
requirements at the same time. Mathematical programming is able to give a synthetic approach to
complex situations such as for sustainable watershed management in Dong Nai watershed.
Software used:
The algorithm was developed in ArcView GIS software. The tools employed for deriving
the sustainable watershed management plans in this study were Stat graphics Plus 5.0, LINDO
software, and GIS system of Arc View program.
The Multi-Objective Linear Programming (MOLP) techniques and GIS have been
applied to display the optimum land resource allocation in different scenario, in order to evaluate
the sustainable strategy of land development in a watershed. The information incorporated into
the optimization objectives include economic benefits characterized by net income, sediment
yield, and water discharge. The decision variable coefficients derived for applying in LP model
and GP in this study are presented for the watershed scale. These quantitative coefficients and
their application in solving the land use allocation for sustainability. The type of spatial planning
problems described allocates different land uses across a geographical region, subject to a variety
of constraints and conflicting management objectives. This complex land use planning decisions
were made not only on what to do (selection of activities) but also on where to do it (i.e., relocate
a new set of suitable land use for different scenarios), adding a whole extra class of decision
40
variables to the problem. The new location of suitable land use has been manipulated using the
given criteria in term of slope, soil depth, and rainfall. The final step of DSS involves with the
geographical distribution of the land allocation of different proportion land use from GP solution.
For instance, the new forest land can be located by using the given criteria, such as slope, rainfall,
soil depth. The algorithm was developed in ArcView GIS software to relocate land use map in
Dong Nai watershed. The developed DSS performed very well in Dong Nai watershed, and
applicable to any watershed in Vietnam.
4.7 Decision support system for efficient water management in canal command areas
Rao and Rajput (2009) developed a decision support system (DSS) for canal water
releases (CWREDSS) to provide demand-based optimal canal water releases for reducing the gap
between canal supplies and demands for increasing the water-use efficiency in canal command
areas of Guvvalagudem major distributary of the Nagarjunasagar Left Canal, Andhra Pradesh,
India. Results indicate that the CWREDSS is capable of developing releases under different
scenarios of varying cropping patterns, groundwater use situations and different rainfall
probability levels of the study area, and reduced the gap between demands and supplies
considerably. DSS provides suggestions/ decisions under different situations of water
deficit/surplus.
Problems of low water-use efficiency (about 30–35%) and inequitable distribution of
water among the beneficiaries are usually highlighted. Most of the major irrigation command
areas in India suffer from problems of inadequate and unreliable water supply, having wide gaps
between irrigation potential created and utilized. This leads to temporal imbalance of water
demands and supplies, excessive seepage losses and rise of groundwater table, resulting in
problems of water logging and salinity. In addition, failure of monsoon rains, resulting in water
scarcity and drought lead to disputes among the water users. All these problems exists due to
inadequate attention paid to the assessment of water resources, non matching of canal water
releases with rainfall, crop water requirements and change in the cropping pattern from what has
been envisaged at the time of planning. DSS will help in the decision-making process, to
understand the problem and explore various alternative courses of action. DSS helps the user to
analyze facts and situations, to try out several different scenarios, and help in selecting the most
appropriate decision. The available DSS for irrigation water management is either noncomprehensive and does not consider actual multi-crop systems or does not account for water
distribution on the basis of shorter time intervals (i.e. weekly) and do not incorporate the concept
of equity. Keeping these considerations in view, in this study a decision support system for canal
water releases (CWERDSS) was developed to provide demand-based water release strategies for
reducing the gap between canal supplies and demands and to help irrigation engineers,
agronomists and agro-meteorologists in planning, operation and management of irrigation
systems efficiently. CREWDSS can reduce the water scarcity of the tail-end farmers and increase
the water-use efficiency in canal command areas.
41
The DSS was developed in the form of a computer program using the input interactive
controls and algorithms of Visual Basic 6.0 programing language. The nested ‘If. . . Then . . .
Else’ construct was extensively used as in interactive algorithm for the generation of alternative
decisions using the input information.
Development of DSS is an incremental process. The lifecycle of DSS involves four main
stages: (1) knowledge acquisition, (2) problem structuring and system design, (3) problem
encoding and (4) system testing. Canal water releases at head works are the function of crop
water requirement at field level, groundwater supplies, seepage losses in canal network, canal
geometry and water availability. While developing releases, all these components have to be
estimated. Keeping these in mind, six modules were developed for the DSS, i.e.
evapotranspiration (ET0), rainfall, crop, seepage loss, and groundwater use and release module.
Each module was made for estimating a particular component of canal water releases. The
architecture of the DSS for canal water releases is presented in Fig. 28. Each module contains the
input menus, output menus, text boxes and radial boxes.
Fig. 28. Architecture of the decision support system for canal water releases
The crop module contains various menus and text boxes for crop information (Fig. 29). In
this module, the inputs are crop acreage, crop planning, crop growth stages, crop coefficients,
specialneeds and application efficiency. Generally evaporation losses in the canal network are
ignored in comparison to seepage losses. These seepage losses depend upon the type of soil and
the ratio of the wetted area to the discharge rate in a channel. In this module, seepage losses in the
canal network are estimated based on actual canal geometry and constant seepage rate (Fig. 30).
42
The study developed
a DSS, namely CWREDSS,
for providing demand-based
optimal canal water releases
for reducing the gap between
canal supplies and demands,
thereby increasing the wateruse
efficiency
in
canal
command areas. CWREDSS
will
determine
crop
evapotranspiration, total cropwater requirement, effective
rainfall and irrigation water
requirement of crops. This
Fig. 29. Crop module of CWREDSS
system will also determine the seepage losses in the canals, groundwater use, canal water
demands at the head of the
canal
and
finally,
will
develop canal-water releases
by accounting for
water
demands and canal capacity.
The
DSS
provides
suggestions under different
water
deficit/
surplus
situations. CWREDSS will
help
irrigation
agronomists
and
meteorologists
planning,
management
engineers,
agro-
in
operation
of
the
and
Fig. 30. Hydraulic details of minor and estimated seepage losses.
irrigation
systems. The developed DSS was evaluated under different situations of the case study area.
From the results it can be concluded that the CWREDSS is capable of developing releases under
different scenarios of varying cropping patterns, groundwater-use situations and different rainfall
probability levels of the study area, and reduced the gap between demand and supply
considerably. CWREDSS can also be used to determine the most suitable cropping pattern for
efficient utilization of the water resources of a canal command area. The CWREDSS software
was distributed to officials of Nagarjunasagar Project (NSP), Khammam Division, AP. They were
tested for some canal command areas and the results obtained are encouraging. This software is
also available free of cost to users at different command areas.
43
Application of the DSS
CWREDSS will help irrigation engineers, agronomists and agro-meteorologists in the planning,
operation and management of irrigation systems.
4.8 Development of a DSS for Integrated Water Resources Management in Bangladesh
Zaman, et al, developed a decision support system for Bangladesh in 2009. The Institute
of Water Modelling (IWM), in Bangladesh, hosts a suite of hydrodynamic models that can
estimate river stages and discharge, flood and groundwater levels and water quality variables for
most rivers and regions of the country. Researchers at IWM have developed a water resource
Decision Support System (DSS) that can use outputs from their mathematical models to simulate
and predict likely impacts on key sectors, such as agriculture, infrastructure, environment,
fisheries, navigation, etc. It is envisaged that this DSS will assist policy makers and planners by
providing information about likely impacts of climate variability and water related projects in
Bangladesh.
The development of DSSs is a key feature in IWM’s long-term plan. This involves
meeting the growing demand to advance from integrated modelling software to hydroinformatic
objects, such as DSSs. This evolution should assist resource managers and decision makers to
adapt better to climate change and chronic water-related challenges, such as arsenic
contamination in groundwater. Further development of the DSS involves incorporating
optimization routines that can generate adaptation options subject to constraints in the human and
environmental systems. IWM’s suite of mathematical models is useful tools to identify and
estimate impacts of climate change and adaptation measures. However, the outputs of these
models need to be translated into values that are meaningful to decision makers.
Decision being made by advanced and complex DSS that related to environmental,
economic, social aspects. This is achieved by using different models and datasets to create
flexible linkages between the different bio-physical and socioeconomic aspects of a resource
system. Thus, a DSS enables its user to quickly analyze and compare alternative courses of
actions or strategies under different uncertain developments or scenarios to demonstrate the
impacts of different options or alternatives. There are several DSS development approaches as
shown in Fig. 31. At the start of the development process, in the conceptualization stage, the
potential uses of the DSS by stakeholders were assessed based on experiences of past IWM
projects. From the consultation process, several concepts were developed.
44
In Fig. 32, the general
usage steps of the
DSS are shown. The
user will define the
scenarios
to
be
analyzed and provide
the DSS with relevant
outputs
from
hydrodynamic models
and thematic, landuse maps of impact
sectors (in the form of
GIS layers). The user
then
selects
Fig. 31. Different stages of the developed DSS
the
necessary impact functions for each sector and the DSS generates impact reports for the scenarios
being analyzed. Users do not have to run the IWM models themselves but need to work closely
with
IWM
modelers so that
new runs can be
specified
when
adjustments
are
made
the
to
scenarios
analyzed.
the
From
consultations
held in the DSS
conceptualization
stage, information
about the type of
DSS required was
obtained and the
basic architecture
was
also
Fig. 32. Concept used in the DSS
developed.
DSS Design and Implementation:
The DSS is GIS-based and primarily data driven. However, it also has features of a
model-driven DSS. In the implementation step, the designs were coded by software programmers
in relevant platforms for each prototype. The first prototype was a MS Excel file that included
45
separate sheets for: user
interface,
crop
details,
crop damage calculation,
fisheries details, river-beel
connectivity
sample
model,
hydrodynamic
outputs, sample land-use
grid (representing a raster
map).the basic process in
prototype
decision
support system (DSS) is
shown in Fig. 33 Several
versions
of
spreadsheet
this
were
developed as more details
were added to the sector
response modules. These
prototypes
were
used
during consultations with
different
stakeholder
groups. When a particular
sector response module
reached a stable version in
Fig. 38. Basic processes considered in the DSS
Excel, it was passed on to software programmers. The programmers coded the module into a GIS
program linked to a geospatial database. During these consultations with stakeholders, it was
quickly realized that the interface should be bilingual (English and Bengali) and this was
incorporated into the DSS design. Eventually, through testing of several prototypes the final
prototype IWRM DSS was obtained.
Potential DSS users and stakeholders in Bangladesh have a wide range of skill sets,
including language requirements. Detailed data for sector impacts are not readily available but
this situation is improving. The DSS has to take this account and designed so that users can input
new data easily. Also, the DSS has to be flexible so that new sectors and impact functions can be
easily incorporated by advanced DSS user. Also, the DSS should incorporate economic and
climate change adaptation modules. When developed, the full DSS can help assess the benefits
and costs of various climate change impacts, adaptation policies and projects.
46
Chapter V
Conclusions
Revolutions in information technology have led the path for development of Decision
Support Systems in different fields as per the requirement of the user community. DSS replaces
the time consuming activity of looking into different data bases and arriving at desired solution to
a given problem. DSS being a computer-based information system linked with different
data bases assists in quick decision making or generation of alternate scenarios of rapidly
changing and not easily specified problem domains. Present review highlights the
availability of different DSS in management of water resources which are used by the
researchers and decision makers around the globe. Efforts have been made to include
different categories of DSS including the geospatial tools for management of water
resources besides its use in agriculture. The input data requirement of the DSS, the output
of the DSS and its operation environment including the source of its availability are
mentioned for the reviewed DSS. Majority of the DSSs’ reviewed in this technical
bulletin are flexible and can be updated with additional data base to make it more
meaningful in solving real world problems. The reviewed DSS are user friendly and can
be operated without any assistance or with minimal training. The help files are also
available listing the sequential steps of operation of the DSS and definition of different
input parameters used in the DSS. The reviewed DSSs are also validated under different
scenarios and the results were in line with the observed parameters acquired from ground
truthing and primary data generated through conduction of field experiments.
Development and validation of the DSS under different field conditions and its
subsequent use by the stakeholders are being discussed at several forums. The DSS
developed using the data from a specific location without using soil-water-cropenvironment parameters of other locations is tagged as the location specific DSS and the
scenarios suggested by the DSS lacks in its wider applicability under variable issues
pertaining to water resources management. Therefore, the DSS populated with data of
different locations including the spatio-temporal variability parameters could be used for
generation of scenarios to meet the requirement of different regions. Nonetheless, the
DSS should be developed using all possible input and output parameters emerging from
different issues of agricultural water management and should be linked by a knowledge
driven programming construct or mathematical models to generate alternative scenarios
for judicious management of natural resources.
47
References
Dash J., Sarangi A. and Singh A.K. 2008 A Decision Support System on Biodrainage for Land
Reclamation. Journal of Soil and Water Conservation,Vol:7, Issue:1, pp:33-37.
Gao, Z. 1999 Decision-Making Support System for Irrigation Water management of Jingtai
Chuan Pumping Irrigation Scheme at the Upper Reaches of Yellow River, Paper
presented in Watsave Workshop 51st IEC, Cape Town, South Africa. Irrigation Water
Management of Jingtai Chuan Pumping Irrigation Scheme.
Katyal, J.C., Kaushalya Ramachandran, Narayana Reddy, M. and C.A.Rama Rao. 1996. Indian
Agriculture-Profile of land resources, Crop performances and Prospects. Proc. South Asia
regional Workshop on Regional Land Cover changes, sustainable Agriculture and their
interactions with global change. COSTED, ICSU and UNESCO, 16-19 December,
Madras.
Kaur, R. 2008. Planning length of long-term field experiments through decision support systems
– a case study. Applied ecology and environmental research 6(2): 63-78.
Labadiea, J. W. 2005. MODSIM: Decision Support System for Integrated River Basin
Management, Department of Civil Engineering, Colorado State University, Fort Collins,
CO 80523-1372, USA.
Loi , N.K. 2006 Decision Support System (DSS) and GIS for Sustainable Watershed
Management in Dong Nai Watershed, International Symposium on Geoinformatics for
Spatial Infrastructure Development in Earth and Allied Sciences.
Mandal, D, Sharda, V.N. (2011). Assessment of permissible soil loss in India employing a
quantitative bio-physical model, Current Science, 100(3): 383-389.
Manoli E., Arampatzis G., Pissias E., Xenos D. and Assimacopoulos D. 2001. Water demand and
supply analysis using a spatial decision support system. Global Nest: the Int. J. Vol 3, No
3, pp 199-209.
Naveh N. and Shamir U., 2001 Decision support system for the Hula project. Israel Institute of
Technology.
Pertiwi S, Konaka T, Koike M. 1998. Decision Support System for management of upland
farming with special consideration on soil conservation. In Proceeding of First
Conference on Agricultural Information Technology in Asia and Oceania-24th to 26th
January. The Asian Federation for Information Technology in Agriculture: WakayamaCity, Japan; 73-78.
Pimentel, D. 1993. World Soil Erosion and Conservation. Cambridge University Press, Great
Britain.
Ramachandra, T. V. And Kumar, U. 2004. Geographic Resources Decision Support System for
land use, land covers dynamics analysis, Proceedings of the FOSS/GRASS Users
Conference - Bangkok, Thailand.
48
Rao B.K. and Rajput T.B.S 2009 Decision support system for efficient water management in
canal command areas. Current Science, Vol. 97, No. 1, 10.
Rao B. S., P. Anuradha 2005 Decision Support System for Irrigation Water Management, Centre
for Good Governance, Hyderabad, India http://www.gisdevelopment.net/publications.
Rao N. H., Brownee S.M. and Sarma P. B. S. 2004. GIS-based decision support system for real
time water demand estimation in canal irrigation systems. Current Science, September,
VOL. 87, NO. 5, 10
Sarangi A., Madramootoo C. A and Cox C. 2004. A Decision Support System for Soil and Water
Conservation Measures on Agricultural Watersheds. Land Degradation and
Development, 15, pp: 49–63.
Silva, L. M.; Park J. R. ; Keatinge, J. D. H. and Pinto P. A. 2001 A decision support system to
improve planning and management in large irrigation schemes, Agricultural Water
Management, Volume 51, Issue 3, 15 November, pp:187-201.
Takase, K. 1995. Global Environment and Agricultural Resource Management (IV) – With
Special Emphasis on Land Degradation by Salinization and Soil Erosion -. International
Development Center of Japan.
Turban, E 1995 Decision support and expert system. Prentice Hall , USA.
Zaman, A.M. , Rahman S.M.M., and.Khan M.R. 2009. Development of a DSS for Integrated
Water Resources Management in Bangladesh, 18th World IMACS / MODSIM Congress,
Cairns, Australia.
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About the authors
Dr Arjamadutta Sarangi : Post-Doc Fellow (Bioresources Engineering, McGill University, Canada)
Ph.D. (Agricultural Engineering, IARI, New Delhi, India)
Brief academic achievements:
Author’s interest in academics fetched him fellowships and scholarships
(including national talent search scholarship) during all stages of his schooling
followed by 90% marks in higher secondary examination and 89% marks in
Intermediate Science Examinations. He completed the Ph.D programme from IARI
during 1998 and received prestigious Jawaharlal Nehru Award in the year 2000 for his
significant research accomplishments in Agricultural Engineering. Dr Sarangi is
presently working as senior scientist at Water Technology Centre, IARI and have developed expertise in
hydrological modeling, agricultural field drainage system design and modeling the drainage effluent
quality and quantity, application of GIS and RS tools for integrated watershed management, use of crop
models, development of decision support systems and data mining tools for prediction of surface runoff,
sediment losses and water quality issues leading to implementation of best management practices for
conservation of soil and water resources over watershed systems. Besides this, he had developed expertise
on use of advanced tools and techniques for predicting the spatio-temporal variability of different
parameters over watershed, investigation of the impacts of climate change on regional water resources
variability and enhancing water productivity at field scales. He is involved in PG school teaching and
student supervision and has taught several courses as faculty of Agricultural Engineering. Dr Sarangi has
received several grants for conduction of research in management of natural resources and was awarded
BOYSCAST fellowship and completed the Post Doctoral fellowship in sustainable agriculture at McGill
University, Montreal, Canada. He has received several recognitions and awards including NAAS Associate
and young scientist awards and Shankar Memorial Award by ISAE. So far, Dr Sarangi has written 54
manuscripts besides popular articles and book chapters.
Dr Dhirendra Singh Bundel: Ph.D. (Geoinformatics), Cranfield University, UK
Dr Bundella is presently working as Principal Scientist at CSSRI, Karnal and
have developed expertise in development spatial decision support system, satellite based
water management strategies, crop water productivity, saline and waterlogged lands
mapping, soil erosion modelling, geoinformatics, radar remote sensing. He has
developed DSS for groundwater quality for suggesting suitability of contaminated
groundwater for irrigation purpose and implications on agriculture and public health in
Haryana. Mapped the waterlogged saline soil in Rohtak, Jhajjar and Bhiwani using RS
and GIS. Worked out water balance of biodrainage planatation under Indo-US
Agricultural Knowledge Initiative on Water Harvesting for Groundwater Recharge and Biodrainage for
Salinity control. Generated and tested digital terrain models from spaceborne stereogrammetry and radar
interferometry, aerial photograph, DGPS, topographical maps, etc. for distributed modelling.
Parameterized and tested a distributed and dynamic soil erosion model, LISEM for the influence of gridcell size and source of DEM on surface runoff and soil loss modelling as well as identification of soil
erosion areas in a watershed for implementing an effective conservation plan for partial area treatment. He
was awarded commonwealth Scholarship to undertake Ph.D programme at Cranfield University, UK and
have published several research articles and edited books chapters.
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