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IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002
Mundawar and Behror taluks, wells have a depth to water level
vary between 10-15 Mts BGL. Whereas in parts of Neemrana,
Rajgarh and Reni blocks located in the Northwestern and
Southern part of district have deep water level exceeding 15
meters due to over exploitation of ground water. In the
Southwestern part and pockets in the Eastern periphery of
district marginal rise in water level observed. Major part of the
district shows marginal depletion (3Mt) in ground level. Area
North of Alwar town extending northwestward up to Neemrana,
around Thanagazi, Reni and Rajgarh area recorded a depletion
of water levels between -3 to-7 meters range. Area around
Neemrana and small pockets north of Alwar exhibit steep
depletion in water ranging from 7 to 10 Mts below ground level.
Water conservation necessity: over exploitation of water
resources, especially ground water at alarming rates needs
immediate attention. The annual rainfall of the district is 600
mm that too confined for a shorter duration of July-September
months. Even in this volume, major part will go as waste in the
form of runoff and evaporation. Hence, there is an urgent
necessity to conserve the precipitation water to the optimum
extent possible. In this context, an attempt has been made to
locate the suitable water harvesting structures such as Check
dams and percolation tanks using decision support system and
Remote-sensing techniques developed at ADRIN.
Main Objectives of the study and Decision Space tool: The
main objective of this study is to suggest the suitable water
harvesting sites such as check dams and percolation tanks using
remotely sensed data and Decision Space (GIS) tool developed
at ADRIN, Dept.of Space. The main difference lies between the
Decision Space software and conventional GIS tools (like
Arc/Info) is the incorporation of experts Knowledge in the
former while computing various parameters/categories like
lithology, slope, hydrology, geomorphology, faults/ lineaments
drainage characteristics, land use etc., Using this tool, suitable
sites for water harvesting structures like check-dams/ percolation
tanks were successfully identified in Jukkal mandal, Nizamabad
district of Andhra Pradesh. Interestingly, the sites identified
through Decision software are in full agreement with the
locations earlier identified by experts using domain knowledge
through visual interpretation methods. The Decision Space
software was already integrated with the National Natural
Resources Information system (NRIS). A detailed description
about the Decision Space software, it's utility, weightage
assigned to various Parameters and categories by domain expert,
integration of various themes & generation of output that is
locating suitable water harvesting structures etc., are presented
herewith.
4. 4 .4 Data Used:
The Primary data comprises IRS-ID LISS-III imagery and
secondary data comprises SOI maps and thematic maps
published by National Bureau soil survey and Land use
(NBSSLU) and other sources:
A) Satellite Imagery: IRS-1D LISS-III Path 95 Row 52
dated March 05, 2001
B) B) SOI Maps on 1:50,000 Scale-54 A/6, 7, 10, and
54A/11
C) C) Soil map of Alwar District by NBSSLU on
1:250,000 Scale
D) D) Watershed Maps prepared by Regional Remote
Sensing Servicing Center, Jodhpur
E) E) Other literature on Rajasthan State.
Remote sensing technology in locating favorable water
harvesting sites: Remote sensing technology with its recent
developments especially in terms of spatial/ spectral resolutions
and also stereoscopic capabilities will play an vital role on
acquire timely and upto date information about terrain
conditions for locating suitable water harvesting structures in a
given region. Its a fact that satellite imagery will furnish
information about multi-themes like geology, geomorphology,
hydrology, land use/cover soils, drainage, lineaments &
639
structures etc., Availability of such an information in a common
format will not only provide criteria for deciding the most
appropriate site for locating rainwater harvesting structures but
also helps in assessing the area of influence and effectiveness of
such structures in a spatial context. The kind of voluminous data
acquired through remote sensing technology requires a system to
store, organize, and analyze different data sets to derive useful
information. Geographic Information Systems (GIS) is one such
system, which has the capacity to Model the real world spatial
problems.
4.4.5 Methodology:
4.4.5.1 Interpretation/analysis of remotely sensed data:
Thematic maps like hydrology, geomorphology, land use/cover,
lineaments/ faults, etc, were prepared using IRS-ID LISS-III
Geocoded Product on 1:50,000 scale visually. Basic
Information such as contours and spot heights, drainage,
settlements and transport network etc, were taken from 1:50,000
Scale SOI maps. Soil, Lithology and other information
incorporated from maps compiled by NBSSLU, Nagpur RRSSC,
Jodhpur. Since data compiled from different sources, was
brought to common scale through registration and rectification
process for further analysis & integration with Decision Space.
4.4.5.2 Data integration in GIS: The capability of GIS to store,
organize and analyze large amount of spatial and feature
attribute data makes it a useful tool for performing spatial
analysis. Information about various Parameters/categories in the
database needs to be modified in such a way to make them
appropriate for the intended use. The sort of manipulations
would develop new derived data layers, which forms the main
input for further analysis. From an analytical perspective view
the difficult task is the weighting the relevant spatial information
and derive the output data layers. The Analytical Hierarchy
Process (AHP) a Multiple Criteria Decision Making Method
(MCDMM) is the weighting strategy used to determine the
relative importance of a set of criteria. Fuzzy logic has been
included for handling continuous variables. Usually, the type of
spatial decision making problems typically involve a large set of
feasible alternatives and multiple, conflicting and
incommensurate evaluation criteria. The alternatives are usually
evaluated by a number of individuals (decision-makers/ interest
groups). The individuals are often characterized by unique
preferences with respect to the relative importance of criteria on
the basis of which the alternatives are evaluated. It is argued that
central to many spatial decision-making problem is the search
for consensus among various interested parties. Adapting
Analytical Hierarchy Process (AHP) method has helped us to
solve multi-criteria multi-person problem efficiently. Remote
sensing represents a powerful technology for providing input
data for measurement, mapping, monitoring and modeling
within a GIS context. Data from various sources are integrated to
derive useful information in the GIS environment. Ideal Point
Analysis (IPA) a compromise programming and an optimization
technique have been adapted for data integration. The DECISION
SPACE, software developed around Arc/Info at ADRIN contains
these decision-making methods and provides the user a decision-
making environment.
4.4.5.3 Steps Involved In Spatial Analysis.
> Identification of relevant information: Selection of suitable
site for water harvesting structures like Check-dams &
Percolation tanks etc., were to be determined based on the
physical characteristics/parameters of the terrain. These
parameters would then become the actual database for the
analysis. The Prominent factors that would have a definite
bearing on the site selection process for various water harvesting
structures include: A) Lineament density B) Slope/aspect C)
Rainfall D) Groundwater Prospects E) Geomorphology F) Land
use/cover G) Weathered Zone thickness H) Soil depth I) Soil
texture and J) Geology.
> Determine the of Weights of Parameters/categories using
Decision Space software: Weighting the parameters and
categories among the parameters was done using Analytical
