TABLE OF CONTENTS CONTENTS LIST OF FIGURES LIST OF TABLES ABSTRACT

i-ii iii iv v

1.

INTRODUCTION 1.1 Hydro-Geomorphology 1.2 Basic Concept of Watershed 1.3 Drainage Morphometry 1.4 Role of Remote Sensing in Groundwater Study 1.5 Importance of GIS for Groundwater Zonation 1.6 Geoprocessing 1.6.1 Geoprocessing Model and ModelBuilder 1.6.2 Scripting

1-12 2 3 4 5 8 10 11 12

2.

PHYSIOGRAPHY AND WATERSHED CODIFICATION SYSTEM 2.1 Location and Extension 2.2 Climate Condition 2.2.1 Rainfall 2.2.2 Temperature and Humidity 2.3 Watershed Delineation and Codification

13-20 14 14 14 17 17

3.

OBJECTIVES, DATA, MATERIAL AND METHODS 3.1 Objectives 3.2 Data and Material Used 3.3 Software Used 3.4 Methodology 3.5 Digital Image Processing 3.5.1 Pre-Processing 3.5.2 Post Classification Process 3.6 Integration With Geographic Information System 3.6.1 Process of GIS

21-27 22 22 22 23 23 24 25 27 27

4.

MORPHOMETRIC ANALYSIS 28-35 4.1 Drainage Network 29 4.1.1 Stream Order (u) and Number of Stream (Nu) 29 4.1.2 Stream Length (Lu), Average Length of Stream and Stream Length Ratio 4.1.3 Bifurcation Ratio (Rb) 30 4.1.4 Length of Main Channel (CI) 30 4.2 Basin Geometry 32 4.2.1 Length of Basin (Lb) 32 4.2.2 Basin Area (A) 32 4.2.3 Basin Perimeter (P) 32 (i)

4.3 4.4 4.5 4.6 4.7

4.2.4 Form Factor (Ff) Drainage Density Texture Ratio (Rt) Drainage Texture (Dt) Stream Frequency (FS) Drainage System and Pattern

32 33 33 34 34 34

5.

PHYSICAL PROPERTIES OF KARAWAN WATERSHED 5.1 Geology / Lithology 5.2 Geomorphology 5.3 Lineament 5.4 Slope Analysis 5.4.1 Digital Elevation Model 5.4.2 Slope 5.5 Soil Texture 5.6 Land Use / Land Cover

6.

GIS MODELING FOR GROUNDWATER POTENTIAL ZONE IDENTIFICATION 6.1 GIS Modeling 55 6.1.1 Why GIS in This Work? 55 6.1.2 Why Build Models? 56 6.1.3 What is GIS Based Modeling? 56 6.2 ModelBuilder 57 6.2.1 What is the ModelBuilder Window 59 6.2.2 What ModelBuilder Does? 59 6.3 Building Model 60 6.4 Integration of Remote Sensing and GIS 61 6.4.1 Buffer Analysis of Linear Features 61 6.4.2 Rasterization 65 6.4.3 Reclassification 65 6.5 Integration Analysis in GIS Environment 77 6.5.1 Weighted Determination 80

7.

GROUNDWATER POTENTIAL ZONE AND VALIDATION

83-88

8.

CONCLUSION AND RECOMMENDATION 8.1 Conclusion 8.2 Recommendation

89

ABBREVIATION ADDITIONAL READING APPENDIX GROUNDWATER POTENTIAL MODEL FLOW CHART

(ii)

36-53 37 39 41 45 45 45 50 51

90 91-92 93-96 97

LIST OF FIGURE 2.1

Location Map of Karawan Watershed

14

2.2

Average Rainfall data of Sagar Rain Gauge Station, Year 1941-1990

15

3.1

Methodology Flow Chart

26

4.1

Drainage Order and Drainage Network Map

31

4.2

Drainage Density Map

35

5.1

Lithology of the Karawan Watershed

38

5.2

Geomorphology of Karawan Watershed

40

5.3

Lineament Pattern in Karawan Watershed

43

5.4

Comparison between Drainage and Lineament Pattern

44

5.5

Digital Elevation Model of Karawan Watershed

46

5.6

3-D View of the Karawan Watershed

47

5.7

Slope Map of Karawan Watershed (in degree)

48

5.8

Soil Texture Map of Karawan Watershed

49

5.9

Graphical Representation of LU / LC of Karawan Watershed

52

5.10 LU / LC Map of Karawan Watershed

53

6.1

Depiction of Modeling Process Using Modulerbuilder (ESRI Education Series 2000)

6.2

Modulerbuilder Engine Windows

62

6.3

A Conceptual Overview of a Model

62

6.4

Model Element

62

6.5

Creating Free Floating Text

62

6.6

Flow of Project Data

63

6.7

Drainage Buffer of Watershed

64

6.8

Lineament Multi Ring Buffer Map

66

6.9

Showing the Lineament Multi Buffer Process

66

6.10 Showing the Drainage Buffer Tool on ArcGIS Modulerbuilder Engine

67

6.11 Showing the Polygon Shape file to Raster Feature on ArcGIS Modulerbuilder Engine 6.12 Reclassification of the all raster Layer on ArcGIS Modulerbuilder Engine

58

6.13 Lineament Buffer Weight Map

62

6.14 Geomorphology Weight Map

62

6.15 Lithology Weight Map

62

6.16 Slope Weight Map

62

6.17 Soil Weight Map

63

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6.18 Drainage Density Weight Map

64

6.19 Drainage Order Buffer Weight Map

66

6.20 LU / LC Weight Map

66

6.21a Weighted Overlay Dialog Box of ArcGIS

67

6.21b Weighted Overlay Dialog Box of ArcGIS

67

7.1

Composite Groundwater Suitability Unit Map

69

7.2

Groundwater Potential Zone Map

70

7.3

Distribution of Boreholes in Groundwater Potential Zone

LIST OF TABLES 2.1

Summary of mean Rainfall Data of Sagar Station, Year 1941-1990

2.2

Average Monthly Temperature (°C) and Humidity (%) of Sagar Station,

Period (1941-1990)

2.3

Classification of Watershed Unite

2.4

Karawan Watershed Codifications

4.1

Linear Aspects of the Drainage Network of the Karawan Watershed

4.2

Bifurcation Ratio

5.1

Explanation of the Lithology Characteristics of Karawan Watershed

5.2

Land Use and Land Cover Statistics of Karawan Watershed

6.1

Different Themes and Thematic Parameters Considered for Groundwater Prospects

Evolution and Their Class Weight

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ABSTRACT

W

ater is the most important natural resource which forms the core of the ecological system. The advent of remote sensing has opened up new vistas in groundwater prospect evaluation. A case study was conducted to find out the groundwater potential zones in Karawan watershed, Sagar District, Madhya Pradesh, with an aerial extent of 275.61 Km2. IRS LISS-3 satellite imagery was used for detailed hydrogeomorphological for Morphometric analysis and identifying ground water potential zones. The thematic maps such as geology (Lithology), geomorphology, soil, lineament, land use / land cover and drainage map were prepared for the study area. One of the most widely used digital elevation model (DEM) data sources is the elevation information provided by the ASTER DEM and obtained the slope (Degree) of the study area. Evaluation of the Morphometric parameters requires preparation of drainage map, ordering of the various streams and measurements of catchment area, perimeter, length of drainage channels, drainage density, bifurcation ratio, stream length ratio, which help to understand the nature of the drainage basin. Author had to evaluate the groundwater prospective zones because the groundwater resources in the area have not been fully demoralized. Different hydromorphogeological units have been differentiated based on image interpretation. The hydromorphogeological units such as structural landforms, structural hills (vindhyan sediments), Denudetional hills (volcanic), were identified and appropriate field confirmations were made. The geomorphic units such as lineaments, fractures, and pediplain were identified under structural landforms. The weathered buried Pediplain are the potential zones for groundwater targeting. The groundwater potential zones were obtained by overlaying all the thematic maps in terms of weighted overlay methods (Multi criteria analysis) in using the geoprocessing modular builder tool in ArcGIS 10. ModelBuilder engine is a comprehensive geographic decision support tool that makes the solving of complicated problem simple. ModelBuilder helpful for easily manage project analysis requirements, from simple geographic analysis to complex spatial modeling (ESRI Educational Services, 2000).During weighted overlay analysis, the ranking has been given for each individual parameter of each thematic map and weights were assigned according to the influence. The study shows that the remote sensing and geoinformatics techniques can be applied effectively for groundwater prospect evaluation.

(v)

CHAPTER- 1

INTRODUCTION

CHAPTER- 1 INTRODUCTION

G

roundwater is a dynamic and replenishing natural resource. But in hard rock terrains, availability of groundwater is of limited extent. Occurrence of groundwater in such rocks is essentially confined to fractured and weathered horizons. Poor knowledge about this resource, because of its hidden in nature and its occurrence in complex subsurface formations, has been and is still a big obstacle to the efficient management of this important resource. In India, 65 per cent of the total geographical area is covered by hard rock formations with low porosity (less than 5%) and very low permeability (10-1 to 10-5 m/day) (Saraf and Choudhary, 1998). The Remote Sensing and GIS tools have opened new paths in water resources studies. Remote sensing provides multi-spectral, multi-temporal and multi-sensor data of the earth’s surface. One of the greatest advantages of using remote sensing and GIS for hydrological investigations and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful analysis, prediction and validation (Sarma and Saraf, 2002). Some important terminologies are use for analysis, prediction and validation for identifying ground water potential zone of case study such as, hydro-geomorphology, watershed, drainage morphometry (Morphometric analysis), remote sensing, Geographic Information System (GIS) and geoprocessing. The Morphometric analysis of a drainage basin and channel network play a significant role in understanding the hydrogeomorphological behavior of the basin and expresses the prevailing climate, geology, geomorphology and structure, etc. The relationship between various drainage parameters and the above factors are now almost well established (Horton, 1945; Strahler, 1957). Recently several workers have used remote sensing data and GIS on morphometric parameters and have concluded that remote sensing and GIS has emerged as a powerful tool in analyzing the drainage morphometry (Agarwal, 1998; Nag, 1998; Das and Mukhrjee, 2005). The present study mainly aims to analyze the morphometric attributes and identifying groundwater potential zone using remote sensing and Geographic Information System (GIS) of the Karawan basin, Sagar District, Madhya Pradesh. Analysis morphometric parameters such as linear and areal are a major advancement in the quantitative study of the geometry of river basin which helps in understanding the influence of drainage morphometry on landforms and their characteristics. Groundwater prospect in an area is controlled by many factors such as geology, geomorphology, drainage, slope, presence of fractures, surface water bodies, canals and irrigated fields amongst “Ground Water Potential Modeling”

1

Chapter-1 others. Slope for example is one of the factors that control the rate of infiltration of rainwater into the subsurface and could therefore be used as an index of groundwater potential evaluation. In the gentle slope area the runoff is slow allowing more time for rainwater to percolate, whereas high slope area facilitate high runoff allowing less residence time for rainwater hence comparatively less infiltration. In one way or the other, each of the listed factors contributes to groundwater occurrence. These factors can be interpreted or analyzed with GIS (Geographic Information System) using RS (Remote Sensing) data. These factors can be interpreted or analyzed with GIS using RS data. Burrough (1986) defined a GIS ‘‘as a powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world for a particular set of purpose’’. GIS thus enables a wide range of map analysis operations to be undertaken in support of groundwater potential zonation of an area. Several conventional methods exist for the exploration and preparation of groundwater potential map of an area. These methods include; geological, geophysical and hydrogeological. However, RS amongst these methods is considered to be more favorable as it is less expensive and applicable even in inaccessible areas. It is a rapid and cost effective tool in producing valuable data in geology and geomorphology. In classifying groundwater potential zones, visual integration of data generated from remote sensing is feasible but cumbersome. However, with the advent of GIS technologies, the mapping of groundwater potential zones within each geological unit has become easy.

1.1 Hydro-geomorphology The term hydrogeomorphology can be divided into three terms hydro- means water including both surface and ground water; geo- means the earth and morphology- is the surface expression of the features in the form of landforms. This means that the hydrogeomorphology is dealing with the aspect of water, rocks and earth’s morphological features (land) of these water and land are most important natural resources for human beings. Almost all of geomorphology is Hydrogeomorphology, because water is one of the most important agents in forming and shaping of landforms. From the ground water point of view integration of geological, structural and hydrogeological data with hydrogeomorphological data is very much useful in finding out the groundwater potential zones with fruitful results. A hydrological unit is defined as “An area of land, above or upstream from a specific point on a stream, which is defined by a hydrological boundary that include all the source areas that could contribute surface water runoff, directly or indirectly to the designated outlet points” (NRCS, 1995). The term hydrological unit is synonymous to the basin, river basin, drainage basin, catchment basin, river catchment or the watershed. Of the most commonly used term are the basin, catchment and watershed. The hydrological unit can be classified on the basis of size, shape, drainage density, stream characteristics and other land characteristics. “Ground Water Potential Modeling”

2

Chapter-1 Now discussing about hydrological units we know that geomorphologists and hydrologists often view streams as being part of drainage basin. A drainage basin is the topographic region from which a stream receives runoff, through flow and ground water flow. Drainage basins are divided into number of hydrological units, which are separated from each other by topographic barriers called a watershed. Watersheds represent all of the stream tributaries that flow to some location along the stream channel. The number, size and shape of the drainage basins found in an area varies with the scale of examination. Drainage basins are arbitrarily defined based on the topographical information available on a map. The quality of this information decreases as map scale becomes smaller. During the last few years various applications of hydrogeomorphology with reference to the Geographical Information System (GIS) and various other techniques have emerged as an extensively effective tool for analyzing and identifying groundwater potential zone. The most important application of the hydrogeomorphological studies is the assessment of ground water potential zones of the watershed or basin. At first ground water potential feasibility map of the area is to prepare using multi-criteria analysis in geoprocessing tool (Modulerbuilder). Multi-criteria evolution is primarily concerned with how to combine information form single index evolution. An integrated approach was adopted using remote sensing and GIS techniques in the study area for evaluation of groundwater potential zones based on the characteristics of geomorphic units together with slope, geology and lineaments The study area has been classified into denudational hill, residual hill, pediment, pediplain, buried Pediplain and structural hill, which were observed both in the Close pet basalt and, sandstone and shale The combination of various techniques and the data sets would be utilized for targeting feasible potential zone of ground water. The data sets include geology (lithology) map, lineament map, drainage map, soil map, slope map, geomorphology map. Above all the hydrogeomorphological map of the area must be prepared to arrive at the firm conclusions. On the basis of relative importance, data sets have been chosen for different information layers and best suitable condition is derived.

1.2 Basic concept of watershed A systematic delineation of river basins was first attempted in 1949 by Central Water and Power Corporation (CWPC) under the leadership of Dr.A.N.Khosla, in which entire country has been distinctly delineated into 6 Water Resources Regions (Watershed Atlas of India, AISLUS, 1990). The 6 Water Resources Regions has been further divided into 66 major river catchments. These delineations have been extensively used in the planning and development of surface water resources of the country.

“Ground Water Potential Modeling”

3

Chapter-1 Hydrologically, a watershed could be defined as an area from which the runoff drains through a particular point on the drainage system. Obviously the size of the watershed may vary from a few square meters to thousands of square km. Watersheds can be large or small. Every stream, tributary, or river has an associated watershed, and small watersheds join to become larger watersheds. It is relatively easy to delineate watersheds using a topographic map that shows stream channels. Watershed boundaries follow major ridgelines; bench mark and triangle point around channels and meet at the bottom, where water flows out of the watershed, a point commonly referred to as a stream or river. The connectivity of the stream system is the primary reason for doing aquatic assessments at the watershed level. Connectivity refers to the physical connection between tributaries and the river, between surface water and groundwater, and between wetlands and water. Watershed the channel flow becomes very pronounced and peak flows are influenced by channel characteristics. The analysis, protection, development, operation or maintenance of the land, vegetation and water resources of a drainage basin for the conservation of all its resources and for the benefit of its residents is the activities under watershed management.

1.3 Drainage Morphometry Morphometric studies involve evaluation of streams through the measurement of various stream properties. analysis of various drainage parameters namely ordering of the various streams and measurement of area of basin, perimeter of basin, length of drainage channels, drainage density (Dd), drainage frequency, bifurcation ratio (Rb) and texture ratio (T) (Kumar et al., 2000). GIS and image processing techniques have been adopted for the identification of morphological features and analyzing their properties of the Karawan watershed, Sagar district, Madhya Pradesh. River basins comprise a distinct morphologic region and have special relevance to drainage pattern and geomorphology .Horton’s law of stream lengths suggests a geometric relationship between the number of stream segments in successive stream orders and landforms (Horton, 1945). Quantitative description of the basin morphometry also requires the characterization of linear and areal features, gradient of channel network and contributing ground slopes of the drainage basin. Detailed analysis of drainage parameters is of great help in understanding the influence of drainage morphometry on landforms and their characteristics. As compared to the conventional morphometry studies, using Remote Sensing enables extant ground reality inputs to assess changes in drainage patterns, density soil characteristics, and landforms present in real time. In this context High Spatial Resolution Indian Remote Sensing Satellite Linear Image Self Scanning (LISS) 3 sensor data of 2011-12, Survey of India (SOI) topographical sheets (1:50,000 scale) and field verification data were used for systematic analysis of various Geomorphic processes, hydrological and landform characteristics of the study area for identifying groundwater potential zone. Drainage “Ground Water Potential Modeling”

4

Chapter-1 Network analysis was carried out at basin level using Spatial Analysis GIS System (ArcGIS. 10) to identify the influence of drainage morphometry on landforms and drainage characteristics.

1.4 Role of Remote Sensing in Groundwater Study A formal and comprehensive definition of applied remote sensing, as given by National Aeronautics and Space Administration (NASA) is as follow: “The acquisition and measurement of data/information on some properties of a phenomenon, object, or material by a recording device not in physical, intimate contact with the features under surveillance; techniques involve amassing knowledge pertinent to environment by measuring force fields, electromagnetic radiation, or acoustic energy employing cameras, radiometers and scanners, lasers, radio frequency receivers, radar system, sonar, thermal devices, seismographs, magnetometers, gravimeters, scintillometers, and other instruments.” In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is exemplified by the use of imaging systems where the following seven elements are involved. Note, however that remote sensing also involves the sensing of emitted energy and the use of nonimaging sensors. Remote Sensing processes are a follows; 1.

nergy Source or Illumination; The first requirement for remote sensing is to have an E energy source which illuminates or provides electromagnetic energy to the target of interest.

2. Radiation and the Atmosphere; As the energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor. 3. Interaction with the Target; Once the energy makes its way to the target through the atmosphere; it interacts with the target depending on the properties of both the target and the radiation. 4. Recording of Energy by the Sensor; After the energy has been scattered by, or emitted from the target, we require a sensor (remote - not in contact with the target) to collect and record the electromagnetic radiation. 5. Transmission, Reception, and Processing; The energy recorded by the sensor has to be transmitted, often in electronic form, to a receiving and processing station where the data are processed into an image (hardcopy and/or digital). 6. Interpretation and Analysis; The processed image is interpreted, visually and/or digitally or electronically, to extract information about the target which was illuminated. “Ground Water Potential Modeling”

5

Chapter-1

7.

pplication; The final element of the remote sensing process is achieved when we apply A the information we have been able to extract from the imagery about the target in order to better understand it, reveal some new information, or assist in solving a particular problem.

Spectral properties (Signature) of earth materials- Any remotely sensed parameter, which directly or indirectly characterized the nature and /or condition of the object under observation, is defined as its signature. We actually used the spectral signature of the object in remote sensing. This can be defined as a unique pattern of wavelengths radiated by an object. These can be categorized as;

A)

Spectral Variation: Variation of reflectivity and emissivity as a function of wavelength.

B)

Spatial Variation: Variation of reflectivity and emissivity with spatial position (i. e. shape, texture and size of the object).

C)

Temporal Variation: Variation of emissivity like that in diurnal and seasonal cycle.

D)

Polarization Variation: Are introducing by the material in the radiation reflected or emitted by it.

Each of these four features of Electro Magnetic Radiation (EMR) May be interdependent i.e. shape may be different at different times, or in different spectral bans. A measure of these variations and correlating them with the known features of an object provide signature of the object concerned. The knowledge of the state of polarization of the reflected radiation in addition to spectral signatures of the various objects in remote sensing adds another dimension for analysis and interpretation of remote sensing data. These parameters are extremely useful in providing valuable data (Image) for discriminating the objects. In the present study IRS LISS-3 (dated Feb. 2012 & Oct. 2011) geo-coded digital images were visually interpreted to generate various thematic maps, lineament map, lithological, geomorphological map etc. Image Interpretation; The ‘art’ of image interpretation is extracting information of hydrogeological relevance from images that depict the terrain. Interpretation focuses in fact, on two interrelated aspects: 1.

The (hydro) geomorphological & (hydro) geological subsurface configuration.

2.

Surface features which influence recharge and show evidence of groundwater outflow.


6

Chapter-1 Although interpretation work is nowadays executed on the computer screen, it should be taken into consideration that large-scale hardcopy images with a transparent overlay offer the advantage of screening the whole image during the interpretation process, allowing the interpretation to be cross-checked over the whole scene. The interpretation can then be transferred to a computer file, either manually or by scanning the overlay (Bannert, 1980). Delineation and identification of various objects from satellite images is also an important task. Satellite images can be interpretated by two methods i.e. visual and digital. In digital image processing, classification is done on the basis of spectral values whereas in visual interpretation keys or photo-interpretation elements are be used. The important elements of visual image interpretation are tone (colour), texture, pattern, association, size, shape, location etc. Geomorphological interpretation is of importance in hydrogeomorphological studies, especially for recharge, because of two reasons; •• Landforms are associated with soils, superficial deposits and denudational history affecting the nature and hydrogeomorphological properties of the near surface materials, termed ‘overburden’ in engineering. •• The proportion of rainfall available for recharge of groundwater depends not only on the permeability of soils and rocks, but also on the residence time of rainwater over groundwater intake areas. Shallow soils or soils with swelling clays (vertisols or ‘black cotton soils’), integrated drainage networks with high density, sloping areas and so on, cause rapid runoff, leaving relatively little water for recharge. Opposite conditions, whereby much water is retained, are, at least potentially, more conducive to recharge. Denudational processes (i.e. weathering and erosion) influence hydrogeomorphology in the sense that recharge depends on kind of soil, type and relative intensity of runoff, while the weathered zone forms a shallow aquifer. Alluvial deposits may be present near rivers or ephemeral channels. Local base levels of erosion and even minor block faulting could have influenced the denudational history. Typical of much of Deccan Trap terrain of low relief is the presence of pediments or pediplains. In the case of large contiguous pediments, it is likely that the weathered zone is fairly thick, because otherwise traces of subsurface massive basalt may be observed. Interruptions in the pediment surface in the form of a strip that follows more or less the contour could indicate presence of massive basalt, and more permeable vesicular basalt is likely to be found on top (i.e. the pediment just upslope of the strip, as well as below it).


7

Chapter-1 Faults and fractures are linear structural elements. In hard rock terrain it is difficult to judge movement along a fault or the type of fracture (or master joints) and their hydraulic properties by image interpretation alone. Linear elements on an image of supposed association with large joints, fractures or faults are termed neutrally ‘lineaments’.

1.5 Importance of Geographic Information System (GIS) for Groundwater Zonation A geographic information system (GIS) is a computer-based tool for mapping and analyzing spatial data. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other information systems and make it valuable to a wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies. GIS is considered to be one of the most important new technologies, with the potential to revolutionize many aspects of society through increased ability to make decisions and solve problems. The major challenges that we face in the world today -- overpopulation, pollution, deforestation, decrease of groundwater, natural disasters – all have a critical geographic dimension. Local problems also have a geographic component that can be visualized using GIS technology, whether finding the best soil for growing crops, determining the home range for an endangered species, or discovering the best way to dispose of hazardous waste. Careful analysis of spatial data using GIS can give insight into these problems and suggest ways in which they can be addressed.

Components of a Geographic Information System;

A working Geographic Information System seamlessly integrates five key components: hardware, software, data, people, and methods.

HARDWARE - Hardware includes the computer on which a GIS operates the monitor on which results are displayed, and a printer for making hard copies of the results. Today, GIS software runs on a wide range of hardware types, from centralized computer. The data files used in GIS are relatively large, so the computer must have a fast processing speed and a large hard drive capable of saving many files. Because a GIS outputs visual results, a large, high-resolution monitor and a high-quality printer are recommended.

SOFTWARE- GIS software provides the functions and tools needed to store, analyze, and display geographic information. Key software components include tools for the input and manipulation of geographic information, a database management system (DBMS), tools that support geographic query, analysis, and visualization, and a graphical user interface (GUI) for easy access to tools. ArcGIS 10 software is use in this study for groundwater zone identification. “Ground Water Potential Modeling”

8

Chapter-1

DATA - Possibly the most important component of a GIS is the data. A GIS will integrate spatial data with other data resources and can even use a database management system, used by most organizations to organize and maintain their data, to manage spatial data. There are three ways to obtain the data to be used in a GIS. Geographic data and related tabular data can be collected in-house or produced by digitizing images from aerial photographs or published maps. Data can also be purchased from commercial data provider. Finally, data can be obtained from the federal government at no cost. In this case study IRS LISS-3 satellite images are use for mapping, Indian Meteorological Department (IMD) data in tabular form are use for climate (temperature, humidity and precipitation) study of Karawan watershed , and reference maps and data are use in this study such as, Survey of India (SOI) toposheets scale on 1:50,000 and Geological Survey of India (GSI) maps.

PEOPLE - GIS users range from technical specialists who design and maintain the system to those who use it to help them perform their everyday work. The basic techniques of GIS are simple enough to master that even students in elementary schools are learning to use GIS. Because the technology is used in so many ways, experienced GIS users have a tremendous advantage in today’s job market.

METHODS -A successful GIS operates according to a well-designed plan and business rules, which are the models and operating practices unique to each organization. The aim of a Geographic Information System (GIS) is to store geographically reference data as different layers. These different data / layers may be manipulated and visually access as output data. The usefulness of output data however is determined by the quality of the input data. The information obtained was used to identify areas with constraints regarding water demand. It was also suggested that the following input data sets were necessary in a GIS for use during modeling; : Topographical map – Scale 1:50,000 : Geological map – Scale 1:250,000 : Soil map – Scale 1:250,000 / 1:50,000 : Satellite Imagery : Climatology and daily rainfall data A GIS serves as a tool in various study. According to croukamp (1996) an integrated GIS is an ideal tool for management of geotechnical data and other related data through its unique ability to store the spatial relationship of all information on a site and the spatial relationship with similar “Ground Water Potential Modeling”

9

Chapter-1 information at different site. In this study used a GIS for groundwater potential zonation the following as input data; : Geology (Lithology) : Structural Geology (Lineament) : Geomorphology (Landforms) : Drainage Density : Slope : Soil Texture : Land use / Land Cover It has also used that remotely sensed data, climatic and hydrological data used successfully in a GIS.

1.6 Geoprocessing Geoprocessing is for everyone that uses ArcGIS. Whether you’re a new user or an old pro, geoprocessing will become an essential part of your day-to-day work with ArcGIS. The fundamental purpose of geoprocessing is to allow you to automate your GIS tasks. Almost all uses of GIS involve the repetition of work, and this creates the need for methods to automate, document, and share multiple-step procedures known as workflows. Geoprocessing supports the automation of workflows by providing a rich set of tools and a mechanism to combine a series of tools in a sequence of operations using models and scripts. The kinds of tasks to be automated can be mundane—for example, to wrangle herds of data from one format to another. Or the tasks can be quite creative, using a sequence of operations to model and analyze complex spatial relationships—for example, calculating optimum paths through a transportation network, predicting the path of wildfire, analyzing and finding patterns in crime locations, predicting which areas are prone to landslides, or predicting flooding effects of a storm event. Geoprocessing is based on a framework of data transformation. A typical geoprocessing tool performs an operation on an ArcGIS dataset (such as a feature class, raster, or table) and produces a new dataset as the result of the tool. “Ground Water Potential Modeling” 10

Chapter-1 Each geoprocessing tool performs a small yet essential operation on geographic data, such as projecting a dataset from one map projection to another, adding a field to a table, or creating a buffer zone around features. ArcGIS includes hundreds of such geoprocessing tools. Geoprocessing allows you to chain together sequences of tools, feeding the output of one tool into another. You can use this ability to compose an infinite of geoprocessing models (tool sequences) that help you automate your work and solve complex problems.

1.6.1 Geoprocessing models and ModelBuilder The tool dialog box and command line allow you to execute a single tool. You can think of this as executing a single instruction in a programming language. While single tool execution is certainly practical, the system wouldn’t be very useful unless you could string together multiple tools, feeding the output of one into another, just like a programming language. In the geoprocessing framework, the ModelBuilder window is how you quickly and easily turn your ideas into software by chaining together elements of the geoprocessing language (the tools) into a sequence. It’s important to realize that models are software since they instruct the computer to do something. The programming language is visual—what you see in ModelBuilder—rather than text-based like a traditional programming language. The most important thing to note here is that models are tools. They behave exactly like all other tools in the toolbox. You can execute them in the dialog box window or at the command line. Since models are tools, you can embed models within models. In fact, several of the system tools provided with ArcGIS are models. Models can be as complex as you dare. You can use any number of the tools in ArcToolbox, including other models you’ve written (since models are just tools) and tools based on scripts. You can use loops and conditions to control the logical flow of a model. Models can be extremely simple and still be productive. You can create a model that contains a single tool, but embeds some of its parameters. For example, the Buffer tool takes seven parameters, but for your current set of tasks, you know that three of these parameters will always be the same. Rather than filling out these parameters each time you execute the Buffer tool, you can quickly create a model and set these three parameters, save it as the My Buffer tool, and use its dialog box rather than the Buffer dialog box. You might only use My Buffer a few times before deleting it, but it’s no loss because it was quick and easy to create and productive for you to do so.


11

Chapter-1

1.6.2 Scripting You can also use a scripting language to create new, useful software. A program that uses a scripting language is called a script. In the world of software programming, languages can be divided into two basic categories: system languages and scripting languages. System languages are things such as C++, Python and Java that are used to create applications from scratch, using low-level primitives and the raw resources of the computer. Scripting languages, such as Python and Perl, are used to glue applications together, using built-in higher level functions of the computer and masking the nuts and bolts a system language programmer must deal with. Compared to system languages, scripting languages are easier to learn and use—a basic understanding of programming is all that’s needed to be productive. In the geoprocessing framework, scripts are analogous to models in that they can be used to create new tools. Models are created with a visual programming language (ModelBuilder) and scripts are created with a text-based language and text editors. Just like models, scripts are tools. You can introduce a script to ArcToolbox using a step-by-step wizard, and it becomes just another tool that you can use in a model or in another script. Several of the system-provided tools are scripts. Technically, you can write a script and not introduce it to ArcToolbox, in which case it’s not a tool, but just a script on disk.


12

CHAPTER- 2

PHYSIOGRAPHY & WATERSHED CODIFICATION SYSTEM

CHAPTER- 2 PHYSIOGRAPHY & WATERSHED CODIFICATION SYSTEM 2.1 Location and Extension

T

he watershed area of Karawan river is 275.61 Sq Kms (figure:1) & located between 23°44’45”N to 23°58’30”N latitude and 78°35’45”E to 78°46’15”E longitudes. The Karawan River originates from the southwest part of the Sagar town located at about 620 meter near the Gond village (23°46’30” N latitude & 74°40’30” E longitudes). Karawan river is 32.5 Kms long, however there is no main tributaries of the Karawan river, there are some small tributaries pouring into the river, notable amongst there are Molali Nala, Garhpahara Nala on the right bank and no any major tributaries on the left bank. The Karawan River flows towards to northeast and meets the river Dhasan near Mehar village in Sagar district; Dhasan River is an important right bank tributary of the Yamuna River. The study area falls in Survey of India (1:50,000) toposheets No. 55I/9, 55I/10 and 55I/13. The Karawan Basin has got good accessibility by road. The state highway no. 15 and National highway no. 34, 26 Passes through the watershed. Sagar town which is the district headquarter, is situated in the east-east-south of the watershed. The nearest railway station is sagar which is one of the main broad gauge electric lines. Apart from state highway the study region has also a good road network of district and village roads connecting the different areas of watershed with each other.

2.2

Climate Condition

2.2.1 Rainfall

Study area has comes in sagar district it has a humid subtropical climate. Like most of north India, it has a hot dry summer (April–June) followed by monsoon rains (July–September) and a cool and relatively dry winter. The average rainfall is about 1304.7mm. In the study area, there are sagar meteorological stations, which are located in 23°49’46”N 78°44’42”E 50 years of rainfall data was collected from the National Meteorological Agency for the stations from 1941 to 1990. Summary of the mean rainfall (mm/month) data is given in the table 2.1 below. From the rainfall data of the stations short rainy seasons are observed in the study area. The rainy period lasts from June to September (figure 2.2), which is torrential in August. The main cause of the rainfall in this region is the southwest summer monsoon is attracted to India by a low pressure area that’s caused by the extreme heat of the Thar Desert and adjoining areas, during summer. “Ground Water Potential Modeling”

14

Chapter-2

LOCATION MAP

Figure 2.1: Location Map of Karawan Watershed.


15

Chapter-2

Month Average Rainfall (mm) Jab Feb Mar April May June July Aug Sep Oct Nov Dec Total

27.0 10.8 9.7 1.8 9.6 134.0 400.8 448.6 193.9 38.2 19.2 11.1 1304.7

Rainy Days 1.9 1.1 0.9 0.3 0.7 7.4 15.3 16.9 9.0 2.0 0.8 0.9 57.2

Table 2.1 Summary of mean rainfall data of sagar station, year 1941-1990

Figure 2.2: Average rainfall of the Sagar Rain Gauge Station (1941-1990)


16

Chapter-2

2.2.2 Temperature and Humidity

The temperature of the study area is typical of tropical monsoon lands. In most cases the mean monthly temperature exceeds 24.82°C. The monthly average minimum temperature registered is 17.56°C in the month of January and the monthly maximum temperature registered is 32.89°C in the month of May. The mean monthly humidity exceeds 54.46%. The monthly average minimum humidity registered is 25.8% in the month of April and the monthly maximum humidity registered is 82.45% in the month of August (table 2.2). Month Jab Feb Mar April May June July Aug Sep Oct Nov Dec MEAN

TEMPERATURE Maximum Minimum 24.41 10.71 27.36 12.89 32.67 17.50 37.69 21.85 40.72 25.07 37.34 23.88 30.31 28.58 30.03 31.47 28.43 25.30 31.19

21.97 21.29 20.85 18.60 15.04 11.91 18.46

Mean 17.56 20.12 25.08 29.77 32.89 30.61 26.14 24.93 25.44 25.03 21.73 18.6 24.82

HUMIDITY Morning Evening 72.66 45.22 63.82 35.76 46.51 24.93 32.89 18.71 32.66 19.78 55.11 43.75 81.88 86.45 82.22 70.32 63.61 70.14 63.19

73.16 78.46 70.81 50.09 42.04 46.27 45.74

Mean 58.94 49.79 35.72 25.8 26.22 49.43 77.52 82.45 76.51 60.2 52.82 58.20 54.46

Table 2.2 Average monthly temperature (°C) and Humidity (%) of sagar station, period (1941-1990)

2.3 Watershed Delineation and Codification India is drained by many rivers and their tributaries where flood and droughts are frequent visitors. To combat these situations and to sustain agricultural production, the country’s land mass needs to be divided into smaller hydrological units that led to conceptualization of “Delineation and Codification of Watersheds in India”. The state of art of the delineation and codification system is that it is an open system and the process can be extended to micro level using the drainage map of larger scale .The Watershed Atlas meets up the requirement of planning at National and State level. One of the unique characters of the atlas is that it provides the basic framework of watersheds of the country and all the land based developmental programme of the country can be recognized with national code of watershed.


17

Chapter-2 The delineation has done in seven stages starting with Water Resource Regions and their subsequent division and subdivisions into Basins, Catchments, Sub-catchments, Watershed, Sub watershed and Micro watersheds in decreasing size of the delineated hydrologic unit. Watershed units are showing in table 2.3. Each of the sub-catchment then divided into watersheds following the lower order streams, a group of tributaries or the left and right bank of higher order stream moving downstream upwards using the same base. When the left and right banks of a higher order stream are being delineated as separate watershed it would be advisable to cut across the stream at the upper end at a suitable confluence point to avoid the ambiguity in locating the ridge. The number of watersheds in a sub-catchment is restricted up to 9. The size of individual sub-watersheds is generally restricted around 5,000 to 9,000 hectares, which is considered a viable working area for implemental programmes. The division of watershed is restricted to 22 alphabets (avoiding a, e, i, l and o). Karawan watershed area is 2C2C5.

1. Water Resource Regions (WRR); The six WRRs suggested by Dr. A. N. Khosla in 1949 have been adopted as such with slight modifications in their numbering which has been done in a clockwise manner, starting with Indus drainage as numbers like 1, 2, 3 etc.

Indus drainage – 1

Ganges drainage – 2

Brahmaputra drainage – 3

All drainage flowing into the Bay of Bengal except those at 2 & 3 – 4

All drainage flowing into the Arabian Sea except Indus drainage – 5

Western Rajasthan mostly ephemeral drainage – 6

2. Basins; Each WRR has been divided into basins which constitute individual big rivers like Krishna, Narmada, Chambal etc. or a combination of smaller ones which are contiguous to each other. Basins are assigned letters as A, B, C………..Z.

3. Catchments; Each basin has been divided into a number of catchments, which pertain to main tributaries or a group of contiguous tributaries or individual streams. Catchments are represented by numerals suffixed to basin code as 1, 2, 3…….9. “Ground Water Potential Modeling”

18

Chapter-2

4. Sub-catchments; The catchments are further divided into a number of sub-catchments, which are mainly smaller tributaries and streams. Sub-catchments are indicated by suffixing alphabets to catchment code as A, B, C………Z.

5. Watersheds; Each sub-catchments has been divided into a number of watershed which are the smallest sized hydrologic units in the macro level category on the base of 1:1 million scale in the Watershed Atlas of India, published by SLUSI. Watersheds are designated by suffixing Arabic numbers to a code of sub-catchment.

6. Sub-watersheds; Each watershed is further divided into sub-watersheds on 1:50000 scale (SOI topographical map) in which main tributaries and streams are taken up for delineation of sub-watersheds. Sub-watersheds designated by small English alphabets as a, b, c………z which is suffixed to watershed code. The small alphabet ‘e’ ‘i’ ‘l’ and ‘o’ are discarded in view of the cartographic consideration and to avoid its ambiguity with the sequence of code. Hence, the total numbers of codes for sub-watersheds is restricted to 22.

7. Micro Level Delineation; Planning to phase out the watershed management at catchment level and to formulate action programme needs, micro level delineation. The delineation of watershed boundary at micro level could easily be attained by superimposing the watershed boundary from Watershed Atlas on to a drainage map of 1:50000 scale. The delineation and codification would follow the similar system based on stream hierarchy and codification from downstream upward that allows getting a micro-watershed of 500 to 1,500 ha size viable enough for implementation of soil and water conservation programmes. Case study area (Karawan watershed) have 27,561 hectare area and it’s comes in watershed unite. Morphometric analysis of a watershed provides a quantitative description of the drainage system which is an important aspect of the characterization of watersheds (Strahler, 1964). Morphometric analysis requires measurement of linear features, areal aspects, gradient of channel network and contributing ground slopes of the drainage basin (Nautiyal, 1994). The remote sensing technique is a convenient method for morphometric analysis as the satellite images provide a synoptic view of a large area and is very useful in the analysis of drainage basin morphometry. The morphometric characteristics at the watershed scale may contain important information regarding its formation and development because all hydrologic and geomorphic processes occur within the watershed. The quantitative analysis of morphometric parameters is found to be of immense utility in river basin evaluation, watershed prioritization for groundwater conservation and natural resources management at watershed level.


19

Chapter-2 Morphometric analysis of a watershed provides a quantitative description of the drainage system and helpful for identifying groundwater potential zones of Karawan watershed. Which is an important aspect of the characterization of watersheds (Strahler, 1964)? Geographical Information System (GIS) techniques are now a day’s used for assessing various terrain and morphometric parameters of the watersheds, as they provide a flexible environment and a powerful tool for the manipulation and analysis of spatial information. In the present study stream number, order, frequency, density and bifurcation ratio are derived and tabulated on the basis of physical properties of watershed using GIS based on drainage lines as represented over the topographical maps (scale 1:50,000). Drainage Area in Hectares >1 Lakh 40,000 – 1 Lakh 4,000 – 40,000 2,000 – 4,000 400 – 2,000 Utilities > Layer Stack.

2

Click on the Input File folder icon, and select the file to add first. Be sure the “Files of Type” is set to the same input type as your data source. The output image will be contain the bands in the order that they have been input, so the first band that you add will be band 1, the second will be band 2, etc.

a.

Click the Add button to add the band to the layer stack.

b.

Click the folder icon again to select the next layer to add, and then click the Add button to add the next layer. Repeat this process until all of the layers you would like to include have been added to the stack.

c.

Click the Output File folder icon to set the name and directory of the new multi-band output image. Use the TIFF files of type for the greatest versatility among different software.

d.

Use the Union output option, and choose to ignore Zero in Stats (this will produce a brighter image). Click OK to run the process.

The Resulting output image will be a multi-band geotif that can be used in Erdas Imagine, ArcMap, etc. Co-Ordinate Transformation: The techniques of co-ordinate transformation are useful for geometric correction with GCPs. Image-to-ground and image-to image both corrections involve rearrangement of the input pixels on to a new grid. Polynomial equations are used to convert the source coordinates to rectified coordinate. Using a polynomial transformation, the relationship between the pixel coordinate system can be defined. Rectification: After calculating the transformation, the images were rectified in the map coordinate systems in ERDAS imagine 2011 software. Resampling is the final step of geometric correction. Different resampling methods can be used in the rectification methods.


24

Chapter-3 The three most common Resampling methods are nearest neighbor assignment, bilinear interpolation, and cubic convolution. After the coordinate transformation, pixels are pointed to new locations. The nearest neighbor algorithm was used in this study because that is simply assigns to each pixel the value of its nearest neighbor in the new coordinate system. It is the fastest resampling technique and is appropriate for thematic data. Rectifying or registering image data on ERDAS imagine 2011 software involves the following general steps, regardless of the application: 1. Locate GCPs. 2. Compute and test a transformation. 3. Create an output image file with the new coordinate information in the header. The pixels must be resampled to conform to the new grid. Images can be rectified on the display (in a Viewer).

Miscellaneous Pre-Processing: 1). Subsetting: Subsetting or spatial subsetting refers to breaking out a portion of a large file into one or more smaller files. Often, image file contain areas much larger than a particular study area. In these cases, it is helpful to reduce the size of the image file to include only the area of interest (AOI). 2). Mosaicking: On the other hand, the study area in which we are interested may span several image file (or scenes). In this case, it is necessary to combine the images and toposheets to create one large file (the mean of spatial extent). This is called Mosaicking. In this study Mosaicking have done on several toposheets (55 I/9, I/10, I/13). Image Enhancement: Image enhancement can be defines as the conversion of the image quality to a better and more under stable level for feature extraction or image interpretation.

Image Classification: The intent of the classification process is to categorize all pixels in a

digital image into one of several land cover classes, or “themes”. This categorized data may then be used to produce thematic maps of the land cover present in an image. Normally, multispectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization (Lillesand and Kiefer, 1994). The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground. Two main classification methods are Supervised Classification and Unsupervised Classification. Supervised classification mode - using training signature Unsupervised classification mode - image clustering and cluster groupings. In this study the supervised classification have been adopted to prepare land use land cover map of the karawan watershed

3.5.2 Post-classification Process Post-classification filtering of image data is used to remove any unwanted noise from LU/LC thematic dataset. Filtering generalizes the dataset by removing stray pixels in the image and producing more homogenous class area. “Ground Water Potential Modeling”

25

Figure 3.1: Flow chart showing data and methods employed for the study. “Ground Water Potential Modeling”

26

Chapter-3

3.6 Integration with GIS After preprocessing, combining data of the different type and from different sources (SOI, GSI and DEM), is the pinnacle of data integration and analysis. In a digital environment, where all the data sources are geometrically registered to a common geographic base, the potential for information extraction is extremely wide. The integration with GIS allows a synergistic processing of multisource spatial data. The integration of the two technologies creates a synergy in which the GIS improves the ability to extract information from remotely sensed data, and remote sensing in turn keeps the GIS up-todate with actual environment information. As a result, large amount of spatial data can now be integrated and analyses. This is allowing for better understanding of environmental process and better insight into the effect of human activities.

3.6.1 Process of GIS Process of GIS starting from data capture to organizing data for spatial analysis. Since data analysis and presentation is a versatile process and requires details discussion. These following steps are using in GIS processing in this case study; •

Data capture: Data capture from maps, images or field surveys.

•

Data sources: The data sources for GIS can be categorized as follows;

•

-

Conventional analog map sources (SOI Toposheets, GSI DRM data)

-

Report and publications

-

Satellite remote sensing (IRS P-6 LISS III satellite image)

-

Field data sources (Surveying and global positioning system (GPS))

-

Existing digital map sources.

Digitizing; In the early days of digital cartography, the most common way of encoding vector data from analog maps/ images into a GIS was digitizing.

In this case study digitizing has done for all datasets layers used the ArcGIS 10 software. •

Vector to Raster conversion; Rasterization is also useful to integrate all datasets for overlay operation.

•

•

Organizing data for analysis; Most ArcGIS software organizes spatial data in a thematic approach that categorized data in vertical layers. The definition of layers is fully dependent on the organization’s requirements. In this case study, typical layers used in groundwater zonation and management agencies include LU/LC, lithology, geomorphology, lineament, slope, drainage density, and soil texture. Integration and modeling of spatial data (It has been described in chapter 6).

•

Spatial analysis (It has been described in chapter 6). “Ground Water Potential Modeling”

27

CHAPTER- 4

MORPHOMETRIC ANALYSIS

CHAPTER-4 MORPHOMETRIC ANALYSIS “The measurement and mathematical analysis of the configuration of the earth’s surface and of the shape and dimensions of its landform provides the basis of the investigation of maps for a geomorphological survey. This approach has recently been termed as Morphometry”.

T

he area, altitude, volume, slope, profile and texture of landforms comprise principal parameters of investigation. Applied various methods for landform analysis, which could be classified in different ways and their results presented in the form of graphs, maps or statistical indices.

The chapter described the morphometric parameters of Karawan watershed. The morphometric analysis of the Karawan watershed was carried out on the Survey of India topographical maps No. 55I/09, 55I/10 and 55I/13 on the scale 1:50,000 and DEM. The lengths of the streams, areas of the watershed were measured by using ArcGIS-10 software, and stream ordering has been generated using Strahler (1953) system.

4.1 Drainage network

4.1.1 Stream Order (u) & Number of Stream (Nu)

In the drainage basin analysis the first step is to determine the stream orders. In the present study, the channel segment of the drainage basin has been ranked according to Strahler’s stream ordering system. According to Strahler (1964), the smallest fingertip tributaries are designated as order 1. Where two first order channels join, a channel segment of order 2 is formed; Where two of order 2 joins, a segment of order 3 is formed; And so forth. The trunk stream through which all discharge of water and sediment passes is therefore the stream segment of highest order. The study area is a 6th order drainage basin (figure m4.1). The total number of 563 streams were identified of which 409 are 1st order streams, 112 are 2nd order, 31 are 3rd order, 8 in 4th order, 2 in fifth and one is indicating 6th order streams.

4.1.2 Stream Length (Lu), Average Length of Stream & Stream Length Ratio;

Stream length is one of the most significant hydrological features of the basin as it reveals surface runoff characteristics streams of relatively smaller lengths are characteristics of areas with larger slopes and finer textures. Longer lengths of streams are generally indicative of flatter gradients. Generally, the total length of stream segments is maximum in first order streams and decreases “Ground Water Potential Modeling”

29

Chapter-4 as the stream order increases. The numbers of streams of various orders in the basin are counted and their lengths from mouth to drainage divide are measured with the help of GIS software. This indicates the homogenous rock material subjected to weathering erosion characteristics of the basin. Deviation from its general behavior indicates that the terrain is characterized by variation in lithology and topography. Average Length of Stream & Stream Length Ratio Calculate by these formulas; _ Lµ = ΣLµ / Nµ _ _ RL = Lµ / Lµ - 1 Here, _ _ Lµ = Average Length of Stream, Lµ - 1 = Average Length of next Order, ΣLµ = Total Length of any given Order, Nµ = Total number of any given Order, RL = Stream Length Ratio.

4.1.3 Bifurcation Ratio (Rb)

The term bifurcation ratio (Rb) is used to express the ratio of the number of streams of any given order to the number of streams in next higher order. Bifurcation ratios characteristically range between 3.0 and 5.0 for basins in which the geologic structures do not distort the drainage pattern (Strahler, 1964). Strahler (1957) demonstrated that bifurcation ratio shows a small range of variation for different regions or for different environment dominates. The mean bifurcation ratio value is 2.85 for the study area (table 4.2) which indicates that the geological structures are less disturbing the drainage pattern. Bifurcation ratio calculates by following formula; Rb = Nu / Nµ + 1

Here, Rb = Bifurcation ratio, Nu = Total No. of given Order.

4.1.4 Length of Main Channel (Cl)

This is the length along the longest watercourse from the outflow point of designated sun watershed to the upper limit to the watershed boundary. Author has computed the main channel length by using ArcGIS-10 software, which is 32.5 Km.


30

Chapter-4

Figure 4.1: Drainage Network & Order of Karawan Watershed


31

Chapter-4

4.2 Basin Geometry

4.2.1 Length of the Basin (Lb)

Several people defined basin length in different ways, such as Schumm (1956) defined the basin length as the longest dimension of the basin parallel to the principal drainage line. Gregory and Walling (1973) defined the basin length as the longest in the basin in which are end being the mouth. Gardiner (1975) defined the basin length as the length of the line from a basin mouth to a point on the perimeter equidistant from the basin mouth in either direction around the perimeter. The author has determined length of the Karawan watershed in accordance with the definition of Schumm (1956) that is 25.6 Km.

4.2.2 Basin Area (A)

The area of the watershed is another important parameter like the length of the stream drainage. Schumm (1956) established an interesting relation between the total watershed areas and the total stream lengths, which are supported by the contributing areas. The author has computed the basin area by using ArcGIS-10 software, which is 275.61 Sq Km.

4.2.3 Basin Perimeter (P)

Basin perimeter is the outer boundary of the watershed that enclosed its area. It is measured along the divides between watersheds and may be used as an indicator of watershed size and shape. The author has computed the basin perimeter by using ArcGIS-10 software, which is 83 Km.

4.2.4 Form Factor (Ff)

According to Horton (1932), form factor may be defined as the ratio of basin area to square of the basin length. The value of form factor would always be less than 0.754 (for a perfectly circular watershed). Smaller the value of form factor, more elongated will be the watershed. The watershed with high form factors have high peak flows of shorter duration, whereas elongated watershed with low form factor ranges from 0.42 indicating them to be elongated in shape and flow for longer duration. Form Factor (Ff) = A / Lb2

Here, A = Basin area, Lb2 = Square of the basin length.


32

Chapter-4

River Basin

Stream Order (u)

Number of Order (Nu

Length of Stream (Lu) In meter

Average Length Of Stream (Lµ) In meter

Stream Length Ratio (RL)

Karawan Watershed 2C2C5

1 2 3 4 5 6

409 112 31 8 2 1 ΣNu=563

241617.8 96742.2 75726.8 43554.4 27562.8 3739.9 ΣLu=488944.17

590.75 863.76 2442.8 5444.3 13781.3 3739.9

0.68 0.35 0.49 0.39 3.68 -

Table 4.1: Linear aspects of the drainage network of the Karawan Basin Stream Order (u) Number Of Stream (Nu) Bifurcation Ratio (Rb)

1

2

3

4

5

6

409

112

31

8

2

1

-

3.6

3.6

3.9

4

2

Mean Bifurcation Ratio (Rb) =2.85

Table 4.2: Bifurcation Ratio

4.3 Drainage Density Drainage density, a fundamental concept in hydro-logic analysis, is defined as the length of drainage per unit area. The term was first introduced by Horton (1932) and is determined by dividing the total length of streams within a drainage basin by the drainage area. A high drainage density reflects a highly dissected drainage basin with a relatively rapid hydro-logic response to rainfall events, while a low drainage density means a poorly drained basin with a slow hydrologic response (Melton, 1957). The length of drainage streams was measured and was divided by the total surface area of the basin. The following map (figure 4.2) shows the drainage density of the Karawan watershed area.

4.4 Texture Ratio (Rt) According to Schumm (1965), texture ratio is an important factor in the drainage morphometric analysis which is depending on the underlying lithology, infiltration capacity and relief aspect of the terrain. The texture ratio is expressed as the ratio between the first order streams and perimeter “Ground Water Potential Modeling”

33

Chapter-4 of the basin (Rt = Nl / P) and it depends on the underlying lithology, infiltration capacity and relief aspects of the terrain. In the present study, the texture ratio of the watershed is 4.93 and categorized as moderate in nature.

4.5 Drainage Texture (Dt) Drainage texture is one of the important concept of geomorphology which means that the relative spacing of drainage lines. Drainage texture is on the underlying lithology, infiltration capacity and relief aspect of the terrain. Dt is total number of stream segments of all orders per perimeter of that area (Horton, 1945). (Smith, 1950) has classified drainage texture into five different textures i.e., very coarse (8). In the present study, the drainage texture of the watershed is 6.78 It indicates that category is fine drainage texture.

4.6 Stream Frequency (Fs) The drainage frequency introduced by Horton (1932) means stream frequency (or channel frequency) Fs as the number of stream segments per unit area. In the present study, the stream frequency of the Karawan watershed is 2.04.

4.7 Drainage System & Pattern The arrangement of streams in a drainage system constitutes the drainage pattern, which in turn reflects mainly Geomorphology, structural/ or lithologic controls of the underlying rocks. The area has moderate to high drainage density. The drainage network shows dendritic to sub-dendritic pattern, Parallel & Sequent streams system. Even though, difference in stream lengths and angle of connection, yet they are in general characterized by a treelike branching system, which is a dendritic drainage pattern that indicates homogenous and uniform soil and rocks. Angular drainage patterns also exist in Karawan Basin. They appear either as one-set or two-sets of angular streams (i.e. with acute angle of stream connection) to indicate the existence of Lineament systems. The drainage network shows dendritic to sub-dendritic pattern.


34

Chapter-4

Figure 4.2: Drainage Density Map of the Karawan Watershed


35

CHAPTER- 5

PHYSICAL PROPERTIES OF KARAWAN WATERSHED

CHAPTER-5 PHYSICAL PROPERTIES OF KARAWAN WATERSHED 5.1 Geology / Lithology

T

he synoptic view and multispectral nature of the satellite imagery help in discrimination and mapping of different lithological unites, Geological mapping is carried out mainly based on visual interpretation of satellite images adopting deductive approach by studying image characteristics and terrain information in conjunction with a prior knowledge of general geological setting of the area. The tone /colour and landform characteristics combined with relative erodibility, drainage, soil type, land use / land cover and other contextual information observable on the satellite image are useful in differentiating different rock groups / types.

In order to understand the Geology / Lithology of the study area, a general lithological map has been prepared with the help of IRS LISS-3 satellite imagery and shown in figure 5.1. Through the general geology / lithology of the area has been mapped by the GSI (Geological Survey of India) in the usually way, various their similar have contributed to diverse geological aspects of the study area. They recorded the principal rock formations namely upper vindhyan supergroup and deccan traps. Near by the Sagar town, comprises of hills of Sub horizontal Vindhyan and Deccan Trap, the former commonly standing up above the level of the latter. The topography of the Vindhyan in the Sagar area is one that is characteristic of Sub horizontal sedimentary rocks, where the highest bed is massive sandstone; the hills are generally flat topped. WSW to NEN direction of the watershed, for some distance around the hills, the Deccan trap has been completely removed, and the original Vindhyan topography can be seen to consist of the steep scarp slopes surrounding the two hills, with a platform between and around the hills. This platform is not neutrally horizontal, sloping gently away flow the sharps, & unlike the horizontal Deccan trap platforms, be appropriately termed a pediment. A further difference between the topography of the two formations in the study area has seen in the shapes of the Contours on the scarp slopes. In the case of the Vindhyan scarps, the contours are fairly smooth, with few indentations but on the Deccan Trap scarps the contours are indented by many little streams which unite lower down to provide a dendrite pattern of drainage. The major lithological formations exposed in the Karawan watershed namely Compact basalt (36.24 Km2) situated on southern part of the watershed area, vesicular basalt (204.99 Km.2) sandstone (52.74 Km2), shale (7.16 Km2) have relatively no water potential because of their massive nature and insignificant primary porosity. Explanation of the lithological characteristics in table 5.1. “Ground Water Potential Modeling”

37

Chapter-5

Figure 5.1: Lithology of Karawan Watershed


38

Chapter-5 Source: GSI District Resource Map Sagar District, Madhya Pradesh

Lithology

Area (Km2)

Stratigraphic Status Group Subgroup Deccan Deccan Trap Trap

Compact Basalt

36.24

Sandstone

52.74

Kaimur

Vindhyan

Shale

7.16

Rewa

Vindhyan

Deccan Trap

Deccan Trap

Vesicular Basalt

204.99

Age (GTS)

Nature & Characteristics

Upper Cretaceous Fine to medium to Paleocene grained, hard volcanic rock Upper to Middle Dark coloured Proterozoic whitish pink, and medium grained rock Upper to Middle Discontinuous Proterozoic outcrops, dark coloured, white to pink coloured, medium to coarse-grained rock. Upper Cretaceous Fine to medium to Paleocene grained, soft volcanic rock

Table 5.1: Explanation of the lithological characteristics of the karawan watershed

5.2 Geomorphology “Geomorphology is the study of landscapes It entails the systematic description of landforms and the analysis of the processes that create them.” Geomorphologists are also concerned with understanding the function landforms and how landforms respond to changes in energy. Because landforms and landscapes result from the combined effects of lithology, structure, and process, geomorphology draws upon nearly all fields of geosciences. The use of remote sensing technology for Geomorphological studies has definitely increased its importance due to the establishment of its direct relationship with allied disciplines, such as, geology, soils, vegetation/Land use & hydrology. The remote sensing technology is ideal for hydrological and hydro-geological studies since terrain does control movement and accumulation of surface and groundwater. Geomorphological mapping involves the identification and characterization of various landforms and structural features. Many of these features are favorable for the occurrence of groundwater and are classified in terms of groundwater potentiality of. Major Geomorphological units found “Ground Water Potential Modeling”

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Figure 5.2: Geomorphology map of Karawan Watershed


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Chapter-5 in the study area are Structural hill, Denudational hill, Residual hill, Pediment and Pediplain/ Buried Pediplain (figure 5.2). Following geomorphic units and their component were identified and mapped. 1)

Residual Hill- Residual hills are resulted from the end product of pediplanation which reduces the original mountains into a series of scattered knolls standing on the Pediplains. These units are considered as poor potential zones, as they have unfractured rock material, low infiltration and behave largely as runoff zone which cover 0.82 km2 area.

2)

Structural Hill (Vindhyan Sediments)- Structural hills (22.52 km2) are the linear or acute hills exhibiting definite trend lines and mostly act as runoff zones.

3)

Pediplain / Pediment (Vindhyan Sediments) - Pediplain / Pediment (69.64 km2) has low relief and surface water remains for considerable time before meeting major rivers. It provides good scope for infiltration and recharge of groundwater. Consequently they pose good potential for groundwater occurrence.

4)

Buried Pediplain (Vindhyan Sediments) - The term buried pediplain (112.49 Km.2) is most generally used to describe a series of coalescening pediments. Depending upon in situ conditions such as rock type, topography, structural features and geomorphic features acted upon them, the development of individual landforms units differ considerably. The buried pediplain have been identifying based on visual interpretation techniques like tone, textures, size, shape, vegetation.

5)

Denudational Hill (Vindhyan Sediments)- A group of massive hills with resistant rock bodies that are formed due to different erosional and weathering (Denudational) processes and occupying in center, southern and north-eastern part of the Karawan watershed are denudational hills (76.45 Km.2). The average elevation of these hill ranges is about 500 to 620 m above msl.

6)

Pediplain / pediment with shale- some Pediplain and peniplain geomorphological features of karawan watershed area have shale lithological formation. Pediplain and peniplain with shale have poor groundwater potential zone comparison to other Pediplain and pediment of karawan watershed.

5.3 Lineament O’Leary and Pohn (1976) tried to bring more clarity to terms and concepts. They defined a lineament as: “A mappable, simple or composite linear feature on the surface, whose parts are “Ground Water Potential Modeling” 41

Chapter-5 aligned in a rectilinear or slightly curvilinear relationship, which differs distinctly from the patterns of adjacent features and which presumably, reflects a subsurface phenomenon’. This definition excludes man-made linear elements (field boundaries, roads, fences, etc., but could include strike ridges due to folding, other bedding planes, gneissic texture, lithological contacts and so on.” Structural trends such as discontinuities can be detected in many forms, such as faults, joints, fractures bedding planes or foliations, and may be useful in several environmental applications including landslide studies, hydrogeology, hydrogeomorphology, ground water assessment and mineral exploration. Such discontinuities can be detected in the form of lineaments detected not only using ground mapping but also using remotely sensed data such as conventional aerial photographs and satellite imagery. Good correlation between structures mapped in the field and using the lineament system enables the lineaments to be regarded as representative of the structural manifestation of a particular area. Lineament in this study is defined as a mappable, linear feature of a surface whose parts are aligned in a rectilinear or slightly curvilinear relationship and which differ from the pattern of adjacent features and presumably reflect some subsurface phenomenon this definition is chosen because it is the most practical definition in the context of remote sensing image interpretation (Gupta, 1991). The lineament mapping is aided by the existence of the geomorphological features such as aligned ridges and valleys, displacement of ridge lines, scarp faces and river passages, straight drainage channel segments, pronounced breaks in crystalline rock masses, and aligned surface depressions (Koch and Mather, 1997; Hung et al., 2005). The non-geological lineaments such as paths, roads, power cables and field boundaries in the study area were eliminated using the topographical map (Yassaghi, 2006) The identified linear features on the IRS LISS-3 imageries (2011 and 2012 images Rabi and Kharif) visualized and digitized using the ArcGIS 10 software. Figure 5.3 presents the lineament map of the study area. Broadly, three sets of lineaments could be identified in the field. They are NNW-SSW, NNE-SSW and NE-SW. Maximum lineament in Karawan watershed is along with streams (figure 5.4).Because fractures are lines of weakness and are foci for weathering processes (hydrolysis in particular), drainage lines in fluvially eroded terrain often develop along paths prepared by earlier weathering. The presence and passage of water along the fracture often causes lateral and vertical expansion of weathering, hence the zone of influence expands because of the presence of groundwater in the deeply weathered fracture zone. Adjoining areas, with low fracture densities and little or no weathered overburden may be dry for large periods and thus experience less intense weathering. By erosion, the weathered overburden may be partly removed and incising rivers may make use of the fractures. This is the case in the Karawan watershed areas of Figures where the drainage lines are mainly adjusted to fractures in the basalt and sandstone. “Ground Water Potential Modeling”

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Figure 5.3: Lineament Pattern in Karawan Watershed


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Figure 5.4: Compression between Drainage and Lineaments patterns


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5.4 Slope Analysis The slope amount map has been prepared using ASTER DEM. In relation to groundwater flat areas where the slope amount is low are capable of holding rainfall, which in turn facilitates recharge whereas in elevated areas where the slope amount is high, there will be high run-off and low infiltration. The method of producing the slope amount map is described below;

(A)

ASTER Digital Elevation Model (DEM);

(B)

Create Slope map in ArcGIS 10 software;

5.4.1 Digital Elevation Model (DEM)

Digital Elevation Models are data files that contain the elevation of the terrain over a specified area, usually at a fixed grid interval over the surface of the earth. The intervals between each of the grid points will always be referenced to some geographical coordinate system. This is usually either latitude-longitude or UTM (Universal Transverse Mercator) coordinate systems. By definition, “Any digital representation of the continuous variation of relief over space is known as a digital elevation modal.” The high-resolution ASTER data is expected to have significant impact in topographic mapping (figure 5.5) By False Colour Composite (FCC) sheet IRS LISS-3 satellite Image has 3-D surface representation of the study area (as shown in figure 5.6). This is very well shown the terrain characteristics (Plain, Mountain etc.) of the Karawan watershed. 3-D surface create by help of 3-D Analysis Toolset in ArcGIS 10.DEM generate for analyze the slope of study area.

5.4.2 Slope

Slope is the most important and specific feature of the earth’s surface form. Maximum slope line is well marked in the direction of a channel reaching downwards on the ground surface. In any region valley slopes, occupy most of the area of erosional relief in greater extent in comparison to flood plains, river terraces and other local depositional landforms. In geomorphology, the slope is combined effect of ‘form’ (Environmental conditions of slopes such as the geology, climate and vegetal cover) and ‘process’ (agents, such as soil creep, surface wash and the process of weathering). ‘Form’ and ‘Processes’ - both have existed right from the remote past. The sequence of the past forms prepares the way for the present ones, and this constitutes the evolution of a slope.


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Figure 5.5: Digital Elevation Model of Karawan Watershed


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Figure 5.6: 3D view of the Karawan Watershed “Ground Water Potential Modeling”

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Figure 5.7: Slope Map of Karawan Watershed in Degree


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Figure 5.8: Soil Texture Map of Karawan Watershed


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Chapter-5 The incline or steepness of a surface Slope can be measured in degrees from horizontal (0-90), or percent slope (which is the rise divided by the run, multiplied by 100). The slope (Degree) was derived from DEM. The slope of any terrain is one of the factors controlling the infiltration of groundwater into the sub-surface and also a suitability indicator from the groundwater prospect point of view. Higher slope area facilitates high run-off allowing less residence time for rainwater, whereas in the gentle slope area the surface run-off is slow, allowing more time for rainwater to percolate and hence comparatively more infiltration. The slope map of the study area is given in figure 5.7.

5.5 Soil Texture; The soil is essentially the living skin of the Earth and it is of tremendous importance to all people primarily because it is the foundation upon which our whole agricultural system rests. In simple terms, soil consists of the weathered remains of whatever rocks occur naturally within a region, along with variable amounts of organic material. The weathered material is produced by chemical and physical weathering processes and it is modified by the activities of plants and animals and microorganisms. If you are used to looking at layers of rock and you are familiar with the Law of Superposition (which says that in a sequence of sedimentary rocks, the lowest is the oldest and the highest is the youngest), you might be misled by the stratified nature of a soil. Soil horizons develop within some parent material and slowly work their way down from the surface. One of the most important features of soil, from the standpoint of its water-holding capacity is the variation in porosity with depth. Porosity is a measure of the open space within some soil or rock and it is function of the sizes of particles and the way they are arranged. Pore spaces represent the reservoir for holding water and a related parameter, called permeability. Texture indicates the relative content of particles of various sizes, such as sand, silt and clay in the soil. Texture influences the ease with which soil can be worked, the amount of water and air it holds, and the rate at which water can enter and move through soil. Soil texture is an important soil characteristic that drives groundwater and groundwater management. The textural class of a soil is determined by the percentage of sand, silt, and clay. The study area has been divided into six types of soil texture as fine (112.68 Km2), loamy skeletal (30.80 Km2), loamy (91.06 Km.2), fine loamy (4.40 Km.2), clayey (37.45 Km.2) and clayey skeletal (7.43 Km.2). The digitized map of soil texture in the area is shown in (figure 5.8).


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5.6 Land use / Land cover Land cover (LC) - Physical and biological cover of the earth’s surface including artificial surfaces, agricultural areas, forests, (semi-)natural areas, wetlands, water bodies. Land use (LU) - Territory characterized according to its current and future planned functional dimension or socio–economic purpose (e.g. residential, industrial, commercial, agricultural, forestry, recreational). Land is the most important natural resource, which embodies soil, water and associated flora and fauna involving the total ecosystem (Rao et al., 1996). Comprehensive information on the spatial distribution of land use/land cover categories and the pattern of their change is a prerequisite for management and utilization of the land resources of the study area. The land use pattern of any terrain is a reflection of the complex physical processes acting upon the surface of the earth. These processes include impact of climate, geologic and topographic conditions on the distribution of soils, vegetation and occurrence of water. For better development and management of the ground water, it is necessary to have timely and reliable information of land use and land cover. Keeping the above views in mind, the authors have prepared a land use/land cover map using IRS-P6 LISS-3 data (figure 5.10). LU / LC classes are classified by help of ERDAS Imagin-11 software. This figure depicts that there are ten units of land cover/land use pattern in the study area, which are given below and shown on the map. Barren rocky, Built-up land, dense forest, Double crop, Fallow land, Kharif crop, Open land, Rabi crop, Scrub forest and water body. Karawan watershed land use and land cover statistics shows in table 5.2.


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S.No. 1 2 3 4 5 6 7 8 9 10

LU / LC Class Barren Rocky Built Up Land Dense Forest Double Crop Fallow Land Kharif Crop Open Land Rabi Crop Scrub Forest Water Body

Area in Hact 438.5664 1389.5424 4058.208 15.7824 4456.8576 1875.168 7774.7328 6208.3584 3635.0208 335.4624

Table 5.2: Land Use and Land Cover Statistics of Karawan Watershed

Figure 5.9: Graphical Representation of LU/LC of Karawan Watershed


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Figure 5.10: Land Use and Land Cover Map of Karawan Watershed


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CHAPTER- 6

GIS MODELING FOR GROUNDWATER POTENTIAL ZONE IDENTIFICATION

CHAPTER-6 GIS MODELING FOR GROUNDWATER POTENTIAL ZONE IDENTIFICATION

G

roundwater is a precious resource of limited extent. In order to ensure a sustainable use of groundwater, proper evaluation of recharge is required. In general, groundwater model needs a large volume of multidisciplinary data from various sources. GIS can provide the appropriate platform for efficient processing and analysis of diverse data sets for decision making in groundwater potential zone identification, management and planning. In this work, an integrated GIS-based methodology is developed and tested for the identification of the groundwater potential zone. GIS is one of the most important tools for integrating and analyzing spatial information from different sources or disciplines. It helps to integrate, analyze and represent spatial information and database of any source, which could be easily used for planning of resource development, environmental protection and scientific researches and investigations (Maruo, 2003). In this work, all the needed data were prepared as GIS layers and have been integrated through GIS tools for analysis. GIS technology has been around since early 1960’s. However and after all of this time, most people still use GIS merely for map preparation and visualization purposes. GIS capabilities enable us to discover the embedded patterns and to figure out the spatial relationships in the geographic data across data of different types. The outcome of GIS analysis helps to focus our efforts and actions and choose the overall best option or plan (Parrish et al., 2005). As mentioned earlier, Model Builder of ArcGIS (Arc Toolbox) have been used in developing the groundwater potential model. The groundwater potentiality of the area has been assessed through integration of the relevant layers which include hydro-geomorphology, lineament, soil, land use land cover, slope, and drainage density in ArcGIS modular builder. Criteria for GIS analysis have been defined on the basis of groundwater conditions and appropriate weightage has been assigned to each information layer according to relative contribution towards the desired output.

6.1 GIS Modeling

6.1.1 Why GIS in This Work?

Determination of groundwater potential zone in Karawan watershed areas is neither straightforward nor easy. This is a consequence of the time variability of Precipitation, climates and the spatial variability in soil texture, geomorphology, land use and land cover, structures and lithology. GIS technology enables the ease of data processing, visualization, sorting, assessment, computations, and map preparation when compared with traditional technologies. GIS assists “Ground Water Potential Modeling”

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Chapter-6 in measurement, management, multidimensional monitoring and analysis, modeling operations, output visualization and data processing. In addition, GIS capabilities in developing models that consider the spatiality in properties are very advanced. For instance, Model Builder has a lot of advantages that automate repetitive tasks, explore data iteratively, has a visual approach and enables considering more processes and data.

6.1.2

Why build models?

Building a model helps you manage and automate your geoprocessing work flow. Managing processes and their supporting data can be difficult without the aid of a model. A sophisticated model contains a number of interrelated processes. At any time, you may add new processes, delete existing processes, or change the relationships between processes. You may also change assumptions or parameter values, for example, replace old datasets with newer ones, or consider alternative scenarios in which input factors are prioritized differently. Building a model helps you manage this complexity in a number of ways: • • • •

It makes processes and the relationships between processes explicit, and the model you create is dynamically updated whenever a change is made. It lets you set values for the parameters of each tool, and it records this information, making the model output easily reproducible. It lets you edit the structure of the model by adding and deleting processes or changing the relationships between the processes. It lets you edit the parameter values defined for tools to experiment with alternative outcomes.

6.1.3 What is GIS-based modeling?

In general terms, a model is a representation of reality. A model represents only those factors that are important to your work flow and creates a simplified, manageable view of the real world. In ArcGIS, a model is displayed as a model diagram. You automate your work flow by stringing processes together in the model diagram that will execute in sequence when the model is run. A model is any device that represents an approximation of a real situation (Anderson and Woessner, 1992). Models are used in many ways, ranging from water applications to scientists developing complex models to monitor global warming. Within the GIS context, a model can simplify complex data and emphasize relationships and patterns in the data, making it easier for the decision maker to understand the problem and develop an appropriate solution (Parrish, 2005). In addition, GIS modeling capabilities facilitate an efficient environment for handling spatial data. “Ground Water Potential Modeling”

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Chapter-6 The advent of GIS has allowed for the expansion of modeling. Spatial models use GIS to combine digital maps in an ordered process to assist decision making. As with anything else, as you gain more experience, you can increase the sophistication of your modeling techniques (Parrish, 2005).

6.2 Model Builder GIS technology has not only made it possible to process, analyze, and combine spatial data, but it has also made it easy to organize and integrate spatial processes into larger systems that model the real world. However, the more complex a spatial model becomes, the more difficult it is to keep track of the various data sets, processing procedures, parameters, and assumptions that you have used (ESRI Educational Services, 2000).ModelBuilder is a new technology from ESRI that helps you create and manage spatial models that are automated and self documenting. A spatial model in ModelBuilder is easy to build, run, save, modify, and share with others (ESRI Educational Services, 2000). ModelBuilder is a tool that helps you create spatial models for geographic areas. A model is a set of spatial processes that converts input data into an output map using specific functions that simulates specific phenomena. Large models can be built by connecting several processes together. In ModelBuilder, a spatial model is represented as a diagram that looks like a flowchart (ESRI Educational Services, 2000). It has nodes that represent each component of a spatial process. Rectangles represent the input data, ovals represent functions that process the input data, and rounded rectangles represent the output data that is created when the model is executed. The nodes are connected by arrows that show the sequence of processing in the model (ESRI Educational Services, 2000). The model is much more than a static diagram; it stores all the information necessary to run the processes and create the output data in ArcGIS. You can also create documentation that is saved as part of the model. This enables you to reuse the model and share it with others. You can apply the same model to different geographic areas by changing the input data. You can easily modify the model to explore “what if” scenarios and obtain different solutions (ESRI Educational Services, 2000). ModelBuilder has the tools you need to create, modify, and run a model. ModelBuilder uses ArcGIS to process the input and create the output data (ESRI Educational Services, 2000).

Some features of ModelBuilder include: •

A model window where you build and save your models.

•

Wizards that automate the creation of new spatial processes or the editing of existing processes. “Ground Water Potential Modeling”

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Figure 6.1: Depiction of modeling processes using Model Builder. (ESRI Educational Services, 2000).

Figure 6.2: Modular Builder Engine Window


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• •

Property sheets that let you quickly modify the properties of input data, processes, or output data. Drag-and-drop tools that let you build and connect processes manually.

•

Layout tools that help you arrange your model neatly.

6.2.1 What is the ModelBuilder window?

The ModelBuilder window (figure 6.2) is the interface you use to create models in ArcGIS. A ModelBuilder window is displayed immediately when you create a new model. The ModelBuilder window consists of a display window in which you build a diagram of your model, a Main menu, and a toolbar that you can use to interact with elements in your model diagram. You can run a model from within the ModelBuilder window or from its dialog box. There are five pull down menus on the Main menu. The Model menu contains options for running, validating, viewing messages, saving, printing, importing, exporting, and closing the model. You can also use this menu to delete intermediate data and set properties for the model. The Edit menu lets you cut, copy, paste, delete, and select model elements. The View menu contains an Auto Layout option that applies the settings specified in the Diagram Properties dialog box to your model. It also contains options for zooming in and out. The Window menu contains an overview window that you can use to display the entire model while you zoom in on a certain part of the model in the display window. From the Help menu, you can access the ArcGIS Desktop online Help system and the About ModelBuilder box.

6.2.2 What ModelBuilder Does?

ModelBuilder helps you build, manage, and automate spatial models. Without ModelBuilder, the management of models and of the data supporting them can be difficult. A sophisticated model contains a number of interrelated processes. At any time, you may add new processes, delete existing processes, or change the relationships among processes. In addition, you may replace old data sets with newer ones, change assumptions or model parameters, and consider alternative scenarios in which input factors are prioritized differently (ESRI Educational Services, 2000). ModelBuilder helps you manage the complexity of a model in the following ways: •

It makes processes and the relationships among processes explicit. ModelBuilder diagrams processes and the relationships among processes in a flowchart that is dynamically updated whenever a change is made.

•

It lets you set the properties of the input data, the functions, and the output data, and it records this information in the model. This makes the model output reproducible. “Ground Water Potential Modeling”

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• • • • •

It stores documentation inside the model. You can document sources of input data and assumptions you made in the model. It stores and manages the model files and the output data on disk. It lets you edit the structure of the model by adding and deleting process or by changing the relationships among the processes. It lets you edit the properties of processes to experiment with alternative outcomes. When using ModelBuilder, you can create, visualize, document, run, modify, and share models.

ModelBuilder provides both beginning and advanced users with a set of easy to- use tools for building various types of spatial models within ArcGIS Spatial Analyst (ESRI Educational Services, 2000). ModelBuilder is a comprehensive geographic decision support tool that makes the solving of complicated problem simple. ModelBuilder will help you easily manage your project analysis requirements, from simple geographic analysis to complex spatial modeling (ESRI Educational Services, 2000).

6.3 Building models Inside a ModelBuilder window, the display window is the working area where you build a diagram of your model. The diagram you build looks like a flowchart. It consists of processes linked together that will run in sequence when the model is run. The diagram that follows is a conceptual overview of a model built from three processes (figure 6.3). Each element in a model has a unique symbol. Project data elements represent the geographic input data that exists before the model runs. The data referenced by these elements is used as input parameter values for tools in a model. Project data elements are represented by dark blue ovals (figure 6.4) and are variables by default. Variables expose parameter values so they can be shared between processes. Tool elements represent the operations to be performed on input data parameter values. Tool elements are represented as yellow rectangles (figure 6.4). Derived data elements represent the output data created by a tool. Data referenced by these elements does not exist until the model is run. The exception is when running a tool that updates the project data, such as Add Field. In this case, the derived data is actually the project data, with an additional field added. Derived data from one process can serve as input data for another process. Derived data elements are represented by green ovals (figure 6.4) and are variables by default. Variables expose parameter values so they can be shared between processes. “Ground Water Potential Modeling”

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Chapter-6 Value elements reference nongeographic data parameter values. Values set for these elements can be used as input to tools in a model where appropriate. Value (or nongeographic data) elements are represented by light blue ovals (figure 6.4). Derived value elements reference non-geographic data parameter values that are created by running a tool. An example of a derived value is the output value from running the Calculate Default Cluster Tolerance tool. Derived values from one process can serve as input values for other processes. Derived value elements are represented by light green ovals (figure 6.4). A connector is a line showing the sequence of processing. Data elements and tool elements are connected together. The connector arrow shows the direction of processing. In addition to the basic model elements, there are text labels, which are graphical elements that place explanatory text in a model. A label is not part of the processing sequence. The default text for elements can be changed, and labels can be attached to elements or float free in the model diagram (figure 6.5). A model can be simple or complex. The simplest possible model contains a single process. In the model that follows, stream data is processed to create a dataset of buffer zones around streams. You can see (figure 6.6) the flow of project data (Input Streams) into the Buffer tool and from the Buffer tool to the derived data (Output Streams Buffer). The present study has attempted to apply integrated remote sensing and GIS for generating new thematic data layers as well existing data for delineating potential groundwater zone. The eight thematic layers taken for the determination of potential groundwater were drainage density, slope steepness, soil texture, drainage order, land cover/use and distance from lineaments, geomorphology and lithology. Prior to integration of the data sets, individual class weights and map scores were assessed based on Multi Criteria Analysis (MCA) (table 6.1); Multi Criteria Analysis (MCA) is a tool that has been developed for complex multi criteria problem(s) within decision making. The method(s) include qualitative as well as quantitative aspects of the problem(s) in the decision making process.

6.4 Integration of Remote Sensing and GIS

6.4.1 Buffer Analysis of Linear Features

Buffer analysis is used for identifying areas surrounding geographic features. The process involves generating a buffer around existing geographic features and then identifying or selecting features based on whether they fall inside or outside the boundary of the buffer. In case of Geographical Information Systems, the units of buffering are points, lines, and polygons. Buffer operation refers “Ground Water Potential Modeling” 61

Chapter-6

Figure 6.3: A conceptual overview of a model: A model contains processes, and a process contains a tool element and its parameter values. In the example above, project and Derived data parameter values are shared between Processes.

Figure 6.4: Model Elements

Figure 6.5: Creating free floating text

Figure 6.6: Flow of Project Data “Ground Water Potential Modeling”

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Figure 6.7: Drainage Buffer (Buffer distance 500 m.) of Karawan Basin


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Figure 6.8: Lineament Multi Ring Buffer Map


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Chapter-6 the creation of a zone of a specified width around a point or a line or a polygon area. In this study involve the line buffering process (Drainage and Lineament buffer) (figure 6.7 and 6.8). Lineament and drainage buffer generated on Arc GIS 10 Modulerbuilder (figure 6.9 and 6.10).

6.4.2 Rasterization

The Rasterization is process of the conversion of points, lines, and polygons into cell data. In other word, the term rasterization can in general be applied to any process by which vector information can be converted into a raster format. Rasterization process required in this study for weighted overlay function using ArcGIS 10 Modulerbuilder engine (figure 6.11).

6.4.3 Reclassification

“ The process of taking input cell values and replacing them with new output cell values. Reclassification is often used to simplify or change the interpretation of raster data by changing a single value to a new value, or grouping ranges of values into single values—for example, assigning a value of 1 to cells that have values of 1 to 50, 2 to cells that range from 51 to 100, and so on.” There are many reasons why you might want to reclassify your data. Some of the most common reasons are: •

To replace values based on new information

•

To group certain values together

•

To reclassify value to a common scale

•

To set specific values to NoData or NoData cells to a value.

In this study given the values based on groundwater potentiality of the each class of the all layer (table 6.1).

1.

Lineament;

For analysis of lineaments in relation to groundwater prospective zones, distance analyses were carried out and 4 buffer zone classes (150 m., 300 m., 500m. distance and other area) were produced .Reclassified weighted map of lineament (figure 6.13) has been then produced based on the weight (table 6.1). After given the weights those areas closer to lineaments are the highest zone of increased porosity and permeability which in turn have greater chance of accumulating groundwater.


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Figure 6.9: Showing the Lineament Multi Buffer Process on ArcGIS 10 Modulerbuilder Engine.

Figure 6.10: Showing the Drainage Buffer Tool on ArcGIS Modulerbuilder Engine.


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Figure 6.11: Showing the Polygon shape file to Raster feature on ArcGIS Modulerbuilder Engine.

Figure 6.12: Reclassification of the all Raster Layer on ArcGIS Modulerbuilder Engine. “Ground Water Potential Modeling”

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2.

Geomorphology;

The feature identified and rated based on their groundwater potential. Groundwater prospects in buried pediplain are excellent. Pediment and pediplain zone occurring in the very good groundwater potential. The denudational hill and residual hill indicates very little groundwater potential. Structural hills indicate the moderate groundwater potentiality because there are lineament structures and some pediment and pediplain area developed on the shale type of rock that area indicates the poor groundwater porosity. According to groundwater potentiality of the geomorphological landforms reclassify value show the table 6.1

3.

Lithology;

The study area belonging only four types of lithology mainly, compact basalt shows very poor groundwater potential. Sandstone, shale and vesicular basalt having characteristics excellent, moderately good and moderate groundwater potential respectively. Reclassified map of lithology (figure 6.15) has been then produced based on the weight (fable 6.1).

4.

Slope;

The study area show flat area (0–1.50), indicate presence of excellent groundwater potential. Low slope (1.5 – 9.40) indicate very good groundwater potential, moderate slope (9.4 - 290) indicates moderate groundwater potential. High slope (29 - 570) indicates presence of poor groundwater potential and steeper slope (800 >) indicate the null groundwater potential. Major area of study was having low slope indicates high groundwater potential but may affected by underlying lithology as well as hydrogeomorphology feature. Reclassified map of slope (figure 6.16) was then produced based on the weight (table 6.1). After given the weights those areas closer flatness are the highest zone of increased porosity and permeability which in turn have greater chance of accumulating groundwater.

5.

Soil;

Soils in the study area can be under six hydraulic soil texture, i.e. texture fine (Excellent), loamy skeletal (Moderately good), loamy (Very good), Fine loamy (good), clayey (Moderately good) and clayey skeletal (Moderate). Reclassified map of soil (figure 6.17) has been then produced based on the weight (table 6.1).


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Figure 6.13: Lineament Buffer Weight Map


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Figure 6.14: Geomorphology Weight Map


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Figure 6.15: Lithology Weight Map


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Figure 6.16: Slope Weight Map


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Figure 6.17: Soil Weight Map


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Figure 6.18: Drainage Density Weight Map


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Figure 6.19: Drainage Order Buffer Weight Map


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Figure 6.20: LU / LC Weight Map


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6.

Drainage Density;

With respect to groundwater occurrences the higher drainage density is related to less infiltration of water to the ground, which in turn leads to higher run off and vice versa. The reclassified map of drainage density (figure 6.18) has been produced based on these weights (table 6.1).

7.

Drainage Order ;

With respect to the occurrence of the higher drainage order buffer (500 m. Distance) is related to higher infiltration of water to the ground, which in turn leads to low runoff and vice versa. The reclassified map of drainage order buffer (figure 6.19) has been produced based on these weights (table 6.1).

8.

Land Use / Land Cover;

One of the parameters that influence the occurrence of sub-surface groundwater occurrence is the present condition of land cover and land use of the area. The effect of land use / cover is manifested either by reducing runoff and facilitating, or by trapping water on their leaf. Water droplets trapped in this way go down to recharge groundwater. Land use/cover may also affect groundwater negatively by evapotranspiration, assuming interception to be constant. Classification of land use/cover for analysis was done based on their character to infiltrate water in to the ground and to hold water on the ground. Generally settlements are found to be the least suitable for infiltration. Reclassified map of Lu/LC (figure 6.20) was then produced based on the weight (table 6.1).

6.5 Integration Analysis in GIS Environment The main objective of the study is to generate groundwater potential zone of the based on different thematic maps by considering their relevance to groundwater occurrence. In order to produce the potential groundwater zone map detailed GIS analysis of eight thematic maps was conducted. A groundwater model was constructed using ArcGIS model builder engine (figure 6.2). Using the model all maps were rasterized, reclassified and given appropriate weight in order to integrate them for multi criteria evaluation (MCE). The following steps have been followed to produce groundwater potential zone: (i) Selection of data for an input based on their groundwater controlling parameters. (ii) Using the model feature dataset was prepared and each data set that was produced “Ground Water Potential Modeling”

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Chapter-6 Raster Layer

Lineament Buffer

Geomorphology

Lithology

Soil Texture

Slope

Drainage Density

Influence (%) (Theme weight) 21

18

16

13

13

8

Feature Classes or Buffer Distance

Feature class Weight

Lineament Buffer 0 – 150 m. Buffer 151 – 300 m. Buffer 301 – 500 m. Buffer Other Area Landforms Buried Pediplain Residual Hill Denudetional Hill Pediment / Pediplain Structural Hill Pediment / Pediplain With Shale Rock Type Compact Basalt Vesicular Basalt Shale Sandstone Soil Type Fine Loamy Skeletal Loamy Fine Loamy Clayey Clayey Skeletal Slope Gradient in Degree 0 – 1.5 1.5 – 9.4 9.5 - 15 15 – 29 29 - 57 57 - 80 D.D. In Km.2


9 5 2 1 9 1 1 8 5 3

1 5 7 9 9 6 8 7 6 5

9 8 5 5 3 1

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LU / LC

Drainage Buffer

4

7

0 – 0.8 0.8 – 1.3 1.3 – 2.18 2.18 – 2.6 2.6 – 3.14 3.1 – 3.8 3.8 – 5.5 LU / LC Built Up Land Kharif Crop Rabi Crop Double Crop Fallow Land Scrub Forest Dense Forest Open Land Barren Rocky Water Body Drainage Order 1st 2nd 3rd 4th 5th 6th

9 8 7 5 3 2 1 1 8 8 8 7 3 3 7 2 9 2 3 7 7 9 9

Table 6.1: Different themes and thematic parameters considered for groundwater prospects evaluation and their class weights


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Chapter-6 from previous work, remote sensing imagery, digital elevation model (DEM), topographic maps and field observation were imported into geodatabase to have the same spatial reference. (iii) All the data sets were then converted into raster grid in the model in order to perform different GIS analysis between data layers such as overlay analysis. (iv) All the data sets were reclassified based on their importance to groundwater potentiality (availability). (v). Prior to integration of the data sets, individual class weights and map scores were assessed based on Multi criteria weighted overlay (figure 6.21a and b) (vii) After given the weight of the each class and influence (%) of the each layer depend on the groundwater potentiality prepare the groundwater potential map.

6.5.1 Weighted Determination

A Number of that tells how important a variable is for a particular calculation. The larger the weight assigned, the more that variable will influence the outcome of the operation, called the weighted based function. All following steps have done on Arc GIS Modulerbuilder engine (figure 6.2). The steps for running weighted overlay are: 1)

Select an evaluation scale; In the Weighted Overlay dialog box (figure 6.21a and b), select an evaluation scale to use. Values at one end of the scale represent one extreme of suitability (or other criterion); values at the other end represent the other extreme. The default evaluation scale is from 1 to 9 in increments of 1 (least suitable 1, most suitable 9). After the reclassified of the all layer using a scale of 1 to 9 (1 being least suitable and 9 being most suitable), an evaluation scale of 1 to 9 by 1 entered for the evaluation scale in the Weighted Overlay dialog box.

2)

Add raster’s; Click the Add raster row button to open the Add Weighted Overlay dialog box. Click the Input raster drop-down arrow and click a raster, or click the Browse button to browse to an input raster and click Add. Click the Input field drop-down arrow to change the field if desired. Click OK. The raster is added to the Weighted Overlay table. Click the Add raster row button again to enter the next raster, and so on.

3)

Set scale values; The cell values for each input raster in the analysis are assigned values from the evaluation scale. This makes it possible to perform arithmetic operations on raster’s that originally held dissimilar types of values. The change default values assigned to each cell according to importance or suitability. “Ground Water Potential Modeling” 80

Chapter-6

4)

5)

Assign weights to input raster’s; Each input raster can be weighted, or assigned a percentage influence, based on its importance. The total influence for all raster’s must equal 100 percent. For instance, it might be more important to build a shopping center on soils that are stable than to locate in a popular shopping area. The maps of these parameters were assigned respective theme weight (figure 6.21a) and their class weights (figure 6.21b). The individual theme weight (table 6.1) was multiplied by its respective class weight (table6.2)and then all the raster thematic layers were aggregated in a linear combination equation in Arc Map GIS Raster Calculator module as given Here:

GWPM = (LMwt * 0.21) + (GMwt * 0.18) + (LTHwt * 0.16) + (Slopewt * 0.13) + (Soilwt * 0.13) + (DDwt * 0.08) + (DOwt * 0.07) + (LULCwt * 0.04) Here, GWPM = Ground Water Potential Map, GM = Geomorphology, LTH = Lithology, DD = Drainage Density, DO = Drainage Order, LULC = Land Use Land Cover, wt = Feature class weight. The final cumulative map generated by applying the above equation. 6)

Run the Weighted Overlay tool; The cell values of each input raster are multiplied by the raster’s weight (or percent influence). The resulting cell values are added to produce the output raster (figure 7.1).

The tool was used for suitability modeling (to locate suitable areas); higher values generally indicate that a location is more suitable. The tool was used to generate a groundwater potential, high values will generally indicate higher groundwater potential.


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Figure 6.21a: Weighted Overlay Dialog Box of ArcGIS 10.

Figure 6.21b: Weighted Overlay Dialog Box. “Ground Water Potential Modeling”

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CHAPTER- 7

GROUNDWATER POTENTIAL ZONE AND VALIDATION

CHAPTER-7 GROUNDWATER POTENTIAL ZONE AND VALIDATION

T

he delineation of groundwater potential zones by reclassifying into different potential zones; Very Good, Good, Moderate, poor and Poor (figure 7.2) was made by utilizing the model designed using ArcGIS model builder engine. Criteria for GIS analysis have been defined on the basis of groundwater conditions and appropriate weightage has been assigned to each information layer according to relative contribution towards the desired output. The map produced has shown that the groundwater potential of the project area is related mainly to lineaments, geology, geomorphology, soil, drainage density, drainage order, LU/LC and slope. The integrated resulted in a Composite Groundwater Suitability Unit Map (CGSU). The output CGSU map is a surface with all the pixels having unified weight values named as Composite Suitability Indices (CSI). These CSI range from 2 to 9. Higher the value indicates more suitability for groundwater and lower value indicates lesser suitability. Mean and Standard Deviation have been calculated from the resulted CSI.

Mean of CSI = 5.9 Standard Deviation of CSI = 1.18 Resulted CSI values are grouped using the combinations of the mean and standard deviation. A ‘Domain’ (ArcGIS object) has been prepared using class and group. Five groundwater potential zones are defined by grouping of CSI using mean and standard deviation. The groundwater potential zones are:

Upper Limit of CSI

Groundwater Potential

Mean – 2* Standard Deviation Mean - Standard Deviation Mean Mean + Standard Deviation Mean + 2* Standard Deviation

Very Poor Poor Moderate Good Very Good

= = = = =

CGSU map has been classified using the ArcGIS 10 software for final ‘Groundwater Potential Zone Map’ showing spatial distribution of five groundwater potential zones (figure 7.2). The groundwater potential zones map generated through this model was verified with the yield data to ascertain the validity of the model developed. The verification showed that the ground “Ground Water Potential Modeling”

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Chapter-7 water potential zones demarcated through the model are in agreement with the bore well yield data. Since the present approach has been built with logical conditions and reasoning, this approach can be successfully used elsewhere with appropriate modifications. Thus, the above study has clearly demonstrated the capabilities of remote sensing and GIS technique in demarcation of the different groundwater potential zones. The validity of the model developed was tested against the borehole data, where out of 35 with yield borehole data collected from the study area 19 are on very good and good zones, 9 on moderate zones, 7 on poor and very poor zones (figure 7.3). The model generated will help as a guideline for designing a suitable groundwater exploration plan in the future. The spatial distributions of the various groundwater potential zones obtained from the model generally show regional patterns of lineaments, drainage, landform and lithology. Spatially the very good and good categories are distributed along areas near to lineaments and less drainage density and where the lithology is affected by secondary structure and having interconnected pore spaces. This highlights importance of lineaments, geology and hydrogeomorphological parameters in the project area. Areas with moderate groundwater prospects are attributed to contributions from combinations of the land use/cover, lithology, slope and landform. The low to poor categories of groundwater potential zones are spatially distributed mainly along ridges where slope class is very high, the lithology is compact/massive and far from lineaments.


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Figure 7.1: Composite Groundwater Suitability Unit Map.


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Figure 7.2: Groundwater Potential Zone Map


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Figure 7.3: Distribution of boreholes in ground water potential zones


88

CHAPTER-8 CONCLUSIONS AND RECOMMENDATION 8.1 Conclusion

T

he main objective of this project is to use GIS and Remote sensing technique for the assessment, evaluation and analysis of spatial distribution of ground water potential zones with in an area of 275.61 Km.2. Ground water potential zone map have been produced using eight thematic maps from satellites images, exiting data and field data. Produced ground water potential zone map has been compared and validated by existing discharge data obtained from different localities of the project area. The result showed fairly significant correlation or agreement with the discharge data. This study has shown that large spatial variability of ground water potential. This variability closely followed variability in the structure, geology, geomorphology and land use/cover in the project area. The most promising potential zone in the area is related to volcanic rock of which is affected, by secondary structure and having interconnected pore spaces, with plain geomorphic feature and less drainage density. Most of the zones with poor to very poor groundwater potential lie in the massive basements unit which is far from lineaments. This study generally demonstrates that GIS and Remote sensing techniques in combination with field data could be used for the assessments of ground water potential zones in an area with little primary porosity and low bedrock hydraulic conductivity and where hydrogeological properties are mainly determined by secondary factors fracture zones and associated weathering. It can be considered as a time and cost-effective tool for delineations and identification of high ground water potential target area.

8.2 Recommendation Remote sensing data are powerful tools to improve our understanding of groundwater systems. Despite unable to measure hydrogeological properties directly, they provide continuous detailed terrain information and allow the mapping of features significant to groundwater development therefore it is important to incorporate them in the data collection stage of groundwater exploration works. Despite various satellite data with different spectral and spatial resolutions coupled with digital image processing techniques help to produce detailed maps, ground verification is crucial to increase the accuracy of the interpretation results. The result obtained from this study should be supported by subsurface data obtained from geophysical study. Since geology, geomorphology and lineament mainly control the distribution occurrence and flow of groundwater, analysis of these parameters should be supported by high-resolution terrain data and satellite imagery. “Ground Water Potential Modeling” 89

Chapter-8

ABBREVIATIONS • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

A Basin Area AISLUS All India Soil and Land Use Survey AOI Area of Interest CGSU Composite Groundwater Suitability Unit Map CI Channel Length CSI Composite Suitability Indices CWPC Central Water and Power Corporation DBMS Data Base Management System Dd Drainage Density DEM Digital Elevation Model DIP Digital Image Processing DRM District Resource Map Dt Drainage Texture EMR Electro Magnetic Radiation ESRI Environmental Systems Research Institute FCC False Color Composite Ff Form Factor Fs Stream Frequency GCP Ground Control Point GIS Geographic Information System GPS Global Positioning System GSI Geological Survey of India GTS Geological Time Scale IRS Indian Remote Sensing IMD Indian Meteorological Department Lb Basin Length LISS Linear Image Self Scanning Lu Stream Length LU / LC Land Use / Land Cover MCA Multi-Criteria Analysis MCE Multi-Criteria Evolution NASA National Aeronautics and Space Administration NRCS Natural Resource Conservation Service Nu Stream Number P Basin Perimeter Rb Bifurcation Ratio Rt Texture Ratio SOI Survey of India µ Stream Order UTM Universal Transverse Mercator WGS World Geological Survey WRR Water Resource Region “Ground Water Potential Modeling”

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ADDITIONAL READING • A Manual: Multi Criteria Analysis (2009) Department for Communities and Local Government: London. • A.M.J. Meijerink, D. Bannert, O. Batelaan, M. W. Lubczynski, T. Pointet Remote Sensing Application to Groundwater. United Nations Educational Scientific and Cultural Organization, International Hydrological Programme. IHP- 4th, Series on Groundwater No. 16. • ArcGIS 9: Geoprocessing in ArcGIS (2004) ESRI. • Ausaf Sayeed (1991) Trend in Objective Geology, 1st Edition. CBS Publishers and Distributers, Delhi. • B. Bhatta (2009) Remote Sensing and GIS, 2nd Edition. Oxford University Press, Delhi. • Campbell, J.B. (1987) Introduction to Remote Sensing. The Guilford Press, New York. • Colin E. Thorn (1988) Introduction to Theoretical Geomorphology, 1st Edition. Boston Unwin Hyman. • Corey Tucker, Jason Purdy (2008) Developing Guide to Geoprocessing. ESRI Developer Summit. Palm Springs, CA. • Cuchlaine A. M. King, John C. Doornkamp (1971) Numerical Analysis in Geomorphology, 1st Edition. ST. Martin’s Press, New York. • Dean Djokic, Steve Koppa, Nawajish Noman (2010) Hydrological Modeling with ArcGIS. ESRI International User Conference, San Diego, CA. • Hassan A. Karimi (2009) Handbook of Research on Geoinformatics. University of Pittsburgh, U.S.A. • Heather Kennedy (2000) Dictionary of GIS Terminology. ESRI Press, Read land, California. • Jensen, John R. (1986) Introductory Digital Image Processing. Prentice-Hall, New Jersey. • J.P. Sharma (2012) Practical Geography, 4th Edition. Rastogi Publications, Delhi. • Lillesand, T.M. and Kiefer, R.W. (1994) Remote Sensing and Image Interpretation. John Wiley and Sons Inc., New York. • Majid Husain (1994) Geomorphology, 1st Edition. Anmol Publications Pvt Ltd, Delhi. • Michael Witherick, Simon Ross, John Small (2001) A Modern Dictionary of Geography, 4th Edition. Arnold Student Reference. • M. G. Hart (1985) Geomorphology Pure and Applied, 1st Edition. CBS Publishers and Distributers, Delhi. “Ground Water Potential Modeling”

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Chapter-8 • Parbin Singh (2008) Engineering and General Geology, 8th Edition. S.K. Kataria and Sons Publications, Delhi. • Paul A. Longley, Michael F. Googchild, David J. Maguire, David W. Rhind (2004) Geographic Information System and Science, 2nd Edition. John Wiley and Sons. • P.S. Roy, R.S. Dwivedi, D. Vijayan, Remote Sensing Application. National Remote Sensing Center. • R.B. Salama, G.A. Bartle, L. Ye, D.R. Williamson, G.D. Wastson, A. Knapton (1997) Hydrological & Hydrogeology of The Upper Kent River Catchment & Its Controls on Salt Distribution & Pattern of Groundwater Discharge. Technical Report No. 27/97, CSIRO Australia. • Savindra Singh (2007) Geomorphology, 6th Edition. Vasundhara Publication, Gorakhpur. • Sandeep Goyal (2002) An Integrated Approach for Watershed Management A case Study of Chundi Watershed. Department of Geography University of Allahabad, Allahabad. • Scott Davis (2007) GIS for Web Developers. Pragmatic Book Sely, Raleigh, North Carolina Dallas, Texas. • Semera (2003) Remote Sensing and GIS: Application for Groundwater Potential Assessment in Eritrea. Environmental and Natural Resource Information System, Royal Institute of Technology, Sweden. • Shahab Fazal (2008) GIS Basics. New Age International Publishers. • Shashi Shekhar, Hui Xiong (2008) Encyclopedia of GIS. Springer Reference.


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Appendix Groundwater Potential Model Python Script # -----------------------------------------------------------------------------------------------------------------#Groundwater Potential Model.py # Created on: 2012-08-11 17:20:27.00000 # (generated by ArcGIS/ModelBuilder) # Description: Groundwater Potential Zone # -----------------------------------------------------------------------------------------------------------------# Import arcpy module import arcpy # Check out any necessary licenses arcpy.CheckOutExtension(“spatial”) arcpy.CheckOutExtension(“3D”) # Local variables: Drainage_shp = “Drainage_Topo.shp” Geomorphology_shp = “Geomorphology.shp” Lineament_shp = “Lineament.shp” Lithology_shp = “Lithology.shp” Soil_shp = “Soil_Family.shp” Watershed_area_shp = “Watershed_sagar.shp” DEM = “dem.img” Land_Use_Cover_Raster = “final_lulc.img” Drainage_Bufferf_shp = “Drainage_Buf.shp” Drainage_Buffer_shp = “Marge_Drain_Bf.shp” Lineament_Buffer_shp = “Lineament_Buf.shp” Intersect_Lineament_shp = “Lin_Buf_Final.shp” Lineament_Bufferf_shp = “F_Lin_Buf.shp” Drainage_Density = “dd” Slope_Raster = “slope” Lineament_Raster = “Lineament” Drainage_Raster = “drainage” soil_Raster = “soil” Lithology_Raster = “Lithology” Geomorphology_Raster = “Geomorphology” Slope_Weight_Map = “slope” Lineament_Weight_Map = “Lineament” “Ground Water Potential Modeling” 93

Chapter-8 Drainage_Weight_Map = “drainage” Soil_Weight_Map = “soil” Lulc_Weight_Map = “lulc” Lithology_Weight_Map = “Lithology” DD = “dd” Geomorphology_Weight_Map = “Geomorphology” Groundwater_Potential_Map = “map” # Process: Polygon to Raster (5) arcpy.PolygonToRaster_conversion(Geomorphology_shp, “Landform”, Geomorphology_Raster, “CELL_CENTER”, “NONE”, “71”) # Process: Reclassify (8) arcpy.gp.Reclassify_sa(Geomorphology_Raster, “LANDFORM”, “’Buried Pediplain’ 9;’Residual Hill’ 1;’Denudetional Hill’ 1;’Pediment/ Pediplain’ 8;’Structural Hill’ 5;’Pediment/ Pediplain With Shale’ 3”, Geomorphology_Weight_Map, “DATA”) # Process: Line Density arcpy.gp.LineDensity_sa(Drainage_shp, “NONE”, Drainage_Density, “69.4713200000003”, “578.927666666669”, “SQUARE_KILOMETERS”) # Process: Reclassify (7) arcpy.gp.Reclassify_sa(Drainage_Density, “Value”, “0 0.30056905746459955 9;0.30056905746459955 0.84159336090087955 9;0.84159336090087955 1.3225038528442399 8;1.3225038528442399 1.763338470458987 4;1.763338470458987 2.1841351509094253 7;2.1841351509094253 2.6249697685241675 7;2.6249697685241675 3.1259181976318291 5;3.1259181976318291 3.8072080612182488 3;3.8072080612182488 5.1096739768981934 1”, DD, “DATA”) # Process: Polygon to Raster (4) arcpy.PolygonToRaster_conversion(Lithology_shp, “Rock_Type”, Lithology_Raster, “CELL_ CENTER”, “NONE”, “71”) # Process: Reclassify (6) arcpy.gp.Reclassify_sa(Lithology_Raster, “ROCK_TYPE”, “Sandstone 9;’Vesicular Basalt’ 6;’Compact Basalt’ 1”, Lithology_Weight_Map, “DATA”) # Process: Reclassify (5) arcpy.gp.Reclassify_sa(Land_Use_Cover_Raster, “Class_name”, “’Built_Up Land’ 1;’Kharif Crop’ 8;’Rabi Crop’ 8;’Double Crop’ 8;’Fallow Land’ 7;’Scrub Forest’ 3;’Dense Forest’ 3;’Land with or without scrub’ 7;’Barren Rocky’ 2;’Waterbody & Rivers’ 9”, Lulc_Weight_Map, “DATA”) # Process: Polygon to Raster (3) “Ground Water Potential Modeling”

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Chapter-8 arcpy.PolygonToRaster_conversion(Soil_shp, “FAMILY_TEX”, soil_Raster, “CELL_ CENTER”, “NONE”, “72”) # Process: Reclassify (4) arcpy.gp.Reclassify_sa(soil_Raster, “FAMILY_TEX”, “Fine 9;LoamySkeletal 6;Laomy 8;FineLoamy 7;Clayey 6;ClayeySkeletal 5”, Soil_Weight_Map, “DATA”) # Process: Buffer arcpy.Buffer_analysis(Drainage_shp, Drainage_Bufferf_shp, “500 Meters”, “FULL”, “ROUND”, “NONE”, “”) # Process: Merge arcpy.Merge_management(“’Drainage_Buf.shp’;’Watershed_sagar.shp’”, Drainage_Buffer_shp, “OBJECTID \”OBJECTID\” true true false 9 Long 0 9 ,First,#,Drainage_Buf.shp,OBJECTID,1,-1;Id \”Id\” true true false 9 Long 0 9 ,First,#,Drainage_Buf.shp,Id,-1,-1,Watershed_sagar.shp,Id,-1,-1;Order_ \”Order_\” true true false 50 Text 0 0 ,First,#,Drainage_Buf.shp,Order_,-1,-1;Shape_Leng \”Shape_Leng\” true true false 19 Double 0 0 ,First,#,Drainage_Buf.shp,Shape_Leng,-1,-1;BUFF_DIST \”BUFF_DIST\” true true false 19 Double 0 0 ,First,#,Drainage_Buf.shp,BUFF_DIST,-1,-1;Area \”Area\” true true false 13 Float 0 0 ,First,#,Watershed_sagar.shp,Area,-1,-1;Parimeter \”Parimeter\” true true false 4 Short 0 4 ,First,#,Watershed_sagar.shp,Parimeter,-1,-1”) # Process: Polygon to Raster (2) arcpy.PolygonToRaster_conversion(Drainage_Buffer_shp, “Order_”, Drainage_Raster, “CELL_ CENTER”, “BUFF_DIST”, “73”) # Process: Reclassify (3) arcpy.gp.Reclassify_sa(Drainage_Raster, “ORDER_”, “1 1;2 3;3 5;4 5;5 9;6 9”, Drainage_ Weight_Map, “DATA”) # Process: Slope arcpy.Slope_3d(DEM, Slope_Raster, “DEGREE”, “1”) # Process: Reclassify arcpy.gp.Reclassify_sa(Slope_Raster, “Value”, “0 1.2578274595971202 9;1.2578274595971202 4.7168529734892006 8;4.7168529734892006 9.433705946978403 8;9.433705946978403 15.09392951516546 5;15.09392951516546 29.558945300532383 5;29.558945300532383 57.231149411668817 3;57.231149411668817 66.664855358647202 1;66.664855358647202 76.098561305625736 1;76.098561305625736 80.186500549316406 1”, Slope_Weight_Map, “DATA”) # Process: Multiple Ring Buffer arcpy.MultipleRingBuffer_analysis(Lineament_shp, Lineament_Buffer_shp, “150;300;500”, “Default”, “distance”, “ALL”, “FULL”) “Ground Water Potential Modeling”

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Chapter-8 # Process: Intersect arcpy.Intersect_analysis(“’Lineament_Buf.shp’ #;’Watershed_sagar.shp’ #”, Intersect_ Lineament_shp, “ALL”, “”, “INPUT”) # Process: Merge (2) arcpy.Merge_management(“’Lin_Buf_Final.shp’;’Watershed_sagar.shp’”, Lineament_Bufferf_ shp, “FID_Lineam \”FID_Lineam\” true true false 9 Long 0 9 ,First,#,Lin_Buf_Final.shp,FID_ Lineam,-1,-1;distance \”distance\” true true false 19 Double 0 0 ,First,#,Lin_Buf_Final.shp,distance,-1,-1;FID_Waters \”FID_ Waters\” true true false 9 Long 0 9 ,First,#,Lin_Buf_Final.shp,FID_Waters,-1,-1;Id \”Id\” true true false 6 Long 0 6 ,First,#,Lin_Buf_Final.shp,Id,-1,-1,Watershed_sagar.shp,Id,-1,-1;Area \”Area\” true true false 13 Float 0 0 ,First,#,Lin_Buf_Final.shp,Area,-1,-1,Watershed_sagar.shp,Area,-1,-1;Parimeter \”Parimeter\” true true false 4 Short 0 4 ,First,#,Lin_Buf_Final.shp,Parimeter,-1,-1,Watershed_sagar.shp,Parimeter,-1,-1”) # Process: Polygon to Raster arcpy.PolygonToRaster_conversion(Lineament_Bufferf_shp, “distance”, Lineament_Raster, “CELL_CENTER”, “distance”, “71”) # Process: Reclassify (2) arcpy.gp.Reclassify_sa(Lineament_Raster, “VALUE”, “0 1;0 150 9;150 300 5;300 500 2”, Lineament_Weight_Map, “DATA”) # Process: Weighted Overlay arcpy.gp.WeightedOverlay_sa(“(‘Geomorphology’ 18 ‘VALUE’ (1 1; 3 3; 5 5; 8 8; 9 9;NODATA NODATA); ‘dd’ 8 ‘VALUE’ (1 1; 3 3; 4 4; 5 5; 7 7; 8 8; 9 9;NODATA NODATA); ‘Lithology’ 16 ‘VALUE’ (1 1; 6 6; 9 9;NODATA NODATA); ‘lulc’ 4 ‘VALUE’ (1 1; 2 2; 3 3; 7 7; 8 8; 9 9;NODATA NODATA); ‘soil’ 13 ‘VALUE’ (5 5; 6 6; 7 7; 8 8; 9 9;NODATA NODATA); ‘drainage’ 7 ‘VALUE’ (1 1; 3 3; 5 5; 9 9;NODATA NODATA); ‘slope’ 13 ‘VALUE’ (1 1; 3 3; 5 5; 8 8; 9 9;NODATA NODATA); ‘Lineament’ 21 ‘VALUE’ (1 1; 2 2; 5 5; 9 9;NODATA NODATA));1 9 1”, Groundwater_Potential_Map)


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table of contents

table of contents