development of a watershed management plan for amachal through ion using gis_report

SOORAJ KANNAN, P.V.

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL

THROUGH PRIORITISATION USING GIS

Development of A Watershed Management Plan for Amachal Through Prioritisation Using GIS

Project Report submitted in partial fulfilment of the requirements for the Diploma in

GEOINFORMATION TECHNOLOGY AND REMOTE SENSING APPLICATIONS

By Sooraj Kannan, P.V.

Centre for Environment and Development Thiruvananthapuram 2006

Sooraj Kannan, P.V.

DECLARATION

Me, Sooraj Kannan, P.V., hereby declare that the project entitled “Development of a Watershed Management Plan for Amachal Through Prioritisation Using GIS”

submitted to the Centre for Environment and Development, Thiruvananthapuram, in

partial fulfillment of the requirement for the Advanced Course in GeoInformation

Technology and Remote Sensing Applications with specialization in Spatial Planning is

a bonafide record of the work done by me under the supervision and guidance of Dr.T.

Sabu, Director (R&D), CED, and that no part of this has formed the basis for the award

of any degree, diploma or other similar titles of any University or Organization.

SOORAJ KANNAN, P.V.

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS i

Sooraj Kannan, P.V.

CERTIFICATE

This is to certify that the project entitled “Development of a Watershed Management Plan for Amachal Through Prioritisation Using GIS” submitted by Sooraj

Kannan,P.V. to the Centre for Environment and Development, Thiruvananthapuram, in

partial fulfillment of the Advanced course in Geoinformation Technology and Remote

Sensing Applications with specialization in Spatial Planning is an authentic record of

work carried out by him under our supervision at the Centre for Environment and

Development and that no part of this has formed the basis of any award of any degree,

diploma or other similar titles of any University or Organizations. We further certify that

he has completed all the assigned works and duties to our complete satisfaction and has

impressed me with his dedication and constant strive for excellence in work.

GUIDE CO-GUIDE

Countersigned by

EXECUTIVE DIRECTOR, CED

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS ii

Sooraj Kannan, P.V.

ACKNOWLEDGEMENTS

I wish to express my sincere gratitude to Dr. T. Sabu and Mrs. Sandhya S.N. for giving

me the opportunity to carry out the present study under their guidance. The help given in

many ways by Dr. T.Sabu, Director (R&D), CED, Thiruvananthapuram sparing his time

in between his high profiled assignments is gratefully acknowledged, although it is

insufficient to express my gratitude in a few words or sentences.

I am grateful to Dr.Babu Ambat, Director, Centre for Environment and Development for

providing necessary facilities during the study. The help and the encouragement by

various faculty members like Mr. Anil Kumar, Mr. Saharsh, B., Mrs. Aparna Simple

Sanal, Mr. Ravindran played a key role in achieving the fulfillment of the study and is

gratefully acknowledged. I also wish to express my sincere thanks to the staff of Centre

for Environment and Development for their help in need.

A special mention is given here to the staff of Department of Soil Conservation under

Government of Kerala for their help in collecting data and information of the study area.

SOORAJ KANNAN, P.V.

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS iii

Sooraj Kannan, P.V.

EXECUTIVE SUMMARY

A clear understanding of the processes with in a watershed is needed for watershed

development, utilization and management due to its complex nature of occurrence. The

integration of all natural resources on GIS platform will give enough scope for

understanding the inter-relationship between the available natural resources with in the

watershed. The present study provided a GIS methodology to prioritise the watershed

according to the severity and owners attitude for implementation of conservation

measures.

Thus the major objective of the study is the characterization and quantification of land

and water resource components of Amachal watershed at micro level for better

watershed development as well as management through prioritizing. And the specific

objectives are to study detailed geo-hydrology of Amachal watershed, computerize the

natural resources themes through GIS software application, evaluate geo-morphology

and terrain, integrate natural resources and land use planning for sustainable watershed

development and management and prioritise areas within watersheds for development

with available resources.

The natural resource based data of the Amachal watershed situated in

Thiruvananthapuram District of Kerala had been used for this study. The study area

belong s to Amachal ward and the part of Chandramangalam of Kattakkada panchayat

of Vellanad Block in Thiruvanathapuram district. The total geographical area of the

project area is 104.26 hectares with a cultivable area 92.93 hectares and cultivated area

of 92.28 hectares. The major crops in these micro- watersheds are coconut (59%) and

rubber (30%). Generally homestead farming is followed by families. Amachal –paithala is

the important stream in the watershed that debouches to the Neyyar River. The area is

characterized by moderate climate.

Runoff can be estimated using SCS curve number method. The curve numbers of each

type land cover can be calculated by overlying of soil theme and land use theme based

on Antecedent Moisture Condition (AMC) using Arc GIS environment. The rate of

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS iv

Sooraj Kannan, P.V.

erosion from the various parts of the watershed is found out using USLE model. Entire

watershed is classified into three regions of severe, moderate and slight erosion. Stake

holders’ attitudes are found out through surveys and given weights for each holding

based on that. Through overlay analysis using arcGIS the prioritisation map is generated

by assigning weights to each layers of consideration.

Triangulated irregular network of the area is generated from the contour data. Slope map

of the area is made and classified into gently sloping (0%-3%), moderate sloping (3%-

5%), strongly sloping (5-10%), moderate steep (10%-15%) and steep (above

15%).runoff map is formed using Soil Conservation Society Curve Number Method. The

runoff varied from 37 mm (loamy sand under tapioca and banana mixed cropping) and

101mm (sandy clay loam under paddy cultivation). Soil erosion severity is analysed

using Universal Soil Loss Equation and erosion map is generated. Stake holders’

attitude is represented as a map. Through overlay analysis prioritization map is

generated.

The out come of this attempt is in the form of a custom Arc GIS engine that integrates

multiple enterprise land information databases that would emerge as an effective

decision support system for integrated natural resource management to facilitate

prioritization for sustainable development. In nutshell, GIS can be successfully applied

to geographical data for the integration of collection, storage, retrieving, transforming

and displaying spatial data for solving complex planning and management problems in a

watershed under a time constraint.

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS v

Sooraj Kannan, P.V.

CONTENTS

EXECUTIVE SUMMARY iv

LIST OF FIGURES AND TABLES vii

LIST OF ABBREVIATIONS viii

CHAPTER - 1 INTRODUCTION 1

1.1 Background 1

CHAPTER – 2 OBJECTIVES OF THE STUDY 4

2.1 Major Objectives 4

2.2 Specific Objectives 4

CHAPTER – 3 STUDY AREA 6

3.1 Location 6

3.2 General information 6

CHAPTER – 4 REVIEW OF EARLIER WORK 8

4.1 Land Use Planning 8

4.2 Runoff and Soil Erosion 10

4.3 GIS in Watershed Management 15

4.4 Prioritisation of Micro-Watersheds 16

CHAPTER – 5 TECHNICAL PROGRAM 17

5.1 General Aspects of Basic principles of GIS 17

5.2 theoretical concepts of soil conservation 22

5.3 GIS Procedure for preparation and Analysis of maps 23

CHAPTER – 6 RESULTS AND DISCUSSION 40

6.1. Slope Region 40

6.2. Runoff map 40

6.3 Erosion Severity Map 43

6.4 Stakeholders’ Attitude 43

6.5 Prioritization 43

CHAPTER – 7 CONCLUSIONS AND SUGGESTIONS 47

REFERENCES 49

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LIST OF MAPS

FIGURE TITLE Pg. No. 5.1 Boundary and contour map 25 5.2 Cadastral map 26 5.3 Land use map 27 5.4 Soil texture map 28 5.5 Slope classes 30 5.6 Curve no map 31 5.7 Soil erodibility map 33 5.8 Length of slope map 35 5.9 Crop factor map 37

5.10 Land owners attitude map 38 6.1 Digital elevation model 41 6.2 Runoff map 42 6.3 Erosion severity map 44 6.4 Prioritisation map 45

LIST OF FIGURES

FIGURE TITLE Pg. No.

5.1 Boundary and contour map 29 5.2 Cadastral map 30

LIST OF TABLES

TABLE TITLE Pg. No. 3.1 Landuse classification of Amachal watershed 6 5.1 Curve number values 22 5.2 Feature classes in the dataset 24 5.3 Soil erodibility factor values 34 5.4 Crop management factor values 36 5.5 Weights for prioritisation 39

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS vii

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LIST OF ABBREVIATIONS

Abbreviations Interpretation cm centimeter

CN Curve number

Dept department

et al and others

Etc etcetera

Fig Figure

GIS Geographic information system

g gram

hr hour

ha hectare

HSG Hydrologic soil group

IS Indian Standard

km2 Square kilometer

m metre

m2 square metre

min minute(s)

ml millilitre

mm millimetre

N normality

pp page

SCS Soil Conservation Service

SYI Sediment Yield Index

t tonnes

Trans Transaction

USLE Universal Soil Loss Equation

WEPP Water Erosion Prediction project

Yr Year

< less than

> greater than

/ per

DEVELOPMENT OF A WATERSHED MANAGEMENT PLAN FOR AMACHAL THROUGH PRIORITISATION USING GIS viii

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CHAPTER 1 INTRODUCTION

1.1 Background

The concept of water management is undergoing a major turn by changing from site

specific or component specific quantification and utilization of water resources to holistic

approach. Finally, it has paved the way to a new school of thought of integrated water

recourses management by accurate spatial as well as temporal characterization and

quantification of system components in order to tackle the inherent stochasticity.

Presently the concept of integrated water management is gaining popularity globally at a

macro-level. But, it can also be practiced successfully at macro-level like watershed

management within a river basin, as this holistic approach is capable of increasing the

overall efficiency of natural resources utilization of a basin.

Watershed based developmental programs started to make a prominent appearance on

the development agenda in India in 1980s and 1990s. Watershed is considered as a unit

for integrated resource management, where management is not merely of land, water

and biomass, rather integration for self-reliance and holistic development of rural poor. It

is more community –based, than just technology oriented, leading to empowerment and

self-reliance of primary stakeholders. Managing these resources can provide an entry for

external agencies to understand poor people’s perception and for building their

capacities to reduce poverty and environmental degradation.

Though the Government with its intervention has developed some areas on watershed

basis but the area yet to be developed is still to a large extend. It is impossible for any

Government to take up area for development on watershed basis, since it involves huge

investment and efforts. Prioritization of watersheds plays a key role in identifying

watersheds, which need immediate attention, and those can be taken up for the

development with available resources.

This effort was undertaken to explore possibilities for adapting the GIS-RS capabilities

for local level planning and development .Total decentralization of plan formulation in

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Kerala has given opportunity to the stakeholders to directly participate the

developmental activities. The Panchayats committees are empowered to formulate

projects depending on the local needs. Therefore it is required to equip the Panchayats

with databases sufficient to meet their data need for projects formulation. In order to

integrate socio-cultural indicators into an ecological model, it is necessary to identify

social stratification within land use patterns and determine social differentiation within

watershed communities or neighborhoods. Particularly in a situation like the one exists in

Kerala, where idiosyncrasies monsoon and diverse physiographic conditions give rise to

unequal distribution of water. Unlike other peninsular states of India, Kerala has some

unique land form related aspects such as: over 90% of the geographical area is either in

midland or highland category, nearly 70%of the surface area is carpeted with more than

1.5m soil profile, 60%of the surface cover literate etc. Similarly, due to effective

implementation of land ceiling average small land holding category (<1 hectare) makes

up 91.5% whereas, in the rest of India it is 58.1%. Therefore, the land and water

conservation measures, which are being practiced successfully elsewhere not

necessarily yield same success rate in Kerala. Hence, in order to get the acceptance

and cooperation of the stakeholders, it is imperative to adopt site-specific conservation

measures, which are more suitable for Kerala’s socio-econo-cultural environment.

Augmented by deforestation, soil erosion/ runoff, and rising demand leading to

unsustainable use is one of the major contributing factors to drinking water scarcity and

poor agricultural yields in some districts of Kerala. Given the nature of monsoon rainfall

in Kerala, the key to meet the district’s growing demand for water for domestic and

agricultural use is to effectively harness rainwater, the ultimate source of all freshwater

resources. Artificial recharge of ground water seems to be the only corrective measure

to compensate for the overexploitation of groundwater by augmenting the natural

infiltration of rainwater or surface water into under-ground formations through various

methods. Artificial recharge may be induced by number of methods depending on the

local topographic, geologic and soil conditions.

It has been learned that the key social variables, even in subtle nuances, influence the

success of watershed based developmental initiatives, their sustainability and

subsequently the overall rural development scenario in Kerala. Moreover, they could

establish a positive correlation pattern between the sociological motivation of rural and

peri-urban stakeholders and the sustainable natural resource development. Whereas,


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the stakeholders, right from assistant development commissioners at district

headquarters up to village extension officers at Panchayats, are insensitive to this

synergy .This is mainly due to their inability to integrate appropriately weighed multi-

criteria datasets while formulating action plans. This situation had warranted the need for

a simple but technically sound computer based decision support system.

The font end has to be designed using GIS so that the mid-level planners and

executives of the Kerala State Planning System such as Block Development Officers,

the project officers of District Rural Development Agencies can effortlessly estimate

actual area to be occupied by the rain pits or bunds in a plot. It will be not only

advantageous for the planners but also to the social engineers to motivate the farmers

and built congenial environment for soil and water conservation programme. This was

made possible by inclusion of the underlying sets conditions and algorithms into the GIS

topological data structures. This module can be called directly into the GIS interface

(without extensive reprogramming) to work on spatial data inventory and instantly

provide the ‘what if’ functionality that is so much at the heart of the DSS concept.

A clear understanding of related processes within a water divide is needed for watershed

development, utilization and management due to its complex nature of occurrence. The

integration of all natural resources on GIS plat form will give enough scope for

understanding the inter-relationship between the available natural resources within the

watershed.

Geographic information system (GIS) is a computerized database management system

for capture, storage, retrieval, analysis and display of spatially referenced defined data.

GIS is very powerful tool for spatial planning and resource management. The present

study is also an attempt to utilize the advance in information technology and internet for

participatory developmental planning.


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CHAPTER 2

OBJECTIVES OF THE STUDY

2.1 Major Objective

Kerala is facing serious problems of soil erosion due to its undulating topography. The

implications of soil erosion in the study area extend beyond the removal of valuable

topsoil. The loss of soil from farmland may be reflected in reduced crop production

potential, lower surface water quality and damaged drainage networks. Crop

emergence, growth and yield are directly affected through the loss of natural nutrients

and applied fertilizers with the soil. Soil quality, structure, stability and texture are

affected by the loss of soil. The breakdown of aggregates and the removal of smaller

particles or entire layers of soil or organic matter weaken the structure and even change

the texture. Textural changes in turn affect the water-holding capacity of the soil, making

it more susceptible to extreme condition such a drought. Soil erosion is not always as

apparent as the on-site effects. Eroded soils, deposited down slope inhibit or delay the

emergence of seeds, bury small seedling and necessitate replanting in the affected

areas. Also these problems results in the violation of the socio economic structure of the

area there by creating a social imbalance.

A detailed study taking into consideration of the various aspects of the problem and with

a systems approach has to be carried out, to formulate a viable strategy for

management of soil erosion in this area.

Thus the major objective of the study is the characterization and quantification of land

and water resource components of Amachal watershed at micro level for better

watershed development as well as management through prioritizing.

2.2 Specific objectives

The specific objectives include:

1. To study detailed geo-hydrology of Amachal water shed

2. To computerize the natural resources themes through GIS software application

3. To evaluate geo-morphology and terrain modelling


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4. To integrate natural resources and land use planning for sustainable watershed

development and management

5. To prioritise areas within watersheds for development with available resources.


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CHAPTER 3 STUDY AREA

3.1 Location The identified area of Amachal model Watershed projects is falling within Latitude 8 0

28’57”to 8 0 29’44” North and Long.770 6’26”to 770 7’16” East. Administratively, the study

area belong s to Amachal ward and the part of Chandramangalam of Kattakkada

panchayat of Vellanad Block in Thiruvanathapuram district

3.2 General information The total geographical area of the project area is 104.26 hectares with a cultivable area

92.93 hectares and cultivated area of 92.28 hectares 3% of the total land area is

accounted as “other fallow and current fallow”. Table 3.1 gives details of the landuse

classification of the Amachal watershed.

Table 3.1 Land use Classification of Amachal Watershed

Category Area(hectare) Percentage to total

geographical area

Building and courtyard

Non-agricultural landuse

Net sown area

Current fallow

Other fallow

Cultivable waste

Other Miscellaneous trees

7.99

3.34

89.64

2.64

0.41

0.09

0.15

7.66

3.20

85.98

2.54

0.39

0.09

0.14

Total geographical area 104.26 100.00

The major crops in these micro- watersheds are coconut (59%) and rubber (30%).

Generally homestead farming is followed by families.

Amachal –paithala is the important stream in the watershed that debouches to the

Neyyar River. The major ponds in the watersheds are Paithalakulam, Mathrakulam,

Shappukulam, Melechirakulam The rain distribution of the area is bimodal with two well

defined raining seasons namely, south west monsoon (from June to September)


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followed by the Northeast monsoon (from October to November).The area is

characterized by moderate climate with the monthly average maximum and minimum

temperature ranges from 28.40 C to 35.50 C and 21.50 C to 25.60 C, respectively. The

mean monthly maximum and minimum relative humidity are 88 percent (in July) and 54

percent (in February) respectively. An annual average 6.25hours of sunshine are

received with a decrease in sunshine hours from June to August and an increase from

December to April.


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CHAPTER 4

REVIEW OF EARLIER WORKS

4.1 Land Use Planning The risk of soil erosion by water varies as a function of many factors, but the degree of

protection provided by vegetation is the most important. Agricultural Resource

Management has acquired a new dimension with the development of space technology.

Several satellites were put in the orbit and continuously monitoring the dynamic and

complex agricultural and environmental system and agricultural land use have been

some of the major applications in agriculture. Through GIS and ancillary information

regarding crop phonology, soil type, field parcelling, agronomic management etc. can be

incorporated along with remote sensing data for improved analysis of crop and

agricultural land use inventory and monitoring.

Csornai et al. (1990) suggested a GIS based image classification procedure for

improved crop identification and acreage estimation over a large area in Hungary. The

methodology adopted in the creation of digital field boundary data and GIS supported

classification methods.

Rao et al. (1991) reported that remote sensing application with IRS-1A LISS-1 data

helped generation of district wise land use/land cover maps for whole country on

1:250,000scale to serve the requirement of agro climatic zonal planning commission of

Government of India.

Theocaropoulose et al. (1995) reported how a soil survey land evaluation project

benefited from the adoption of GIS technology. They reported that the use of GIS

through the capabilities of data storage, processing and presentation could assist with

such environment management tasks as land use planning, policy formulation and

maintenance of land resources, environmental research and monitoring. It is also


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observed that there was reduction uncertainly in the selection of land use strategies and

additional benefits are derived from decision based on more relevant informations.

Harris et al. (1997) in their study, land cover maps created as a part of a riparian

restoration research project were used to compare the time-costs involved in calculating

lands cover areas with GIS and manually with a planimeter and dot-grid. Sample

modelling of riparian forest resource potential was also performed to investigate the GIS,

value for restoration planning.

Wu et al. (1997) evaluated the soil properties of CRP (Conservation Resource

Programme) land using remote sensing and GIS in Finney County, Kansas. Suitable

images were used to derive a land use/land cover map. The map was incorporated with

computerized soil survey information system. Soil property characteristics and

readability index for each land use type were analyzed. The study demonstrated the

advantage of GIS and remote sensing for evaluating the CRP.

Boyle et al. (1998) used Geographic Information System technology to develop

automated methods for quantifying the special variability of flood hazard and land use

impact assessment in flooding conditions. An interface module developed within GIS

incorporated floodwater elevations predicted would provide water resources managers

with improved insight into flooding conditions, strengthening the risk assessment

process and the administration of human activities in river flood plains.

Das (1999) explained the role of soil information System (SIS) in sustainable use of land

resources. He stated that the SIS based on database obtained through remote sensing

and ground survey in combination with GIS and decision support system (DSS) have

immense potential in planning, judicious management, conservation and sustainable use

of soil, land and crop resources.

Karnchanasudhan (2002) applied GIS for the analysis of rise suitability area in Thailand

by overlying of soil map, rainfall map and irrigation map. The undesirable areas such as

conserved forest, urban areas, water body etc. were removed. Rice suitability area was

then categorized based on the boundary of 25 micro watersheds, into three levels; high,

moderate and marginal suitability areas for rice.


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Pradhan (2002) developed and rested a GIS and remote sensing based methodology for

the land cover mapping of Bhutan and Nepal using IRS data from the analysis, broad

leaf forest and coniferous forest has been found as the dominant land cover classes.

The methodology will work well and is recommended to use at a watershed level using

medium or high-resolution satellite data.

Sharma et al. (2002) reported that the information obtained through remote sensing and

GIS techniques helps in the better understanding of geographic locations, distribution of

quality of land in the watershed and prioritization of critical areas for the soil and water

conservation treatment. It was also evident that thematic maps with relevant attribute

data could help in efficient land use planning for the optimal use of land. To obtain the

integrated/ composite information, maps can also be integrated step wise, two at a time

through GIS techniques. The spatial database thus generated is very helpful to the land

users and land use planners in the demarcating suitable and unsuitable land.

Rajalakshmi and Dutta (2004) assessed the changes in runoff due to land use change in

hydrological basins. The distributed hydrological modelling is attempted considering the

spatial variability using remote sensing, and GIS. The study area consisted of three

major river basins of India such as Mahanadi, Godavari and Brahamani-Baitarani. It was

found that nearly 60% of the sub-basins were affected, when there was a change of 30%

of the rice agriculture area to minor agriculture area. Where as 5%, 10%, 15% change in

forest cover to minor agriculture or rice agriculture affected the same number of the sub-

basins considering the change in the runoff greater than 5% for a sub-basin.

4.2 Runoff and Soil Erosion Reliable run off and soil loss estimation is a valuable design and planning tool. Its most

immediate advantage is that a well-defined conservation objective can be formulated, to

reduce soil losses to specified acceptable level and there by ensure the maximum safe

economic use of each piece of land. For locating vulnerable and priority areas, the

catchment of a river has to be studied for different types and intensities of erosion and

mapping different erosion units.


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Wischmeier (1959) found that one hundreds of the products of the kinetic energy of

storm (KE) and 30 minutes intensity (I30) are the most reliable single estimate of rainfall

erosion potential and was termed as El30. Annual total of storm El30 value is referred to

as rainfall erosion index.

Wischmeier and Mannering (1969) initiated studies to see as to which extent various

properties of soil affect its erodobility. The significant variables percent were sand,

percent silt clay ratio, organic matter content, aggregation index, antecedent soil

moisture, bulk density percent slope, pH of surface and sub soil, soil structure, thickness

of soil layer , land use preceding three year period, volume of pore space filled by air,

slope shape, presence or absence loessial mantle and clay skins on pod surfaces. A

multiple regression equation was developed based on various soil properties and their

interaction. The equation is so cumbersome and requires the determination of so many

properties so it is not used extensively. Work was again carried out to simplify the

procedure for determination of K and a simple nomograph based on five soil parameters

have been developed.

The USLE is an erosion model design to predict the long time average soil from a

specified cropping management system. With appropriate selection of numerical values

for various soil erosion variables, the equation will compute the average soil loss for a

cropping system, for a particular crop year in a rotation or a particular crop stage period

within a crop year. It computes the soil loss for given site as a product of six major

factors where most likely values at a particular location can be expressed numerically

(Wischmeier and Smith, 1978).

Wischmeier and Smith (1978) simplified the procedure for determining the L and S

factors combining the L and S factors together by considering the two as single

topographic factors and a nomograph for determine LS factor was developed for

convenience. The information’s given on C value may be made use of after carefully

considering the cropping pattern, crops, cropping stages and crop management

variations in the United States and in India And making suitable adjustments for these


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variations. Based on the intensive studies they have recommended P value for a number

of situations.

Onstad and Otterby (1980) studied the effect of crop residual on runoff. Crop residues

on the soil surface decrease runoff from all storm sizes and eliminate runoff from small

storms. Runoff reductions and consequent increases in soil water storage are greatest

on less permeable soils.

Spanner et al. (1983) first demonstrated potential of GIS erosional soil loss assessment

using RS and GIS techniques for quantitatively assessing erosional soil loss.

Borah (1989) developed a dynamic hydrologic model, which simulates spaces and time

distributed rainfall excess runoff in a watershed resulting from single rainfall.

Pathak et al. (1989) developed a runoff model for small watersheds in the semiarid

tropics. A modified SCS runoff model and a soil moisture accounting procedure were

used to simulate runoff for small watersheds and validity was tested in small vertisole

watersheds at ICRISAT in India.

Fook et al. (1992) made use of remote sensing techniques and GIS for soil erosion

mapping. Erosional soil is most frequently assessed by Universal Soil Loss Equation.

Fernandez and Garbrecht (1994) studied the effect of trends and long-term fluctuations

of rainfall on watershed runoff at Little Washita river basin. It shown that, rainfall patterns

and amounts can mask the beneficial impacts of floodwater retarding structures.

Binger (1996) simulated runoff from Goodwin Creek watershed SWAT (Soil and water

Assessment Tool). SWAT has predicted the relative trends of runoff on a daily and

annual basis from multiple sub basins, Except for a completely wooded sub basin.

Desmet and Govers (1996) made a computer algorithm to calculate the USLE and

RUSLE, L, S factors over a two dimensional landscape. A comparison of manual

calculation and a computer algorithm showed that the manual method leads to an under

estimation of erosional risk. The computer procedure has the obvious advantage that it


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can be easily linked to GIS software. If data on land use and soils are available specific

K, C and P values can be assigned to each land unit so that predicted soil loss can then

be calculated using a simple overlay procedure.

Montas and Madramootoo (1996) developed and used a model named ANSWERS to

predict runoff and soil loss in south western Quebec in to small agricultural watersheds.

This model underestimated the sediment yield for all events. Runoff predictions with

adjusted parameters were better than those with measured parameters.

Formaggi et al. (1998) used Universal Soil Loss Equation (USLE) for soil erosion

modelling. The rest site was watershed in region of highly intensive agriculture in Sao

Paulo State (Brazil) and GIS/remote sensing techniques were employed to spatialize the

soil erosion losses by water. They have tested different approaches of modelling the

USLE topographic parameters ”L” and ”S”. The result showed that for ”s” parameters

there was not statistical difference, in the final spatialized results, showing the need of

improve the methods of modelling the USLE most impacting parameters.

Sarangi et al. (2001) studied the use of GIS in assessing the erosion status of the

watershed. Two watersheds viz. Banha watershed at Damodar valley, Jharkand and

IARI watershed at Delhi are considered for hypsometric analysis. The hypsometric

analysis revealed that the Banha watershed is less susceptible to erosion where as IARI

watershed is at stabilized state. This findings point out the need for conservation

measures in Banha watershed for controlling further erosion through construction of soil

conservation structures.

Sikka and Birosy (1997) used the USLE to estimate the soil loss from the 338 on 10km X

10 km grid distributed over entire state of Kerala in India. Parameters of USLE were

worked out by synthesizing information on rainfall, soil topography, land use and

management practices for each grid. The computed values for soil loss were grouped

into six classes. The result showed that major portion of Kerala falls in 0-5-ton/ha year-

soil loss category while less than 5 percent of an area is subjected to serve from of soil

erosion. Small areas contribute to soil loss greater than 40 ton/ha/year. About 40 percent

of state subjected to soil loss in the categories of 5-10and 10-15 ton/ha/year.


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Sooraj Kannan, P.V.

Joshi et al. (2004) conducted studies at Bhetagad watershed to assess the erosion

losses under open pine forest, tea garden, rain fed agriculture land and grazing lands.

The result revealed that soil in terraced agriculture land was more stable than that in

pine forest, tea gardens and g4razing lands. The maximum water, soil and nutrient

losses are recorded from areas covered with open pine forest and minimum for

agriculture land. The well maintained agriculture land had higher conservation values of

water, soil and nutrients than the other land use system.

4.2.1 Rainfall runoff prediction model Jayasree (1990) conducted regression and correlation studies on Chaliyar basin to find

the relationships between rainfall and runoff of the sub-basin and prediction equations

have also been found out.

Steenhius et al. (1995) revised the SCS runoff equation for variable source runoff, for

two watersheds in Australia and three in northeastern United States. By plotting the

effective precipitation against observed runoff for the above watersheds they found that

the SCS curve number equation in it’s elementary from fitted the data well.

Mimikov and Baltas (1996) conducted a study in central Greece and a rainfall runoff

model for flood flow forecasting using mean annual rainfall and annual aerial radar

rainfall information was obtained by using the unit hydrograph approach. They observed

that the model gave better results when radar processed weather data are given as

input.

Kothuari and Singh (1999) developed a multiple input and single output time invariant

non-linear model based on a black box system approach using daily data and it was

used for flow forecasting during monsoon flood events.

Saravanan and Sudharsanan (2004) followed a lumped modelling approach for

modelling flood events of Valliyar in Tamilnaduu using remote sensing and GIS. Flow is

estimated for a minimum, maximum and average rainfall events and the estimated runoff

is compared with observed stream flow measurements.


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Sooraj Kannan, P.V.

4.3 GIS in Watershed management Watershed is an area of land that is drained by network of stream or river and separated

from other watersheds by ridge top boundaries. Often called as drainage basin or

hydrologic unit, watershed can cover large multistage area or relatively small area. A

digital representation of the continuous variation of relief cover space is known as Digital

Elevation Model (DEM).

Green and Cruise (1995) constructed a geographic information system for an urban

watershed in Bata Rouge, Louisana and used to direct a hydrologic modelling effort for

watershed management. The locational data were also used to determine dimensions of

the HRU’s as well as all flow lengths. The curve number method was used to determine

dimensions of the HRU’s as well as flow lengths. The curve number method was used to

determine rainfall excess and the discharge was routed using a standard kinetic wave

model. System capabilities are demonstrated the lot, polygons, block and multi block

scale.

Garbrecht et al. (2001) described the GIS and distributed watershed and models, which

addresses selected spatial data issue, data structures and projections, data sources,

and information on data solution and uncertainties. Spatial data that are covered include

digital elevation data, stream and drainage data, soil data, remotely sensed data and

radar precipitation data.

Pandey et al. (2004) developed the DEM of Banikduh agricultural watershed using

ARC/INFO GIS software from contour map. Flow direction and flow accumulation

themes were developed using depression less DEM. Topographical parameters and

stream properties relating to land surface watershed were extracted. The DEM and

associated parameters derived from their study may be successfully used for simulation

of runoff and sediment yield from Banikdih watershed for planning of best management

practices.


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Sooraj Kannan, P.V.

4.4 Prioritization of Micro watersheds Aravind et al. (2004) attempted to prioritize sub watersheds for a few taluks in Karnataka

state considering seven criteria viz. silt yield index, SC/ST population, wasteland,

agricultural labourer population, forest area and irrigated area. Specific weightings were

assigned to each criterion in terms of marks. Data pertaining to all the parameters were

converted into a spatial domain (Map format) as cove rages and were fed as input into

the prioritization module developed by using Arc Macro Language (AML).The resultant

maps indicating the priority numbers for all the sub watersheds for a given taluk were

generated and submitted to the Watershed development department for implementation.

The outputs also comprise maps and tables for individual criterion selected for

prioritization, which may be used for any other projects, provided the criteria selected

should be within these criteria. Thus remote sensing and GIS when clubbed together

produce synergetic results and can be successfully adopted to prioritize the watersheds

in a more scientific and unbiased manner.


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Sooraj Kannan, P.V.

CHAPTER 5 TECHNICAL PROGRAMME

5.1 General Aspects and Basic Principles of GIS

The design and implementation of a specific GIS project include many considerations and

processes. However a series of logical steps are required to be followed in the execution of a

GIS project.

1. Building and designing a geodatabase

2. Data representation models

3. Spatial data input and data acquisition

4. Edit and topology creation

5. Attribute data input

6. Map projection

7. Management and manipulation of data

8. Presentation of results of analysis

i) Building Geodatabase

Geographic database in short Geodatabase, represents geographic features and

attributes as objects and is hosted inside a relational database management System. A

geodatabase is a modern container for GIS data. It is a next generation, Object-

relational Geographic data model. Geo databases are supported in all of the current

ARC GIS Desktop Products.

Feature datasets

Feature datasets exists in the Geodatabase to define a scope for a particular spatial reference.

All feature classes that participate in topological relationships with one another must have the


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Sooraj Kannan, P.V.

same spatial reference. Feature datasets are a way to grow feature classes with a same

spatial reference so that they can participate in topological relationships with each other. They

also have a natural organizational quality, much like a folder on a file system.

Feature classes are collections of features of the same type with a common spatial

representation and set of attributes. Each feature class stores a set of points, lines, arcs,

polygons or annotations (text). They can have topology which determines the

relationship between the features. To define the features more than one feature class is

often required.

ii) Data Representation Models

In Geographical Information System, data can be modeled in three basic ways

• As a collection of discrete features in vector format

• As a grid of cells with spectral or attribute data (Raster format)

• As a set of Triangulated points modeling a surface

Vector Data Model

Vector data represents features as points, lines, and polygons and is best applied to

discrete objects with defined shapes and boundaries. In vector data format features

have a precise shape and position, attributes, metadata and useful behavior.

The vector data model represents geographic features similar to the way maps do.

Points represent geographic features too small to be depicted as lines or areas, lines

represent geographic features too narrow to depict as areas, and areas represent

homogenous geographic features. An x, y (Cartesian) coordinate system references real

world locations.

Raster Data Model

The raster data model is made up of a regular grid of dots (called cells or pixels) filled

with values. It represents imaged or continuous data. Each cell or pixel in a raster is a


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measured quantity. The most common source for a raster data set is a satellite image or

aerial photograph. It can also be a photograph of feature such as a building. The raster

data sets is useful in storing and working with continuous data such as elevation, water

table, pollution concentration , ambient noise level etc

Triangulated Irregular Network data Model (TIN Model)

Triangulated Irregular Network data Model is an alternative tot the raster data model for

representing continuous surfaces. It allows surface models to be generated efficiently to

analyze and display terrain and other types of surfaces. A TIN is a useful and efficient

way to capture the surface of a piece of land. TIN supports perspective views. It is

possible to drape a photographic image on the top of a TIN for a photorealistic terrain

display. TINs are particularly useful for modelling watersheds, visibility, line of site, slope,

aspect, ridges and rivers, and volumetric. TIN can model points, lines and polygons.

Contour maps can be generated from a TIN using linear interpolation for a smoothing

algorithm.

The vector, raster and TIN data models are powerful ways to model the Earth. Each of

them uses the Cartesian coordinate system for defining the location on the earth

surface. Adopting a common map projection and scale and adjusting coordinates so

that each model shares a common origin, ensures that the same coordinates represents

the same location in each model. This is called Georeferencing and is important

because it allows selecting the optimum data model for representing a particular aspect

of the earth. It also provides greater flexibility for analyzing and displaying data.

iii) Spatial data input

Spatial data input is one of the major prerequisite for GIS methodology. Two basic

options involved in dbase construction are (i) use the existing data and (ii) create new

data. New GIS data can be created from various sources such as satellite images,

GPS data or different types of paper maps such as Geographical Top sheets, Survey

maps etc. The paper maps can be converted into digital maps through digitizing


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process. A newly digitized map has measurement unit as the source of map and

therefore it must be converted into real world coordinates by using a set of control

points with known real world coordinates. The following steps are required for spatial

data input as part of GIS analysis.

Data Acquisition

Data acquisition is one of the most critical time consuming part of GIS analysis. Design

of data base include location of study area, relevance of co-ordinate system used, data

layers required, feature representation of each layer, attributes of each feature type and

the way in which information is coded and organized in the attribute file.

iv) Defining Topology

Topology, as used in GIS expresses explicitly the spatial relationship between different

features in a geodatabase. In Geographical Information System technology, topology is the

model used to describe how features share geometry; it is also the mechanism for

establishing and maintaining topological relationships between features. ArcGIS implements

topology through a set of validation rules. These rules define how features may share a

geographical space, and a set of editing tools that work with features that share geometry in

an integrated fashion. A topology is stored in a geodatabase as one or more relationships

that define how the features in one or more feature classes share geometry.

Data editing and correcting

Data editing and correction is done efficiently using topology classes. In the Arc Map the red

polygon and lines marked the places where the topology rules are violated. The dirty

areas allow selected parts, rather than the whole extend of the topology to be validated

after editing. Dirty areas are created when a feature is created or deleted, a feature’s

geometry is modified, a features sub type is changed or versions are reconciled, or

when the topology properties are modified. Dirty areas are removed when they are

validated


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Sooraj Kannan, P.V.

v) Attribute data

Attribute data can be incorporated into the vector features. By selecting the layer -open

attribute table- options add field, and the attribute data can be entered as short integer,

float, text etc. The data relevant to the spatial data are stored in the attribute table of the

feature class which gives valuable information on various features stored in the feature

class.

vi) Map projection

Cartesian coordinate system has properties that make them useful for representing real

world coordinates on maps .projection formulas are mathematical expression which

convert data from a geographical location on a sphere or spheroid to a representative

location on a flat surface. This process inevitably distorts at least one of these properties

–shape, area, distance, direction and often more. Because measurements of one or

more of these (distorted) properties are often used to make decisions ,anyone who uses

maps as analytical tools should know which projections distort which properties , and to

what extent.

vii) Management and Manipulation of Data

A GIS integrates spatial and other kinds of information within a well defined database

structure and provides software tools that can be used to manipulate and display

geographical data-objects. Most GISs are graphically oriented, with display and map

output capabilities ranging from cartographic displays, spatial imagery and 3-D overlays,

to graphs and histograms of data or statistical investigations. Additionally, the integrated

data structure and standardized "tool kit" of GIS functions provides useful capabilities to

aid in data exploration, data intercomparison, spatial and temporal overlay studies, and

more complex analyses of multiple datasets in space and time. One of the most

important current areas for growth in GIS is error analysis - an important factor in the use

of multi-thematic data combinations in analysis or modelling.

A GIS is a working environment representing an analytical philosophy that is established

by the combination of geographical data and information objects (in the database) and


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Sooraj Kannan, P.V.

an organized set of data management, analysis, exploration, and descriptive modeling

tools. The introduction of GIS has revolutionized the way the scientific world views and

manipulates geographical information. Of particular interest here are the advantages that

GIS methods may provide for quality assessment and analysis of data, and for the

derivation of useful information from static data modeling (data combinations and

derivations).

5.2 Theoretical concept of Soil Conservation 5.2.1 Estimation of runoff

SCS curve number method was adopted to estimate the daily runoff. The mathematical

equations involved in the method were described below. Let Ia be the initial quantity of

interception, depression storage and infiltration that must be satisfied by any annual

rainfall before runoff can occur. The actual runoff, actual retention and the storage

capacity S are related by

Q= (P-0.2s)2

P+0.8 S

Q has the same units of P and is usually expressed in mm.

The curve number has defined by the United States SCS is defined by

CN = 25400

254+S

Where, S is the recharge capacity of watershed in mm.

Curve numbers for different land the conditions and hydrologic soil groups for

Antecedent Rainfall Condition II are given in the table 5.1.

Table 5.1 Curve Number Values

HSG LAND USE C No. A 55 B 61 C 68 D

coconut

75 A 59 B 64 C 70 D

coconut predominant mixed cropping

74


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HSG LAND USE C No. A 38 B 53 C 73 D

rubber

76 A 66 B 71 C 72 D

Rubber predominant mixed cropping

78 A 61 B 67 C 75 D

Tapioca Banana mixed cropping in wet land

81 A 63 B 71 C 83 D

Paddy

86

5.2.2 Estimation of annual soil loss The Universal Soil Equation (USLE) is given by:

A=R.K.L.C.P

Where A=the average soil loss for the given period

R=rainfall erosivity index

K=soil erodibility factor

C=crop management factor

L=length of slop factor

S=steepness of slop factor

P=conservation practice factor

Extensive Experimental evidence is needed to determine these factors.

5.3 GIS procedure for preparation and Analysis of Maps Various types of data products, literature and survey study were used to generate the

maps for the Integrated Watershed Management Through prioritization of Amachal

watershed. The western Ghat Development Cell under the planning and Economic

Affairs Department, coordinating the Amachal model Watershed Programme has

generated a number of thematic maps such as landuse, land capability, land irrigability,

crop suitability, assets, soil characteristics etc. at survey plot level. A socio-economic


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Sooraj Kannan, P.V.

survey report of the watershed also has been published by the Department of Economic

and Statistics, Government of kerala in 2003.

The Ancillary/Collateral data collected are Hydrological and climatological data,

Cadastral Map (fig. 5.2), Soil (fig. 5.4) and land use data (fig. 5.3), Drainage,

Elevation/Contour data (fig. 5.1) and Socio-economic data (Optional)

Arc GIS 9.0 was the software was used for the study. ArcGIS 9.0 presents a

comprehensive set of analysis tools that work with all the supported data formats,

including geodatabase features. It also offers a completely new framework for working

with these tools that combine them together in a visual modelling environment and apply

scientific principles related to the study.

In this study GIS is applied to the geographical data for integration of collection, storing,

retrieving, transforming and displaying spatial data for solving the planning and

management problems. GIS made the data handling and analysis much easier with

meaningful research outcomes. As part of the study a geodatabase ‘Watershed’ was

designed which contained a data set ‘Amachal’ comprising of different feature classes.

The details are given in table 5.2. Projection of the study area was given as

UTM_1984_Polyconic.

Table 5.2 Feature classes in the dataset

SI.No. Feature class Type Details of the features

1 Boundary Polygon Boundary of the area

2 Land use Polygon Land use classification

3 soil Polygon Soil type classification

4 Contour Polyline Contour of the area

5 Streams Polygon Stream lines of the area


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Sooraj Kannan, P.V.

Spatial analyst extension of Arc GIS was used for the analysis of the study. Spatial

Analysis helps to identify trends on the data, create new relationships from the data,

view complex relationships between data sets, Make better decisions. Spatial Analyst

provides a rich set of tools to perform cell-based (raster) analysis. Of the three main

types of GIS data, (raster, vector, and tin), the project utilized the raster data structure

which provided the most comprehensive modelling environment for spatial analysis.

1. Slope map generation: Slope regions were derived from contour base map. By

using 3D analyst of arcGIS the triangulated irregular network (TIN) is generated from the

contour feature class and slope is generated from TIN in raster form. Slope regions have

been reclassified into gently sloping (0%-3%), moderate sloping (3%-5%), strongly

sloping (5-10%), moderate steep (10%-15%) and steep above (15%). (Fig 5.6) 2. Runoff Map: Runoff was estimated using SCS curve method. This was done by the

union of hydrological soil group map and land use map. Hydrological soil group map is

generated from soil texture map. Hydrological soil group gives an idea about the

infiltration capacity of the soil which in turn depends on the textural characteristics of the

soil. Later assigning Curve Number based on land, use, hydrological soil group and

antecedent moisture condition in the union of texture map and land use map, curve

number map is generated (fig 5.7). Based on curve number runoff is calculated and

represented in map form. The procedure for the preparation of runoff map is detailed in

the flow chart.

Figure 5.1 Runoff Map Generation


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Rainfall value of 15cm was obtained for the study area when limiting to 50 years

frequently and 6 hour duration. The runoff varied from 37 mm (loamy sand under tapioca

and banana mixed cropping) and 101mm (sandy clay loam under paddy cultivation).

3. Erosion Severity Map: Soil erosion severity was analysed using the Universal soil

loss equation. USLE is formulated for the gently sloping agricultural areas so that it

cannot be used for the exact estimation of soil loss in the steep sloppy watersheds of

kerala. As our objective is to demarcate the relative severe erosion areas with in the

watershed than exact estimation of the soil loss, this simplest model can do the best.

Maps for each parameters of USLE are prepared and rasterised. Using raster calculator,

the soil loss is estimated by multiplying all the parameter maps. GIS procedure for the

preparation of erosion severity map is given in the figure.

Figure 5.2 Erosion severity Map Generation

1. Estimation of Erosivity Factor (R): The rainfall erosivity factor (R) is the

number of rainfall erosion index unit for a particular location. R is the rainfall and

run off factor by geographic location. The greater the intensity and duration of

rain storm, higher the erosion potential. Erosivity factor for the study area can be

taken as constant throughout the watershed area, since there is no spatial

variation of rainfall characteristics with in this small watershed.

2. Preparation of Erodibility Map (K): The soil erodibility factor (K) in the USLE

relates to the rate at which different soil erode. A simple nomograph developed

(Wischmeier et al. 1971) based on the five soil parameters viz. percent silt plus


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Sooraj Kannan, P.V.

very fine sand, percent sand greater than 0.10mm, organic matter content,

structure and permeability was used to found the Soil Erodibility (K) factor. K is

the average soil loss in tones /acre per unit area for a particular soil in cultivated,

continuous fallow. K is a measure of susceptibility of soil particles to detachment

and transport by rainfall and runoff. The appropriate K value is determined from

standard tables based on texture of soil. Based on the k values, the soil map was

converted to raster map using spatial analyst (fig. 5.9).

Table 5.3 Soil Erodibility Factor Values

soil texture k factor

gravely clay loam 0.27

gravely sandy clay 0.23

gravely sandy clay loam 0.28

gravely sandy loam 0.07

loamy sand 0.04

rocky stony 0.02

sandy clay loam 0.30

sandy loam 0.13

silt loam 0.38

3. Preparation of Slope Map: Contour map of Amachal Watershed was prepared

by level survey. Contour interval was 5m. The maximum contour value of the

area was 95m. Slope of the study area was prepared using the 3D analyst

extension of Arc GIS. The slope of the area can be seen clearly in the TIN map.

The TIN of the study area is prepared using the 3D analyst one of the powerful

extension of Arc GIS.

4. Topographic Factor (LS): Slope length (L) is the distance from the point of

origin of over land flow to the point where either the slop gradient decreases

enough that deposition begins or the runoff water enters well-defined channels.

S is the slope steepness factor. L and S factor together considered as

topographic factor. Average length of slope is taken as 100ft and LS factor raster


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Sooraj Kannan, P.V.

is prepared by using the empirical formula suggested by Smith and Wishchmeir

(fig. 5.10).

LS = √L (0.76 + 0.53 S +0.076 S2)

100

5. Crop Management Factor Map (C): The crop management factor, C is the

expected ratio of soil loss from land cropped under specified conditions to soil

loss from clan tilled fallow on identical soil and slope and under the same rainfall.

The item reflects the combined effect of cover, crop sequence, productivity level,

length of growing season, tillage practices, residue management and the

expected time distribution of erosive storm with respect to seeding and

harvesting date in the locality. Based on the land use type of the study area crop

management factor, C values are assigned. Crop management factors for

different land use are given table 5.4. Based on these C values Cropping

management map was prepared using spatial analyst (fig. 5.11).

Table 5.4 Crop Management Factor Values

Land use Crop Factor

Coconut 0.1

Coconut predominant mixed cropping 0.15

Paddy 0.4

Rubber 0.1

Rubber predominant mixed cropping 0.15

Tapioca banana mixed cropping in wetland 0.05

6. Conservation Practice Factor (P): Factor, P in the USLE is the ratio of soil loss

with a specific conservation practice to the corresponding loss with up and down

cultivation. P value ranges from 0 to 1 where P value of 1 represents an area

with no conservation practices and up and down slope and 0 represents an area

with proper conservation measures. The value of P in the study area is taken as

1, since there are no properly maintained soil conservation measures.


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Sooraj Kannan, P.V.

Based on the values of R, K, LS, C and P, the soil loss was calculated by using raster

calculator in the spatial analyst. The Raster Calculator can be used to weight and

combine rasters as part of a suitability model. Equal weight is given for all the parameter

rasters. Based on the obtained result, soil erosion was classified as slight/nil, moderate

and severe. Erosion was found maximum from uncultivable rocky and stony area.

Minimum erosion was in loamy soil under coconut, banana or mixed cropping.

4. Stakeholders’ attitude: Stakeholders’ attitude and motivation have great

significance in suggesting suitable conservation measures of the area. In normal

adoption process of innovative techniques, beneficiaries are classified into innovators,

early majority, late majority, and laggards. Here the attitudes of the stake holders are

classified into four, viz, willing to adopt conservation techniques, willing if others are

willing, willing if subsidy is provided and finally not willing to adopt at any circumstances.

The areas with attitude rating are given as weights from 1 to 4 in the attribute table of the

cadastral map. Attitude raster was prepared from cadastral map of Amachal watershed

(fig. 5.12). 5. Prioritization: Prioritisation of the entire area of the watershed is done based on the

runoff generation capability, erosion severity and stake holders’ attitude. Prioritisation is

done by overlay analysis of runoff, erosion severity and stake holders’ attitude maps on

GIS platform. For that all the rasters are reclassified into four classes and overlied by

giving weights shown in the table 5.5.

Table 5.5 weights for each layer

Layer name Weight

Runoff map 35

Erosion map 35

Stake holders attitude 30


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Sooraj Kannan, P.V.

CHAPTER 6

RESULTS AND DISCUSSION

6.1 Slope Regions

Slope regions were derived from triangulated irregular network (TIN) which in turn

generated from contour feature class. Slope regions have been reclassified into gently

sloping (0-3 degrees), moderate sloping (3-5 degrees), strongly sloping (5-10 degrees),

moderate steep (10-15 degrees) and steep above (15 degrees). Slope, being the

determining character of runoff generation capacity and erosion potential of the

watershed is an important parameter in watershed spatial planning. Infiltration

opportunity time will be low in areas with high slope and thus results in heavy runoff and

there by causes severe erosion. Soil erosion is directly proportional to slope of the area

along with the length of slope. South-west part of the watershed have steep slopes

compared to other area. Thus more erosion can be expected from that area.

6.2 Runoff Map

Runoff was estimated using Soil Conservation Society (SCS) curve number method (fig.

6.2). This was done by union of soil texture map and land use map. Later assigning

Curve Number based on land, use, hydrological soil group and antecedent moisture

condition. Rainfall value of 15cm was obtained for the study area when limiting to 50

years frequently and 6 hour duration. Runoff is found out in millimetre per unit area. The

runoff varied from 37 mm (loamy sand under tapioca and banana mixed cropping) and

101mm (sandy clay loam under paddy cultivation). Effect of soil texture on runoff

generation capacity can be clearly seen. Runoff is more from the clayey soil area. This is

because the infiltration capacity of the clayey soil is very low since the porosity of these

soils is very low and the soil structure is generally blocky. Sandy soils drain quickly and

support infiltration and thus cause low runoff. Runoff also depends on the crop cover of

the area. Dense cover crops cause retarding effect on the soil surface and increases


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Sooraj Kannan, P.V.

infiltration. Structure for safe diversion of runoff and water harvesting and recharge

structure can be planned and proposed for these areas with high runoff.

6.3 Erosion Severity Map

Soil erosion severity was analysed using the Universal soil loss equation. Maps for each

parameters of USLE are prepared and rasterised (fig. 6.3). Erosion of the watershed

area ranges from 0 tonnes to 45 tonnes per hectare per year. Obtained raster is

classified into regions of severe erosion (25-45 tonnes/ha/yr), regions of moderate

erosion (10-25 tonnes/ha/yr) and regions of slight or no erosion (0-10 tonnes/ha/yr).

Most of the area (73.8 ha) in Amachal watershed is fallen under moderate erosion

region. Erosion was found maximum from uncultivable rocky and stony area (7.06 ha

including river). Minimum erosion was in loamy soil under coconut, banana or mixed

cropping (23.4 ha).

6.4 Stakeholders’ Attitude Map

Stakeholders’ attitude and motivation have great significance in suggesting suitable

conservation measures of the area. Attitude raster was prepared from cadastral map of

Amachal watershed and displayed as a map. All the four adoption categories can be

seen among the stake holders of the watershed. Majority (80.88 %) of the stake holders

are reluctant towards the implementation of soil conservation interventions. 20.59 % of

the stake holders are not ready to accept any soil conservation projects. So grass root

level extension works should be done to change the attitudes of stake holders towards

the proposed interventions and to convince them about the worse effects of soil erosion.

Subsidies and supports may be provided if needed.

6.5 Prioritization Map

In planning soil conservation programs, community or cooperative action is

indispensable, small farms, which are characteristics in our country, do not allow for an

individual farmer to have an impact on land improvement. By overlay analysis of the

three maps viz, runoff, erosion severity and stake holder’s attitude, a prioritisation map is

generated and displayed (fig. 6.4). The map shows the areas that can be given first


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Sooraj Kannan, P.V.

priority and least priority based on the erosion and runoff characteristics with the land

owners’ attitude. This prioritisation map will help to find the areas where the soil

conservation programmes can be implemented immediately. Area got most priority will

be with severe erosion, heavy runoff generation capacity and positive attitude of land

owners.


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CHAPTER 7 CONCLUSIONS AND SUGGESTIONS

Everybody has a stake in water, and a widely shared vision should therefore include the

opinions of the people, inside and outside the professional water sector. Hence, an

attempt has been made to accommodate the social aspects in the present study, which

will be an integral part of future water resources planning and implementation. The font-

end has been designed using GIS so that the mid-level planners and executives of the

Kerala state Planning System can effortlessly estimate actual area to be occupied by the

conservation measures in a plot. It will be not only advantageous for the planners but

also to the social engineers to motivate the farmers and built congenial environment for

Soil and Water Conservation Programme. The most significant aspect of this interface is

its scope to be developed and formatted on the Arc-IMS platform. So that it will be

available to the local people on their desk tops.

In general, GIS can be successfully applied to geographical data for the integration of

collection, storage, retrieving, transforming and displaying spatial data for solving

complex planning and management problems in a watershed. From the present study it

was clear that a large area could be studied within a short span of time by using spatial

data integration using GIS.

Limitations of the study: The concept of prioritisation has much importance in selection of micro watersheds for

the implementation of watershed development projects in a larger river basins or

developmental blocks. Here the area considered is a micro-watershed and prioritisation

is done with in the holdings. Study is confined to this level because of the constraints in

time and data availability. Along with this the following limitations are found in this study.

• The USLE and the SCS Curve Number method are empirical equations that do

not mathematically represent the physical processes of soil erosion and runoff.

And also they are not developed for Kerala conditions. So that there may be

errors in the estimations.

• Many of the parameter values used to calculate erosion and runoff are assumed

or estimated. The standard values obtained from tables may not be true for the

watershed.


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Sooraj Kannan, P.V.

• The use of the USLE may not be a good predictive model for erosion in this study

area because the extreme slopes throughout the watershed do not correlate well

with the USLE model, which was originally developed for mild slopes in

agricultural areas.

• The absolute estimation of soil erosion is not done in order to reduce the

complexity of analysis. However, results may still be useful to understand the

trend of soil erosion and to identify the areas need immediate attention.

• For prioritization of micro watersheds or sub watersheds the factors like ground

water condition, soil erosion, runoff generation, available natural resources,

agricultural production, socio-economic condition of the people living in the

watershed etc. must be considered. But, here only runoff generation, soil erosion

and attitude of the stake holders are considered.

Suggestions for Future Study

Further improvement can be done in the future studies by incorporating the following

modifications.

• Study may be conducted for a larger river basin for prioritisation of micro-

watersheds.

• A full fledged study of the watershed including parameters like groundwater

condition, socio-economic condition of people etc.

• Better models can be used, which is most suitable to the characteristics of

watershed for exact estimation of erosion and runoff. Soil loss models for steep

slopes may be more suitable for Kerala like highly undulated topography.

• Based on the prioritisation map, site suited remediation works can be suggested,

designed and funds can be allocated based on the works.

• A fully automated decision support system can be developed for the design of

structural interventions.


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Sooraj Kannan, P.V.

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