GIS BASED WATERSHED MANAGEMENT OF KURIANAD-MANIYAKUPARA WATERSHED NEAR KOZHA, KURAVILANGAD IN KOTTAYAM DISTRICT, KERALA

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GIS BASED WATERSHED MANAGEMENT OF KURIANAD-MANIYAKUPARA WATERSHED NEAR KOZHA,

KURAVILANGAD IN KOTTAYAM DISTRICT, KERALA

1 INTRODUCTION

Water resources are increasingly in demand in order to help agricultural and

industrial development, to reduce poverty among rural people, to create incomes and wealth

in rural areas and to contribute to the sustainability of natural resources and the environment.

The land, water, minerals and bio-mass resources are currently under tremendous pressure in

the context of highly competing and often conflicting demands of an ever expanding

population. As a result overexploitation and mismanagement of resources are exerting

dangerous impact on environment.

In India more than 75% of population depends on agriculture for their livelihood.

Agriculture plays a vital role in our country economy. Ministry of Agriculture and Co-

operation has launched and integrated watershed concept using GIS technologies in their

planning exercises. Watershed approach has been the single most important land mark in the

direction of bringing in visible benefits in rural areas and attracting people’s participation in

different developmental programmes.

The basic objective is to increase production of food, fodder and to restore ecological

balance. Watershed management is an iterative process of integrated decision making

regarding uses and modification of land and waters within a watershed (Vinayak and

Umesh, 2013).

1.1 Use of geospatial tools in watershed management

Presently high inhabitant’s expansion, fast urbanization and climate change along

with the irregular frequency and intensity of rainfall make appropriate water management

and storage plans difficult. Therefore, there is an urgent need for the evaluation of water

resources because they play a major role in the sustainability of livelihood and regional

economics throughout the world. It is the primary safeguard against drought and plays a

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central role in food security at local and national as well as global levels. The ever-growing

population and urbanization is leading to over-utilization of the resources, thus exerting

pressure on the limited civic amenities, which are on the brink of collapse (Singh et al.,

2013; Jha et al., 2007).

Quantitative morphometric analysis of watershed can provide information about the

hydrological nature of the rocks exposed within the watershed. A drainage map of basin

provides a reliable index of permeability of rocks and their relationship between rock type,

structures and their hydrological status. Watershed characterization and management

requires detailed information of topography, drainage network, water divide, channel length,

geomorphologic and geological setup of the area for proper watershed management and

implementation plan for water conservation measures (Sreedevi et al., 2013).

Remote sensing data, along with increased resolution from satellite platforms, makes

these technologies appear poised to make a better impact on land resource management

initiatives involved in monitoring Land Use-Land Cover (LULC) mapping and change

detection at varying spatial ranges in semi-arid regions is undergoing severe stresses due to

the combined effects of growing population and climate change (Singh et al., 2012).

GIS based watershed evaluation using Shuttle Radar Topographic Mission (SRTM)

derived elevation data have given a precise, fast, and an inexpensive way for analyzing

hydrological systems (Grohmann et al., 2007).

1.2 Statement of the problem

Natural resources are the major part in the development of a country. Among that

water and soil are most important. A country that tends to have more natural resources and

has a way to refine it, have better and stable economy. Kerala is a state which normally gets

a good amount of rainfall and still facing water scarcity during summer. This is a major

concern and suitable methods should be adopted for the conservation of water, soil and

minerals of the area. Our area of study is Maniyakupara watershed of Muvattupuzha river.

This watershed area (Fig. 1) suffers water scarcity during the summer season and streams

become dry. As per the information from the local people, there were no problems in the

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past. Human interventions and unscientific agricultural approaches may be the result. Due to

this soil and water gets manipulated.

Water covers 71% of the Earth's surface. It is vital for all life forms on Earth. 96.5%

of all planets water is found in seas and oceans. According to UN estimates, the total amount

of water on earth is about 1400 million cubic kilometres (m.cu.km) which is enough to cover

the earth with a layer of 3000 metres depth. However the fresh water constitutes a very small

portion of this enormous quantity. About 2.7% lies frozen in Polar Regions and another

22.6% is present as ground water. The rest is available in lakes, rivers, atmosphere,

moisture, soil and vegetation (Wikipedia, 2014a).

Soil is considered to be the “Skin of Earth”. Soil consists of a solid phase (minerals

and organic matter) as well as porous phase that holds gases and water (Wikipedia, 2014b).

Need for soil conservation arises because of the soil erosion process. Soil erosion is one of

the serious environmental problems in the world today.

1.3 Present study

The present study is an attempt using remote sensing and GIS techniques to propose

various water harvesting and soil conservation measures in order to suggest integrated land

and water resource development plan for Maniyakupara watershed covering 654ha in

Kottayam district, Kerala.

1.4 Background of the study

Watershed management programmes are going on in our country as part of national

interest of preserving and conserving watershed and related livelihood. As part of our

preparation we came to know that several projects were finished and some are going on

under the government scheme. Present full-fledged schemes in Kerala is “Integrated

Watershed Management Programmes (IWMP)” which incorporates several programmes like

(Govt. of Kerala, 2014).

WGDP- Western Ghat Development Programme, Hariyali.

NWDPRA-National Watershed Development Programme for Rain-fed Areas

Thus we accessed a scope of implementing our action plans of selected watersheds

under these schemes with the help of concerned authorities. Thus we selected an area which

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come under the IWMP scheme and contacted the concerned Technical Support Organisation

(TSO), “Centre for social and resource development (CSRD)” based in Pudukad, Thrissur.

They agreed to support us.

2 STUDY AREA

Maniyakupara watershed is located in Kottayam district of Kerala State. It belongs to

the Uzhavoor and Marangatupally Grama Panchayaths and lies in between Kuravilangadu

and Monipally. The overall geographical area of the watershed is about 1339.54 ha (Fig. 1).

The present study area is a part of this bigger watershed having an area of 654 ha. The

reason for selecting this study area instead of entire water shed was the scope of

implementing our analysis results in the integrated watershed programme by the

government. Maniyakupara watershed area lies at 9

76 .

Valiyathodu come under Muvattupuzha watershed and study area covers the micro water

sheds 13m64d and 16m64e in the Kerala Watershed Atlas (KSLUB. 1996.) (Table 1) (Fig.

2). The study area is moderately sloping in majority areas (Fig. 3).

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Figure 2 Base map of study area along with its location in Kerala

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Table 1 Watershed area at a glance - General Information

1 Name of the Block Uzhavoor Block Panchayath

2 Name of the District Kottayam

3 Geographical Area of the Watershed

654 ha

4 Latitude 9°46'6.572"N 9°48'12.105"N

5 Longitude 76°33'45.625"E 76°35'55.856"E

6 Name of the Watershed Maniyakupara

7 Major Water Source Kurianad valiyathodu

8 River flowing nearby the watershed area

Muvattupuzha

9 Livelihood Options Agriculture, Animal Husbandry

Business Wages Govt. Job

Demography 10 Population 4949 11 Number of Males 2331 12 Number of Females 2618 13 Number of SC families 60 14 Number of ST families 0

Agriculture

15 Major Crops Rubber, Areca nut, Coconut, Nutmeg, Banana

16 Marketing Local Land Characteristics

17 Slope Moderately Sloping 18 Erosion Severe

Soil Characteristics 19 Soil Type Gravelly clay loam

The Maniyakupara watershed area consists of 2 Grama panchayaths -

Marangattupally with wards 2 fully and 1, 12, 14 (Partially) and Uzhavoor with 11th ward

fully which together forms a total of 654 ha as treatable area. The study area is moderately

sloping, elevation ranging from 13 to 145 m above Mean Sea Level (MSL) (Fig. 3).

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Figure 3 Perspective view of study area. Look angle from North-West

3 OBJECTIVES To identify priority zones for soil and water conservation in watershed area.

To generate a watershed treatment plan for the area.

To carry out a detailed hydrological analysis of a selected micro-watershed in the

study area.

4 METHODOLOGY The methodology of the project is explained below (Fig. 4).

4.1 Data sources

Source data for the watershed analysis was from SOI toposheet, high resolution

Google earth images, meteorological data from Kottayam dist. Agricultural farm Kozha etc.

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Figure 4 Flowchart of the methodology adopted for the study

DATA COLLECTION

BOUNDARY DELINEATION

GOOGLE IMAGE DOWNLOADED

CADASTRAL MAP (DIGITIZED) FROM CSRD

DATA PROCESSING

METEOROLOGICAL DATA FROM AGRICULTURAL DEPT.

GEOREFERENCED - TOPOSHEET, GOOGLE IMAGE

TOPOSHEET SCANNED

DRAINGE LINE SURVEY

DETAILED TOPOGRAPHICAL SURVEY

GIS- THEMATIC LAYER CREATED- LULC, STREAM, ROAD, CONTOUR,

POWERLINE, SPOT HEIGHT , DEM, STUDY AREA, WATERSHED BOUNDARY

FEATURES EXTRACTED FROM CADASTRAL MAP RECEIVED FROM CSRD

PROJECTED TO UTM

RASTERS CREATED – DEM, SLOPE, RAINFALL

HYDROLOGICAL ANALYSIS – FILL, FLOW DIRECTION, FLOW ACCUMULATION, STREAM NETWORK, STREAM ORDER, FLOW

LENGTH, STREAM TO FEATURE

CONSERVATION PRIORITY ANALYSIS – LULC, SLOPE, ELEVATION, ROAD DENSITY, STREAM DENSITY, DISTANCE FROM SETTLEMENT

DATA ANALYSIS OVERLAY ANALYSIS

RESULT TREATMENT PLAN MAP

PLOT LEVEL CONSERVATION PRIORITY ZONES

HYDROLOGICAL ANALYSIS OF DETAILED TOPOGRAPHIC SURVEYED AREA

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4.1.1 Toposheet

Survey of India (SOI) toposheet – 58 C/9 at 1:50000 scale was scanned to make it to

the digital format. It was georeferenced using ERDAS Imagine 9.3 with the Google image

generated and subsetted along the watershed boundary with the datum World Geodetic

System (WGS) 1984 (Fig. 5).

4.1.2 CNES/Astrium image

CNES/Astrium image of the year 2014 was obtained using Google Earth software

with the help of application software GIS_tool_2010. GIS_tool_2010 is an application used

to generate the kml grid of the SOI toposheets for overlaying in Google earth.

Corresponding kml file of 58C/9 toposheet is generated and overlaid in to the Google

earth, watershed area is located and grids inside the area were saved one by one by’ save

image’ option. These grids are georeferenced by ERDAS Imagine and mosaiced to obtain

the complete watershed area (Fig. 6).

4.1.3 Meteorological data

Rainfall data was collected from district agricultural farm, Kozha. Ten years data

was analyzed and average rainfall was found out. From this data using ArcGIS ‘IDW’ tool

rainfall raster was made. The rainfall data was of the location near to the study area where

the measuring instrument was placed, this detail was used to create the measuring points in

the study area, around 24 points were made using the slightly varied average rainfall

obtained. This points were used in IDW as sample point input (Fig. 7).

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4.1.4 Transect walk

A Transect walk was conducted to identify watershed boundaries and ridge lines.

Around 3 days where taken to complete the watershed boundary delineation. The watershed

boundary was loaded on a GPS enabled mobile phone and applications like Mapin, GPS-

essential, OSM tracker were used. Cadastral map with survey number was digitized by

‘CSRD’ and used for reference.

A GPS enabled mobile phone on which the watershed boundary was loaded was

used to determine the location, whether the area is inside the boundary or not. With the help

of survey number provided on the cadastral map the plots inside the boundary was identified

and corresponding plots were located in the field.

4.1.5 Drainage line survey

The Drainage line survey consisted of 9 members where 4 of them were Panchayath

representatives. It took two days to complete the drainage line survey. The team members

visited all the prominent drains in the project area as part of surveying the status of the

drains. The survey was useful in assessing the state of the drains and to ascertain the need

and suitability of various interventions to protect and develop them. The experience and

knowledge of the natives attributed much to the process.

For keen observation, the entire drainage line was divided into two, Upstream and

Downstream. On the first day the upstream section was surveyed. The drainage line was

easily accessible. The presence of bedrock was seen along the drainage line in some part.

Rubble masonry was a common sight along the sides of the banks as side protection. Check

dam locations was suggested by the panchayath representatives based on the experience and

knowledge on the areas. The locations was analysed using GIS tools and submerged areas

were found out. The presence of bedrock was found through field observations in the

suggested area and the locations suitable for the check dams are proposed. The width and

depth of the proposed site of check dam was found out and is recorded for implementation

purpose for the concerned implementing authority (CSRD). Three existing check dams were

also observed. On the second day, the downstream section was surveyed. Comparatively less

population was observed. Most of the area was covered by rubber plantation along the sides

of the banks. The presence of Areca nut trees, Tapioca cultivation were also observed along

the banks. The suggested check dam sites were analysed and two suitable sites were

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proposed. Side protection was also proposed in necessary locations. Presence of meandering

taken places at certain bends was found out which lead to the change in the path of flow.

Land encroachment at certain locations along the banks was witnessed which lead to

decrease in the width of the stream at those particular locations.

4.1.6 Detailed land topographic survey of a selected micro-watershed

The instrument used to carry out detailed land topographic survey was the Digital

Total Station. Detailed land topographic survey was done to point out the limitations while

conducting hydrological analysis in study area using the Digital Elevation Model (DEM)

from SOI toposheet. Therefore a watershed boundary of a small supporting stream was

chosen to conduct total station based detailed topographic survey. The watershed area was

of 9.305277 ha, boundary was delineated by field observation and spot heights were taken

along the ridge line and inside the watershed very frequently. This data was used to draw the

boundary and area was found out (Fig. 8).

The total station was provided and assisted by Meridian Surveyors, based at Cochin; the

model of the total station used was SOKKIA- SETEX-1 with the following specifications.

Telescope – fully transisting, coaxial and distance measuring optics (length

173mm, objective aperture: 45mm, EDM- 48mm, Magnification: 30x

Angle measurement - Absolute encoder scanning. Both circles adopt diametrical detection. Distance measurement - Modulated laser, phase comparison method with red laser diode.

(Range – upto 10000m with 3AP prisms) Accuracy - with prism fine mode = (2+2ppm x D)mm. D = measuring distance.

The coordinates for the reference point was taken from Google earth, and the

elevation data was obtained from the DEM created from SOI Toposheet. Reference points

were selected from Google Earth image on the basis of convenience of locating the same on

the field. The first station point was established and the two reference points were sited. The

detailed total station survey continued for two days and 992 spot height measurements were

taken (Fig. 26).

By using the point elevation data obtained from detailed topographic survey, DEM

data was created. Slope raster was made from the DEM data and hydrological analysis was

carried out.

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4.2 Thematic layer creation

The feature inside the watershed boundary was digitized and several feature classes

are created in ArcGIS 9.3. Cadastral details where already digitized by CSRD and we

obtained that data. From cadastral map and SOI toposheet we created feature classes like

roads, power line, stream network, survey boundary, panchayath boundary, block boundary

etc.

Land use land cover was created using high resolution Google earth image and direct

field observations. Contour lines of the area were digitized from SOI toposheet (Table 2).

Table 2 Thematic layers with their geometry type and source

Sl. No. Thematic Layers Geometry Type

Source

1 Roads Line Cadastral maps, toposheet, CNES/Astrium image

2 Survey field boundary Polygon Cadastral map 3 Stream network Line Toposheet 4 Watershed boundary Polygon Drawn from toposheet 5 Study area Polygon Obtained from CSRD 6 Spot heights Point Toposheet

7 LULC Polygon Drawn from CNES/Astrium image

8 Power line Line Toposheet, CNES/Astrium image 9 Contour Line Toposheet

10 DEM Raster Using Toposheet contours and spot heights as inputs in “topo to raster tool” in ArcGIS

4.3 Conservation priority analysis

A major part of the project deals with the conservation of natural resources of the

locality such as soil and water. The factors which affects this are,

a) Land use/ Land cover

b) Slope

c) Elevation

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d) Stream density

e) Road density

f) Distance from settlement

According to this factors the intensity soil erosion, runoff varies. Soil erosion is

probably one of the serious environmental problems in the world today. Moreover,

soil erosion affects the productivity of land and is often irreversible. Soil erosion is a

process of dislodgement and transport of soil particle by wind and water. Climate,

topography, soil characteristic, vegetative cover and land use affect erosion so the

conservation methods was adopted in high problematic area.

A multi-criteria evaluation approach was used and criteria score was given according

to the importance (Table 3) (ESRI, 2008).

4.3.1 Land use - land cover

From high quality CNES/Astrium images and field observation the land use, land

cover was identified. On-screen visual interpretation was used for GIS LULC vector layer

creation, which was overlaid on to the Google Earth Image.

4.3.2 Slope

Slope raster was derived from DEM using ArcGIS slope tool. Slope was classified in

percentage.

For each cell, the Slope tool calculates the maximum rate of change in value from

that cell to its neighbour. Basically, the maximum change in elevation over the distance

between the cell and its eight neighbours identifies the steepest downhill descent from the

cell.

Conceptually, the tool fits a plane to the z-values of a 3 x 3 cell neighbourhood

around the processing or center cell. The slope value of this plane is calculated using the

average maximum technique. The direction the plane faces is the aspect for the processing

cell. The lower the slope value, the flatter the terrain; the higher the slope value, the steeper

the terrain.

If there is a cell location in the neighbourhood with a NoData z-value, the z-value of

the center cell will be assigned to the location. At the edge of the raster, at least three cells

(outside the raster’s extent) will contain NoData as their z-values. These cells will be

assigned the center cell

edge cells, which usually leads to a reduction in the slope.

The output slope raster can be calculated in two types of

(percent rise). The percent rise can be better understood if you consider

by the run, multiplied by 100. Consider triangle

rise is equal to the run, and the perce

vertical (90 degrees), as in triangle

4.3.3 Elevation

Elevation details was obtained from SOI Top

m interval

Toposheet. DEM was generated using the digitised contours

of peak importance while dealing with the data related to the topography.

4.3.4 Stream

The line density of the road is specified. Line de

feature in the

length per unit

Line density

It calculates the

radius around each

neighbourhood is considered when calculating the density. If no lines fall within the

neighbourhood at a particular

the radius parameter produce a more generalized density raster. Smaller values

produce a raster that shows more detail. If the area unit scale factor units are small

relative to the features (length of

obtain larger values, use the area unit scale factor for larger units (for example,

assigned the center cell’s z-value. The result is a flattening of the 3 x 3 plane fitted to these


The output slope raster can be calculated in two types of

(percent rise). The percent rise can be better understood if you consider

by the run, multiplied by 100. Consider triangle

rise is equal to the run, and the percent rise is 100 percent. As the slope angle approaches

vertical (90 degrees), as in triangle C, the percent r

Figure 9 Comparing values of slope in degrees and percentage

4.3.3 Elevation

Elevation details was obtained from SOI Top

and the spot heights. The vector contour layer was

Toposheet. DEM was generated using the digitised contours


Stream density

The line density of the road is specified. Line de

in the neighbourhood of each output raster cell. Density is calculated

length per unit area. Line density tool was used here.

Line density

It calculates the magnitude per unit area from polyline features that fall w

radius around each cell (Fig.


neighbourhood at a particular cell, that cell is assigned NoData. Larger values of



relative to the features (length of line sections), the output values may be small. To


value. The result is a flattening of the 3 x 3 plane fitted to these


The output slope raster can be calculated in two types of units, degrees or percent

(percent rise). The percent rise can be better understood if you consider it as the rise divided

by the run, multiplied by 100. Consider triangle B below. When the angle is 45 degrees, the

nt rise is 100 percent. As the slope angle approaches

, the percent rise begins to approach infinity

Comparing values of slope in degrees and percentage

Elevation details was obtained from SOI Toposheet, 58C/9 (1:50000)

. The vector contour layer was created from the SOI

Toposheet. DEM was generated using the digitised contours and spot heights


The line density of the road is specified. Line density calculates the density of linear

of each output raster cell. Density is calculated

Line density tool was used here.

magnitude per unit area from polyline features that fall w

10). Only the portion of a line within the


cell, that cell is assigned NoData. Larger values of



line sections), the output values may be small. To


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value. The result is a flattening of the 3 x 3 plane fitted to these

units, degrees or percent

it as the rise divided

below. When the angle is 45 degrees, the

nt rise is 100 percent. As the slope angle approaches

ise begins to approach infinity (Fig. 9).

Comparing values of slope in degrees and percentage

(1:50000) contours at 20

created from the SOI

and spot heights. DEM data is

nsity calculates the density of linear

of each output raster cell. Density is calculated in units of

magnitude per unit area from polyline features that fall within a

Only the portion of a line within the


cell, that cell is assigned NoData. Larger values of



line sections), the output values may be small. To


square kilometres versus square meters).The values on the output raster will always

be floating point.

4.3.5 Road

Road density is defined as the road

around each cell. Here road magnitude means length of

4.3.6 Distance

The distance from settlement is found out by using the

ArcGIS 9.3. The

the closest source.

4.3.7 Reclassification

The

scores using

4.3.8 Raster

The above

the final cumulative

relative influence

(“LULC” *

The resultant

seriousness of problems


be floating point.

Figure 10

density

Road density is defined as the road

around each cell. Here road magnitude means length of

Distance from settlement

The distance from settlement is found out by using the

GIS 9.3. The ‘Euclidean distance’ tool calculates, for each cell, the Euclidean distance to

the closest source.

4.3.7 Reclassification

base and derived raster layers

scores using ‘Reclassify’ tool in ArcGIS 9.3

Raster calculation

The above reclassified raster layers

cumulative output. Suitable weightage

influence. The algebraic expression used for the same

LULC” * 0.3 + “Slope” * 0.35 + “Elevation” *

density” * 0.1 + “Distance from settlement” *

The resultant raster was again reclassified to get the priority

seriousness of problems.


Output of line density tool

magnitude per unit area that fall within a radius

around each cell. Here road magnitude means length of the road.

The distance from settlement is found out by using the ‘Euclidean distance

tool calculates, for each cell, the Euclidean distance to

layers were then reclassified by giving the appropriate

‘Reclassify’ tool in ArcGIS 9.3 (Table 3).

layers were added together in Raster calculator

weightage was given to the factors according to the

The algebraic expression used for the same is given below.

.35 + “Elevation” * 0.05 + “Stream density” *

.1 + “Distance from settlement” * 0.1)

again reclassified to get the priority zones

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fall within a radius

Euclidean distance’ tool in

tool calculates, for each cell, the Euclidean distance to

giving the appropriate

in Raster calculator to get

was given to the factors according to their

is given below.

.05 + “Stream density” * 0.1 + “Road

zones according to the

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Table 3 The influencing factors with their criteria scores and relative influence weightages

Sl. No.

Influencing Factors

Classes Score Weightage

1 Land use/ Land cover

Rubber Plantation 3 30% Settlements NoData Barren Lands 10 Pineapple Cultivation 6 Tapioca Cultivation 8 Mixed Trees 2 Areca Nut 2 Water body NoData

2 Slope (%) Flat to nearly level (0 – 1) 1 35% Very gentle sloping (1 – 3) 2 Gently sloping (3 – 5) 3 Moderately sloping(5 – 15) 4 Moderately steep to steep(15 – 25)

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Steep (25 – 33) 7 Very steep (33 – 50) 8 Very very steep (>50) 10

3 Elevation (m) above MSL

High (101.2676 – 145.3019)

10 5%

Medium (57.2333 -101.2676)

5

Low (13.1990 – 57.2333)

3

4 Stream density(m/km2)

0.00 – 894.94 1 10% 894.94 – 1789.88 2

1789.88 – 2684.82 3 2684.82 – 3579.76 4 3579.76 – 4474.71 5 4474.71 – 5369.65 6 5369.65 -- 6264.59 7 6264.59 – 7159.53 8

7159.53 – 8054.47 9 8054.47 – 8949.42 10

5 Road density(m/km2)

0.00 – 2555.66 1 10% 2555.66 – 5111.32 2 5111.32 – 7666.97 3

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7666.97 – 10222.63 4 10222.63 – 12778.28 5 12778.28 – 15333.94 6 15333.94 – 17889.59 7 17889.59 – 20445.25 8 20445.25 – 23000.91 9 23000.91 – 25556.56 10

6 Distance from settlement (m)

0.00 – 29.23 1 10% 29.23 – 75.49 2

75.49 – 121.76 3 121.76– 170.47 4 170.47 – 224.10 5 224.10 – 284.93 6 284.93 – 357.98 7 357.98 – 457.84 8 457.85 – 621.00 9

4.4 Plot wise priority zone

The priority raster was converted to polygon in ArcGIS using the ‘raster to polygon’

conversion tool. The output priority polygon feature and survey plot boundary polygons are

united, for this ‘union’ tool was used in ArcGIS. The resultant polygon attribute was

analysed and the plot level priority was obtained and area was summarized. As an example

the details of plot 6/35 was made.

4.5 Watershed treatment plan

The treatment plans for the area was selected by discussions on the present condition

of land and stream network along with the GIS based analysis. As per our field observations

most of the area was rubber plantation and contour bund were present in most of the rubber

plantations, so we avoid contour bund from our conservation methods. Instead of that we

preferred to adopt the conservation method like increase of vegetation cover in rubber

plantations to slowdown water runoff and to increase fertility, for this Pea and Vettiver

vegetation cover was suggested. As per the observations from the drainage line survey, the

conditions of stream network was analysed and possibilities of several conservation methods

was discussed. Stream bank stabilization was avoided as most of the area was stabilized with

rubble masonry, which is not an ideal stabilization technique as it increases the water runoff.

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So our concentration was on methods which can slow down the water runoff and to enable

the water storage. And from discussions we concluded to following conservation methods.

They are;

Check dam

Gully plugs

Boulder check bund

For regions near to roads with high slopes we suggested coir netting. Land use/ Land cover

(LULC) was thoroughly analysed and regions where more conservation needed was sorted

out. Regions like barren lands and water bodies were located and suggestions for vegetation

bund (Vettiver and Agave) were made.

The location of the conservation methods were decided by GIS analysis. Thematic layers

like roads, LULC, stream network was overlaid along with priority, slope rasters and

locations were decided (Table 4).

Table 4 Conservation methods adopted

Sl. No.

Conservation Methods

1 Check dam 2 Gully plug 3 Boulder check bund 4 Vegetation bund (Vettiver and Agave) 5 Coir netting 6 Ground vegetation cover improvement (Pea plant and

other common grasses)

4.5.1 Check dam analysis

The area, volume of each check dam was calculated using GIS tools. The

submergence area of check dams were found out using ‘create contour’ tool. Height of each

check dam was added to the base height of location of dams, using this elevation contours

were made and it was converted to feature using ‘graphics to feature’ tool. Result was line

feature and it was corrected in editor giving the check dam width at outlet. This line feature

was converted to polygon feature using ‘feature to polygon’ tool. The DEM of the study

area was clipped with this

the check dam was calculated using ‘surface volume’ tool

DEM. The clipped raster was given as input and reference plane was selected as ‘below’,

plane height was given as the check dam height. The out

area and volume of the check dam.

4.6 Hydrological

Hydrological

depressions were

accumulation

4.6.1 Fill

This tool was used to remove small imperfections in

cell with an undefined drainage direct

value

contributing area of a sink. If the sink were filled with water, this is the point where

water would pour out.

4.6.2 Flow direction

This tool was used to create the flow direction raster

down slope neighbour

The output of the Flow d

to 255. The values for each direction from the

clipped with this polygon and check dam elevation ra

check dam was calculated using ‘surface volume’ tool

The clipped raster was given as input and reference plane was selected as ‘below’,

plane height was given as the check dam height. The out

area and volume of the check dam.

ydrological analysis

Hydrological analysis was of different s

depressions were filled using ‘fill’ tool and followed by

accumulation’ tools (ESRI, 2008).

This tool was used to remove small imperfections in

cell with an undefined drainage direct

value. The pour point is the boundary cell with the lowest elevation for the


water would pour out.

direction

This tool was used to create the flow direction raster

down slope neighbour. The details of flow direction tool are given below

Elevation raster

Figure 11 Illustration of flow direction raster

The output of the Flow direction tool is an integer raster whose values range from 1

to 255. The values for each direction from the

and check dam elevation raster was made.

check dam was calculated using ‘surface volume’ tool in ArcGIS


plane height was given as the check dam height. The output was a table depicting the surface

analysis was of different steps as shown in flowchart (Fig.

tool and followed by ‘flow direction

This tool was used to remove small imperfections in the DEM i.e.; Sinks.

cell with an undefined drainage direction; no cells surrounding it has a

The pour point is the boundary cell with the lowest elevation for the


This tool was used to create the flow direction raster from each cell to its steepest

. The details of flow direction tool are given below

Flow direction raster

1 Illustration of flow direction raster

irection tool is an integer raster whose values range from 1

to 255. The values for each direction from the centre area (Fig. 12)

24

ster was made. The volume of

ArcGIS using check dam


put was a table depicting the surface

s as shown in flowchart (Fig. 16), DEM

flow direction’ and ‘flow

; Sinks. A sink is a

ion; no cells surrounding it has a lower pixel

The pour point is the boundary cell with the lowest elevation for the


each cell to its steepest

. The details of flow direction tool are given below (Fig. 11).

Flow direction raster

irection tool is an integer raster whose values range from 1

):

If a cell is lower than its eight

neighbour

lowest value, the cell is still given this value, but flow is defined with one of the two

methods explained below. This is used to filter out one

considered noise.

4.6.3 Flow accumulation

This tool was used to create flow

slope

are given below.

identif

The result of Flow

determined by accumulating the weight for all cells that flow

cell.

to any downstream flo

Figure 12 Illustration of calculating flow direction

If a cell is lower than its eight neighbours

neighbour, and flow is defined toward this cell. If multiple


methods explained below. This is used to filter out one

considered noise.

accumulation

This tool was used to create flow

slope neighbour. Rainfall measurement raster was given as weightage. Details of tool

are given below. Total flow accumulation at outlet was also calculate

identify tool. Outlet pixel value is the outlet flow accumulation

Flow direction Flow accumulation

Figure 13 Illustration of flow accumulation raster

The result of Flow accumulation is a raster of accumulated flow to each cell, as

determined by accumulating the weight for all cells that flow

cell. Cells of undefined flow direction will only receive flow; they will not contribute

to any downstream flow. A cell is considered to have an undefined flow direction if

llustration of calculating flow direction

neighbours, that cell is given the value of its lowest

, and flow is defined toward this cell. If multiple neighbours


methods explained below. This is used to filter out one-cell sinks, which are

This tool was used to create flow accumulation raster from each cell

. Rainfall measurement raster was given as weightage. Details of tool

Total flow accumulation at outlet was also calculate

. Outlet pixel value is the outlet flow accumulation (Fig.


llustration of flow accumulation raster

ccumulation is a raster of accumulated flow to each cell, as

determined by accumulating the weight for all cells that flow into each

flow direction will only receive flow; they will not contribute

w. A cell is considered to have an undefined flow direction if

25

llustration of calculating flow direction

, that cell is given the value of its lowest

neighbours have the


cell sinks, which are

each cell to its down

. Rainfall measurement raster was given as weightage. Details of tool

Total flow accumulation at outlet was also calculated using the

(Fig. 13).


llustration of flow accumulation raster

ccumulation is a raster of accumulated flow to each cell, as

into each down slope

flow direction will only receive flow; they will not contribute

w. A cell is considered to have an undefined flow direction if

26

its value in the flow direction raster is anything other than 1, 2, 4, 8, 16, 32, 64, or

128.The accumulated flow is based on the number of cells flowing into each cell in

the output raster. The current processing cell is not considered in this accumulation.

Output cells with a high flow accumulation are areas of concentrated flow and can be

used to identify stream channels. Output cells with a flow accumulation of zero are

local topographic highs and can be used to identify ridges.

4.6.4 Stream network

Stream networks can be delineated from a digital elevation model (DEM) using the

output from the ‘Flow accumulation’ tool. Flow accumulation in its simplest form is

the number of upslope cells that flow into each cell. By applying a threshold value to

the results of the ‘Flow accumulation’ tool using the Con tools, a stream network can

be delineated. For example, to create a raster where the value 1 represents a stream

network on a background of NoData, the tool parameters could be as follows:

With the Con tool:

Input conditional raster: flowacc

Expression: “Value > 100”

Input true raster or constant value: 1

Input false raster or constant value : “”

Output raster: stream_net

As explained above to generate the stream network from flow accumulation raster, raster calculator was used. In raster calculator the following condition was used.

CON(“flow accumulation raster” >= 15000,1,””)

Thus the output was generated as above conditional statement, that is the

accumulation values above 15000 was given ‘1’ and rest is ‘0’ thus stream network

is obtained.

4.6.4.1 Stream

This tool was used to create the stream order raster.

Stream ordering is a method of assigning a numeric order to links in a stream

network. This order is a method for identifying and classifying t

based on their numbers of tributaries. Some characteristics of streams can be inferred

by simply knowing their order.

For example, first

no upstream concentrated flow. Because

point source pollution problems and can derive more benefit from wide riparian

buffers than other areas of the watershed

In both methods, the

order of 1.

Strahler method

In the Strahler method, all links without any tributaries are assigned an order of 1 and

are referred to as first order.

The stream order increases wh

intersection of two first

two second

two links of different o

Stream order





by simply knowing their order.

For example, first-order streams are dominated by overland flow of water; they have

no upstream concentrated flow. Because


buffers than other areas of the watershed

Figure 14 Two methods for calculating stream order

In both methods, the upstream stream segments, or exterior links, are always assigned an

Strahler method


are referred to as first order.

The stream order increases when streams of the same order intersect. Therefore, the

intersection of two first-order links will create a second

two second-order links will create a third

two links of different orders, however, will not result in an increase in order. For





order streams are dominated by overland flow of water; they have

no upstream concentrated flow. Because of this, they are most susceptible to non


buffers than other areas of the watershed (Fig. 14).

methods for calculating stream order

upstream stream segments, or exterior links, are always assigned an


en streams of the same order intersect. Therefore, the

order links will create a second-order link, the intersection of

order links will create a third-order link, and so on. The intersection of

rders, however, will not result in an increase in order. For

27


network. This order is a method for identifying and classifying types of streams


order streams are dominated by overland flow of water; they have

of this, they are most susceptible to non-


upstream stream segments, or exterior links, are always assigned an


en streams of the same order intersect. Therefore, the

order link, the intersection of

order link, and so on. The intersection of

rders, however, will not result in an increase in order. For

28

example, the intersection of a first-order and second-order link will not create a third-

order link but will retain the order of the highest ordered link.

The Strahler method is the most common stream ordering method. However, because

this method only increases in order at intersections of the same order, it does not

account for all links and can be sensitive to the addition or removal of links.

Shreve method

The Shreve method accounts for all links in the network. As with the Strahler method,

all exterior links are assigned an order of 1. For all interior links in the Shreve method,

however, the orders are additive. For example, the intersection of two first-order links

creates a second-order link, the intersection of a first-order and second-order link

creates a third-order link, and the intersection of a second-order and third-order link

creates a fifth-order link.

Because the orders are additive, the numbers from the Shreve method are sometimes

referred to as magnitudes instead of orders. The magnitude of a link in the Shreve

method is the number of upstream links.

Strahler method was adopted to generate the stream order raster. Stream network and

flow direction raster was the inputs.

4.6.4.2 Stream network to feature

This tool was used to convert a raster representing a linear network to features

representing the linear network, thus stream feature was generated. The algorithm

used by the ‘Stream to Feature’ tool is designed primarily for vectorization of stream

networks or any other raster representing a raster linear network for which

directionality is known.

The tool is optimized to use a direction raster to aid in vectorizing intersecting and

adjacent cells. It is possible for two adjacent linear features of the same value to be

vectorized as two parallel lines. This is in contrast to the

which is generally more aggressive with collapsing the lines together.

To visualize this difference,

simulated

(Fig. 15

4.6.5 Flow length

This tool was used to

distance, along the flow path for each cell.

flow direction raster.

The value type for the Flow Length output raster is floating point. A primary use of

the ‘

given basin. This measure is often used to calculate the time of concentration of a

basin. This would be done using the

vectorized as two parallel lines. This is in contrast to the


To visualize this difference, an input stream network is shown below, with the

simulated ‘Stream to Feature’ output compared to the

Fig. 15).

Figure 15 Illustration of raster to feature tool

length

This tool was used to calculate the upstream or downstream distance, or weighted

distance, along the flow path for each cell.

flow direction raster.


‘Flow Length’ tool is to calculate the length of the longest flow path within a


basin. This would be done using the

vectorized as two parallel lines. This is in contrast to the ‘Raster


an input stream network is shown below, with the

output compared to the ‘Raster to

llustration of raster to feature tool

calculate the upstream or downstream distance, or weighted

distance, along the flow path for each cell. Flow length raster was obtained from


tool is to calculate the length of the longest flow path within a


basin. This would be done using the ‘Upstream’ option. The tool can also be used to

29

Raster to Polyline’ tool,


an input stream network is shown below, with the

to Polyline’ output

calculate the upstream or downstream distance, or weighted

Flow length raster was obtained from


tool is to calculate the length of the longest flow path within a


option. The tool can also be used to

30

create distance-area diagrams of hypothetical rainfall and runoff events using the

weight raster as an impedance to movement down slope.

Figure 16 Flow chart showing the hydrological analysis adopted

SPOT HEIGHTS

DEM

FILL

STREAM TO FEATURE,

STREAM ORDER

DEPRESSIONLESS DEM

RASTER CALCULATOR

FLOW ACCUMULATION

STREAM NETWORK

FLOW DIRECTION

FLOW LENGTH

31

5 RESULTS AND DISCUSSION

5.1 Conservation priority analysis

The results of distribution of various influencing themes are following.

5.1.1 Land use/ Land cover

The LULC details are shown below. The (Fig. 17) displays the LULC of the

region along with the roads. Area of each feature is shown in (Table 5).

Table 5 Extent of various Land use/ Land cover types in the study area

Code Description Area (ha) Percentage (%)

1 Rubber Plantation 334.1 51.24

2 Settlements 74.14 11.37

3 Barren Lands 12.47 1.91

4 Pineapple Cultivation 7.79 1.19

5 Tapioca Cultivation 1.08 0.17

6 Mixed Trees 222.08 34.06

7 Water Body 0.31 0.04

TOTAL 651.97 100

5.1.2 Slope

The slope of the region ranges from 0 to 144% (Fig. 18).

5.1.3 Elevation

The elevation raster (DEM) created from contour and spotheights from SOI toposheet

showed the range from 13 to 145 meters above MSL. It was reclassified to 3 zones (Fig.

19).

5.1.4 Stream density

Density of streams in the watershed area was calculated using ‘Line density’ tool in

ArcGIS 9.3. It ranges from 0 to 8949 m/km2 (Fig. 20).

5.1.5 Road density

Road density ranged from 0 to 25556 m/km2 (Fig. 21).

32

5.1.6 Distance from settlements

Distance from settlements ranged from 0 to 621 m (Fig. 22).

5.1.7 Raster calculation

The resultant cumulative conservation priority raster showed a score range from 1.5 to

8.9 (Fig. 23). This was further reclassified into 5 conservation priority zones. The area of

each priority zones are shown below and percentage of area with total area is also given

(Table 6).

Table 6 Details of conservation priority zones

*Percentage based on area of entire study area.

5.2 Plot wise priority zone

The plot wise priority zone was created using ArcGIS tools (Fig. 24). As an example

the detail of plot ‘6/35’ is shown(Table 7).

Table 7 Plot wise priority zone area of plot 6/35.

Sl. No.

Priority zones Area (m2)

1 Very high 0 2 High 2233.30212 3 Medium 156.809021 4 Low 7548.642947 5 Very low 6775.992886

Sl. No. Priority zones (Cumulative priority score)

Area (ha) Area (%)*

1 Very high (7.42 – 8.9 ) 0.086 0.013

2 High (5.94 – 7.42) 5.81 0.88

3 Medium (4.46 – 5.94) 64.31 9.83

4 Low (2.98 – 4.46) 383.31 58.61

5 Very low (1.5 – 2.98) 126.18 19.29

41

5.3 Watershed treatment plan

Site specific watershed treatment plans proposed are depicted in the map given

below (Fig. 25) (Table 8).

Table 8 Conservation methods used and its count Sl. No. Conservation Methods Count

1 Check dam 3 2 Gully plug 32 3 Boulder check bund 20 4 Vegetation bund (Vettiver and Agave) 118 5 Coir netting 75 6 Ground vegetation cover improvement (Pea) 18

The check dam submergence area and volume were found out using ‘surface

volume’ tool in GIS (Table 9) (Fig. 25).

Table 9 Check dam - submergence analysis results

Sl. No. Check Dam Surface Area (m2)

Volume (m3)

1 Kappungal 5071.10 2149.68 2 Anicode 20103.18 11671.49 3 Arappupalam 12320.76 7731.92

5.4 Detailed topographic survey

The location coordinates and elevation of 992 spots were obtained during the total

station survey (Fig. 26). This spot heights are used to make a detailed hydrologically

corrected DEM and the slope of the selected micro-watershed area (Fig. 27, 28).

5.4.1 Hydrological analysis of the selected micro-watershed

The filled depression less DEM was used to carry out the further hydrological

analysis. The outputs were in the following order, flow direction, flow accumulation, stream

network, stream order and flow length. The outputs are shown figures 29 to 33. The

accumulated flow in this micro-watershed just by considering distribution of annual rainfall

is 28846764 cm.

51

6 CONCLUSION

Hydrological analysis done by using the detailed topographic survey was very much

accurate and the results obtained are very useful for detailed treatment and operational

planning of the selected micro-watershed. Hydrological analysis using the SRTM DEM or

DEM created from contours and spot heights from toposheets will not give pleasing results

as it does not have exact elevation data or resolution like detailed topographic survey. The

treatment plans adopted in our study area using DEM (created using toposheet) will not be

as effective as results of detailed topographic survey.

Soil data was not used in the analysis process as it was not available in time; this is

the major drawback of the results obtained. The result does not consider the soil type of the

location which is a limitation; soil type can affect the runoff, erosion, water holding of the

region. Even then, the watershed treatment plan generated through this study can be used for

field implementation because there is no much variation in soil characteristics as revealed

by local farmers.

52

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Jha M. K., Chowdhury A., Chowdary V. M. and Peiffer S. 2007. Groundwater management

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KSLUB. 1996. Watershed Atlas of Kerala. Kerala State Land Use Board,

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Singh Prafull, Thakur J., Singh U. C. 2013. Morphometric analysis of Morar River Basin,

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Singh Prafull, Thakur J. K., Kumar S., Singh U. C. 2012. Assessment of land use/land cover

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Wikipedia. 2014a. Water. http://en.wikipedia.org/wiki/Water. accesed on 15/1/2015.

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Education

GIS BASED WATERSHED MANAGEMENT OF KURIANAD-MANIYAKUPARA WATERSHED NEAR KOZHA, KURAVILANGAD IN KOTTAYAM DISTRICT, KERALA