GIS-based watershed management of Kurianad- Maniyakupara watershed near Kozha, Kuravilangad in Kottayam Dist., 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˚46′6.572″ North latitude and
76˚33’45.625” East longitude. The main water source is Kurianadu valiyathodu.
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 1 Entire watershed area with stream network and present study area

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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
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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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Figure 5 Study area in SOI Toposheet 58C/9 (Study area boundary shown in yellow colour)

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Figure 6 CNES/Astrium image of the study area of 2014 captured from Google Earth

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Figure 7 Rainfall distribution raster and measurement points of entire watershed with study area boundary overlaid

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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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Figure 8 Micro-watershed area selected for detailed topographic survey and its location in study area

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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
Fig. 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
Road 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
The 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)
6
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
Elevation raster 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

33
Figure 17 Land use/ Land cover of the study area along with roads and major locations

34
Figure 18 Distribution of slope (%) in the study area, slope ranging from 0 to 144 %

35
Figure 19 Distribution of elevation (m above MSL) in the study area, elevation ranged from 13.19 to 145.30 m

36
Figure 20 Distribution of stream density in the study area, stream density ranged from 0 to 8949.41601 m/km2

37
Figure 21 Distribution of road density in the study area, road density ranged from 0 to 25556.564 m/km2

38
Figure 22 Distribution of distance from settlements, ranged from 0 to 621m

39
Figure 23 Priority zones for soil and water conservation in the study area (White areas indicate areas covered with built-up)

40
Figure 24 Priority zones with survey plots (White areas indicate areas covered with built-up)

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.

42
Figure 25 Watershed treatment plans proposed along with check dam submergence area

43
Figure 26 Spot heights and stream network obtained from detailed topographic survey

44
Figure 27 Distribution of elevation (m above MSL) in the detailed topographic survey area, elevation ranged from 0 to 68.77 m

45
Figure 28 Distribution of slope in the detailed topographic survey area, slope ranged from 0.087 to 141.868 %

46
Figure 29 Derived flow direction from hydrological analysis of the detailed topographic survey data

47
Figure 30 Derived flow accumulation from detailed topographic survey

48
Figure 31 Derived stream network from detailed topographic survey

49
Figure 32 Derived flow length from detailed topographic survey

50
Figure 33 Stream order generated from hydrological analysis

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
REFERENCES
Govt. of Kerala. 2014. Integrated watershed management programme.
http://rdd.kerala.gov.in/index.php?option=com_content&view=article&id=58&Itemi
d=50. accesed on 15/1/2015.
Grohmann C. H., Riccomini C., Alves F. M. 2007. SRTM-based morphotectonic analysis of
the Pocos de Caldas alkaline Massif, southeastern Brazil. Computers & Geoscience:
33, 10–19.
Jha M. K., Chowdhury A., Chowdary V. M. and Peiffer S. 2007. Groundwater management
and development by integrated RS and GIS: prospects and constraints. Water
Resources Management 21: 427– 467.
KSLUB. 1996. Watershed Atlas of Kerala. Kerala State Land Use Board,
Thiruvananthapuram.
Singh Prafull, Thakur J., Singh U. C. 2013. Morphometric analysis of Morar River Basin,
Madhya Pradesh, India, using remote sensing and GIS techniques: Environmental
Earth Science 68: 1967–1977.
Sreedevi P. D., Sreekanth P. D., Khan H. H., Ahmed S. 2013. Drainage morphometry and
its influence on hydrology in an semi arid region, using SRTM data and GIS:
Environmental Earth Science 70 (2): 839–848.
Singh Prafull, Thakur J. K., Kumar S., Singh U. C. 2012. Assessment of land use/land cover
using Geospatial Techniques in a semi arid region of Madhya Pradesh, India. In:
Thakur, Singh, Prasad, Gossel (Eds.), Geospatial echniques for Managing
Environmental Resources. Springer and Capital Publication, Heidelberg, Germany:
152–163.
Vinayak N. Mangrule and Umesh J. Kahalekar. 2013. Watershed Planning and Development
Plan by Using Rs and GIS of Khultabad Taluka of Aurangabad District.
International Research Publications House 3(10): 1093-110.
Wikipedia. 2014a. Water. http://en.wikipedia.org/wiki/Water. accesed on 15/1/2015.
Wikipedia. 2014b. Soil. http://en.wikipedia.org/wiki/Soil. accesed on 15/1/2015.

GIS-based watershed management of Kurianad- Maniyakupara watershed near Kozha, Kuravilangad in Kottayam Dist., Kerala

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GIS-based watershed management of Kurianad- Maniyakupara watershed near Kozha, Kuravilangad in Kottayam Dist., Kerala