Ijciet 10 01_181

http://www.iaeme.com/IJCIET/index.asp 1999 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 01, January 2019, pp. 1999-2013, Article ID: IJCIET_10_01_181
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=01
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
RUNOFF ESTIMATION OF MINI WATERSHED
OF PEDDA KEDARI RESERVE FOREST,
TEKKALI, SRIKAKULAM, AP USING REMOTE
SENSING, GIS AND SCS CURVE NUMBER
TECHNIQUES
Dr. Ch. Kannam Naidu
Civil Engineering Department, VIIT, Duvvada-530 049, Andhra Pradesh, India
S.Ramlal
Civil Engineering Department, AITAM, Tekkali - 532201, Andhra Pradesh, India
Dr. Ch. Vasudeva Rao
Civil Engineering Department, AITAM, Tekkali - 532201, Andhra Pradesh, India
ABSTRACT
The primary source of water is rainfall for the generation of runoff over the land
surface. Runoff or overland flow is the flow of water that occurs when excess storm
water flows over the earth's surface. Satellite remote sensing and GIS techniques
coupled with conventional filed investigations were used for mapping of land use/land
cover (LU/LC) features of the Mini Watershed of Pedda Kedari reserve forest towards
estimating the runoff of the Mini watershed. The SCS-CN method (SCS, 1985) method
involves the use of a simple empirical formula and readily available tables and curves.
Determination of SCS curve number depends on the soil and land cover conditions,
which the model represents as hydrologic soil group, cover type, treatment and
hydrologic condition. Soils are classified into hydrologic soil groups (HSG) to indicate
the minimum rate of infiltration obtained for bare soil after prolonged wetting.
Runoff computed from a given rainfall event was integrated with the data of land
use treatment, curve numbers and hydrological soil groups by using SCS-CN method.
The estimated runoff contributes more than 37% of total rainfall received in the study
area. The suitable locations of rainwater harvesting and artificial recharge structures
are suggested to increase the groundwater levels for sustainable development of water
resources in the Mini watershed of Pedda Kedari Reserve Forest.
Key words: Mini Watershed, Runoff, Remote Sensing, GIS, LU/LC, SCS-CN, HSG

Dr. Ch. Kannam Naidu, S.Ramlal and Dr. Ch. Vasudeva Rao
Cite this Article: Dr. Ch. Kannam Naidu, S.Ramlal and Dr. Ch. Vasudeva Rao, Runoff
Estimation of Mini Watershed of Pedda Kedari Reserve Forest, Tekkali, Srikakulam,
Ap Using Remote Sensing, Gis And Scs Curve Number Techniques, International
Journal of Civil Engineering and Technology, 10(01), 2019, pp. 1999–2013
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=01
1. INTRODUCTION
When rain falls, initially first drops of water are intercepted by the leaves and stems of the
vegetation i.e., interception storage. The water reaching the ground surface, as the rain
continues, infiltrates into the soil until it reaches a stage where the rate of rainfall exceeds the
infiltration capacity of the soil. After that, surface puddles, ditches, tanks and other depressions
are filled, thereafter runoff is generated. The infiltration capacity of the soil depends on its
texture and on the antecedent soil moisture content (previous rainfall or dry season). The
process of runoff generation is continued as long as the rainfall intensity exceeds the actual
infiltration capacity of the soil but it stops when the rate of rainfall drops below the actual rate
of infiltration.
Remote sensing and GIS have proved an effective means for extracting and processing
varied resolutions of spatial information for monitoring natural resources (Masser, 2001). In
case of inaccessible region, this technique is perhaps the only method of obtaining the required
data on a cost and time-effective basis. Several remote sensing satellites were launched for
various purposes and of various resolutions, which provides a new dimension to the remote
sensing technology. Now, most common remote sensing systems operates in one and/or several
of the visible, infrared, or microwave portions of the electromagnetic spectrum (Jensen, 2007).
Digital elevation models (DEM) are among the remote sensing techniques that have been used
to measure landscape surface roughness properties over large areas. These are used for visual
and mathematical analysis of topography, landscapes and landforms; and also modeling of
surface processes (Millaresis and Argialas, 2000; Tucker et al. 2001). Prudhvi Raju and
Vaidyanathan (1981) analyzed the fracture patterns of Eastern Ghats region, Andhra Pradesh,
which was taken from Landsat imagery using standard visual interpretation techniques. Chetty
and Murthy (1993) have mapped the structural and various lithological features of east coast
of India using remote sensing data. They adopted different remote sensing techniques for
identification of lineaments and other structural features followed by ground check in the field.
Land use and land cover changes are important elements of the global environmental change
processes (Dickinson, 1995; Hall et al. 1995). Traditional approaches to automated land cover
mapping using remotely sensed data have employed pattern recognition techniques including
supervised and unsupervised approaches (Jensen, 1986; Benediktsson et al. 1990; Fried and
Brodley, 1997; Ward et al. 2000; Rashed et al. 2001; Shamsudheen et al. 2005). Murthy and
Venketeswara Rao (1997) have carried out temporal studies of land use/land cover in Varaha
river basin, Andhra Pradesh, India using Landsat and IRS LISS data.
Estimating direct runoff depths from storm rainfall by the United States Department of
Agriculture (USDA) by curve number (CN) method (Soil Conservation Service (SCS), 1972
and 1985) probably the most widely used techniques. The SCS-CN method is one of the most
popular methods for computing the volume of surface runoff in catchments for a given rainfall
event. This approach involves the use of a simple empirical formula and readily available tables
and curves. A high curve number means high runoff and low infiltration, whereas a low curve
number means low runoff and high infiltration. The curve number is a function of land use and
hydrologic soil group (HSG). It is a method that can incorporate the land use for computation
of runoff from rainfall. The SCS-CN method provides a rapid way to estimate runoff change

Runoff Estimation of Mini Watershed of Pedda Kedari Reserve Forest, Tekkali, Srikakulam, Ap
Using Remote Sensing, Gis And Scs Curve Number Techniques
due to land use change (Shrestha, 2003; Zhan and Huang, 2004). The SCS-CN method is a
well accepted tool in applied hydrology. Greene and Cruise (1995) and Ponce and Hawkins
(1996) worked on the applicability of curve number and considered the CN method as one of
the useful tool for calculating runoff depths. Gary and Carmen (2007) conducted a study to
ascertain the impact of land use and management practices on rainfall-runoff relationship and
used GIS techniques to route runoff through a watershed. The land use/land cover, HSGs and
storm rainfall data were utilized to estimate the runoff (Durbude et al. 2001; Ambazhagan et
al. 2005; Jasrotia and Singh, 2006; Rao et al. 2010; Kumar and Rajpoot, 2013).
High relief and steep slopes impart higher runoff, while the topographical depressions help in
an increased infiltration. Surface water bodies like rivers, ponds, etc. can act as recharge zones
enhancing the groundwater potential in the vicinity (Jensen, 1986). Geospatial data land
use/land cover and Lineaments are not available for Mini Watershed of Pedda Kedari
Reserve Forest area. The topic of the present project work has highest importance and
relevance, as the rapid drawing of water resources have enormous impact on the environment
of the study area. The focus of the research work is to identify the various factors affecting the
water and land environments using geospatial information in Mini Watershed Pedda Kedari
Reserve Forest area of Srikakulam district, Andhra Pradesh, India.
1.1. Study Area
The Mini Watershed of Pedda Kedari Reserve Forest is an integral part of Srikakulam district
of Andhra Pradesh State. The district is located in the north-eastern part of the State. The district
has a coastline of 192 km, and is situated in between the Eastern Ghats and the Bay of Bengal.
It is one of the less populated and low literacy district of the State. The district is endowed by
good rainfall, forest wealth, and mineral and surface water resources. The aerial extent is 5,837
km2. The district is bounded by the Bay of Bengal on the east, Vizianagaram district on west
and south, and Odisha state on north and northwest. Howrah–Chennai broad gauge railway line
and NH-5 are passing through the district almost parallel to the coastline. The district is divided
into three revenue divisions viz. Srikakulam, Palakonda and Tekkali. Further these revenue
divisions are subdivided into 38 revenue mandals consisting of six towns and 1,763 villages
with a population of 25,37,593 as per Census 2011. The urban population is 4,36,347 whereas
rural population constitutes 22,63,124 (District Census Handbook, 2011). The density of
population of the district is 462 persons per km2
. The important rivers flowing in the district
are Vamsadhara, Nagavali, Suvarnamukhi, Vegavati, Mahendratanaya and Bahuda. Among
the rivers Vamsadhara, Nagavali and Suvarnamukhi are perennial (Figure 1.0). The general
drainage pattern is dendritic to sub-dendritic and occasionally parallel at places.

Figure 1.0. Location map of the study area
1.2. Data Used and Methodology
The survey of India (SOI) toposheet No. 74 B/2 of 1:50,000 along with GeoEye-I imagery of
1.65 m resolution and Landsat ETM+ imagery of 30 m resolution were used to generate the
different maps. DEM was generated from the toposheet contour data there by generated Slope
map and Aspect map. Land Use/Land Cover was mapped using the GeoEye-I imagery by visual
interpretation. Lineaments map was mapped using the Landsat imagery by applying sobel filter
techniques. The Soil Texture Map was collected from National Bureau of Soil Sciences
(NBSS), Nagpur. The daily rainfall data of Meliaputti was collected for the years of 2005 to
2014. The daily rainfall data has been used for determination of storm events to identify anti
moisture conditions.

Flow Chart
2. RESULTS AND DISCUSSIONS
Runoff computed from a given rainfall event was integrated with the data of land use treatment,
curve numbers and hydrological soil groups by using SCS-CN method. The SCS-CN method
(SCS, 1985) method involves the use of a simple empirical formula and readily available tables
and curves. A high curve number means high runoff and low infiltration, whereas a low curve
number means low runoff and high infiltration. The curve number could be estimated from
land use and hydrologic soil group. It is a method that can incorporate the land use for
computation of runoff from rainfall. The SCS-CN method provides a rapid way to estimate
runoff change due to land use change.
2.1. Land Use/Land Cover (LU/LC) Studies
Satellite remote sensing and GIS techniques coupled with conventional filed investigations
were used for mapping of land use/land cover (LU/LC) features of the Mini watershed towards
estimating the runoff of the area. The knowledge of land use and land cover is important
for many planning and management activities as it is considered as an essential element for
modeling and understanding the earth feature system. Land use defines as any human
activity or economical related function associated with a specific piece of land, while the
term land cover relates to the type of feature present on the surface of earth (Lillesand and
Kiefer, 2003). The Land use/Land cover features (Figure 2.1) were extracted from GeoEye-I
imagery (Figure 2.0) by using visual interpretation techniques.

Figumre 2 GeoEye-I Imagery Figure 2.1. Land use/Land cover Map
Table 1.0. Land use/land cover changes from satellite data observed in the Mini watershed
Category
Area
Km2
%
Built-up land 0.0005810 0.047
Crop land 0.0791250 6.450
Scrub 1.1044620 89.99
Barren land 0.0431430 3.515
2.2. SCS Curve Number Method
Determination of SCS curve number depends on the soil and land cover conditions, which the
model represents as hydrologic soil group, cover type, treatment and hydrologic condition.
This method is based on an assumption of proportionality between retention and runoff as,
( )
P
Q
S
QP
=
−
Where Q is actual direct runoff, P is total storm rainfall, and S is potential maximum
retention.
The retention parameter (S) varies spatially, due to changes in soils, land use and slope and
temporally due to changes in soil-water content. This is the ratio of actual retention of
rainfall to potential retention is equal to the ratio of actual runoff to rainfall minus initial
abstraction. This assumption underscores the conceptual basis of the runoff curve number
method expressed as,
a
a
IP
Q
S
QIP
−
=
−−

( )
( ) SIP
IP
Q
a
2
a
+−
−
=
Where P, Q and S are expressed in mm or inches.
The initial abstraction Ia is all the losses before runoff begins. It includes the water retained
in surface depressions and the water intercepted by vegetation, evaporation and infiltration. So
Ia is highly variable but generally is correlated with soil and cover parameters. After several
studies Ia was found to be approximated as,
0.2SIa =
Substituting Ia in the above equation, we get
1.4............................................ eq
0.8S)(P
0.2S)(P
Q
2
+
−
=
For convenience in evaluating antecedent rainfall, soil conditions, a land use and
conservation practice (SCS, 1985) defines:






−= 10
CN
1000
25.4S
CN is an arbitrary curve number varying from 0 to 100.
2.3. Rainfall Data
Rainfall data was collected from Meliaputti rain gauge center which is the only one center
situated to the nearest for the study area. The recorded daily rainfall data was collected during
the period 2005-2014. The daily rainfall data has been used for determination of storm events
to identify anti moisture conditions. Precipitation in the study area is mainly concentrated in
two rainy seasons, from June to September and October to November. The study area receives
about 55% of rainfall from the south-west monsoon during the months of June to September.
2.4. Soil Textural Map
The soil map has been collected from National Bureau of Soil Sciences (NBSS). Two types of
soil textural classes have been identified and the areal extent of these classes is as follows.
S.No Textural Class
Area
Km2
%
1 Sandy Clay 0.59 47.96
2 Sandy Loam 0.64 52.04

Figure 2.4. Soil Texture Map of Mini watershed
2.5. Hydrologic Soil Groups (HSG)
Soils are classified into hydrologic soil groups to indicate the minimum rate of infiltration
obtained for bare soil after prolonged wetting. The HSG are used in determining runoff curve
numbers. Infiltration rates of soils vary widely and are affected by subsurface permeability as
well as surface intake rates. Soils are classified into four HSG namely A, B, C, and D, according
to their minimum infiltration rate, which is obtained for bare soil after prolonged wetting
(USDA, 1986). The infiltration rate is the rate at which water enters the soil at the soil surface.
Hydrologic soil groups derived based on the textural classes of the soils in the study area are
given as follows.
Soil Textural Class HSG Area in Km2
Sandy loam A 0.64
Sandy clay D 0.59
HSG A: Soils have low runoff potential and high infiltration rates;
HSG D: Soils have high runoff potential. They have very low infiltration rates;
2.6. Curve Number (CN) Values
In order to determine the curve number values the land use categories in the study area were
considered. Standard SCS curve number values (USDA, 1986) were assigned for each land use
and soil group combination. Table 2.0 presents the curve number values and the corresponding
land and soil group combination. The land use classes of the study area are settlements, crop
land, forest land, scrub and wastelands and were taken into consideration for the analysis.

Table 2.0. Curve numbers and statistical distribution of land use categories with HSG in the Mini
Watershed
S.No Land use HSG
Curve
Number
(CN)
Area (A)
km2 CN*A
1 Settlements
A 77 0.000000 0.000000
D 92 0.000581 0.053452
2 Crop land
A 72 0.023784 1.712448
D 91 0.055341 5.036031
3 Forest land
A 45 0.000000 0.000000
D 83 0.000000 0.000000
4 Scrub
A 68 0.600199 40.81353
D 89 0.504263 44.87941
5 Wastelands
A 68 0.016597 1.128596
D 89 0.026546 2.362594
2.7. Antecedent Moisture Condition (AMC)
Antecedent soil moisture condition has an important effect on the runoff. Considering this, SCS
developed three antecedent soil moisture conditions such as AMC I AMC II and AMC III.
Prior to the estimation of runoff for a storm event, the curve numbers should be adjusted on the
basis of the season and 5-day antecedent precipitation. The AMC as described by McCuen
(1982) is the initial moisture condition of the soil, prior to the storm event of interest and this
parameter is taken as an index based on seasonal limits for the total 5-day antecedent rainfall
as follows.
AMC class
5-day antecedent rainfall (mm)
Dormant season Growing season
I <12.5 <35
II 12.5-27.5 35-52.5
III >27.5 >52.5
The following equation is used for calculating the weighted curve number,
∑=
×
=
n
1i i
ii
A
ACN
CN
Where
CNi = curve number of each land use-hydrologic soil group
Ai = area of each land use-hydrologic soil group
n = class number of land use-hydrologic soil group
The weighted curve number was computed using the above formula for AMC II condition
and the obtained value is 84. CN values for AMC-I and AMC-III can be computed using the
following empirical equations (Chow, 1964).
( )IICN0.05810
IICN4.2
ICN
×−
×
=
( )IICN0.1310
IICN23
IIICN
×+
×
=
The weighted curve number obtained from the calculations for AMC-II is 78,
corresponding to this value of the conversion curve numbers for CNI and CNIII are 60 and 89,
respectively. The obtained curve number values have been taken into consideration for

estimating the potential maximum retention (S) of the soil with water for AMC-II of CNII
using the equation as follows.
The calculated values of S is 71.64 for AMC II, 169.33 for AMC I and 31.39 for AMC III
conditions. The obtained S value is substituted in the equation 4.1, to the each storm event for
estimating the runoff.
2.8. Runoff Estimation
The daily rainfall data for 10 years and also the weighted curve number values in the present
study have been taken into the consideration for estimation of runoff using SCS CN method.
The runoff is calculated from the different storm events of observed rainfall during the years
2005 to 2014. Estimated runoff for each and every storm event in different AMC conditions
for all the years is presented in Table 3.0. The runoff contribution is generally higher during
later part of the monsoon months that has been resulted as higher observed runoff. If the storm
event rainfall is less than 25 mm, then it was not considered for determination of runoff because
it does not contribute any runoff. Most of the major storm events occurred in the months of
September and October. The highest precipitation occurs during cyclonic storms, which results
in peak flows in the local drainage. Such cyclonic storms are very common during late July,
August, September and October months. The average annual runoff in the study area was
estimated to be 510.04 mm which corresponds to about 37.41% of average annual rainfall of
the study area. It was also observed from the data, the runoff varies widely from 8.9% (2005)
to 61.63% (2014). The Table 4.0 values represents the annual rainfall-runoff relationship
during 2005-2014, which is indicating that the overall increase in runoff and decrease in the
rainfall trend of the study region. Most of the major storm events occurred in the months of
September and October. The highest precipitation occurs during cyclonic storms, which results
in peak flows in the local drainage.
Table 3.0. Runoff estimation for each storm event during the period from 2005-2014
Date of storm-event Storm Rainfall
(P) mm
5 day total
antecedent
rainfall (mm)
AMC
Class
Storm runoff
(Q)
mm %
17.04.2005-18.04.2005 71.4 0.0 I 6.81 9.54
24.07.2005-26.07.2005 71.4 38.8 II 25.31 35.44
08.08.2005-14.08.2005 185.8 13.0 I 71.85 38.67
01.10.2005-03.10.2005 58.8 34.4 I 3.20 5.44
22.10.2005-23.10.2005 30.2 0.0 I 0.08 0.27
01.11.2005-03.11.2005 77.2 27.2 I 8.83 11.44
15.03.2006-17.03.2006 43.8 5.8 I 0.55 1.26
25.04.2006-26.04.2006 26.2 0.0 I 0.36 1.39
16.05.2006-17.05.2006 34.0 7.2 I 0.00 0.00
27.05.2006-29.05.2006 60.6 65.2 III 34.43 56.81
04.06.2006-06.06.2006 62.2 0.0 I 4.06 6.53
22.06.2006-25.06.2006 50.8 20.2 I 1.54 3.03
28.06.2006-05.07.2006 380.8 15.6 I 233.14 61.22

01.08.2006-04.08.2006 178.2 27.2 I 66.42 37.27
11.08.2006-17.08.2006 113.6 16.4 I 25.53 22.47
21.08.2006-30.08.2006 106.2 9.6 I 21.65 20.39
04.09.2006-05.09.2006 40.0 13.2 I 0.21 0.54
15.09.2006-21.09.2006 90.6 0.0 I 14.24 15.72
29.09.2006-30.09.2006 71.0 0.0 I 6.68 9.41
28.10.2006-30.10.2006 49.6 0.0 I 1.34 2.70
21.06.2007-30.06.2007 286.5 19.2 I 151.25 52.79
19.07.2007-21.07.2007 63.1 0.0 I 4.30 6.82
02.08.2007-08.08.2207 127.4 5.8 I 33.28 26.12
02.09.2007-06.09.2007 93.6 5.2 I 15.58 16.64
10.09.2007-18.09.2007 102.0 61.4 III 72.08 70.67
20.09.2007-24.09.2007 111.8 21.6 I 81.33 72.74
09.02.2008-11.02.2008 43.8 0.0 I 20.43 46.64
24.03.2008-25.03.2008 29.6 0.0 I 9.94 33.59
27.05.2008-28.05.2008 41.2 6.8 I 18.39 44.64
22.06.2008-23.06.2008 63.0 1.0 I 36.51 57.96
11.07.2008-14.07.2008 32.2 23.6 I 11.72 36.41
18.07.2008-21.07.2008 82.6 35.8 II 33.31 40.33
27.07.2008-29.07.2008 76.4 84.4 III 48.44 63.40
02.08.2008-04.08.2008 108.8 33.4 I 78.49 72.14
07.08.2008-10.08.2008 104.7 108.8 III 74.62 71.27
16.08.2008-17.08.2008 89.4 0.0 I 60.34 67.49
05.09.2008-07.09.2008 87.0 2.0 I 58.12 66.81
09.09.2008-18.09.2008 214.6 87.0 III 181.04 84.36
22.09.2008-23.09.2008 57.6 34.6 I 31.84 55.29
12.07.2009-16.07.2009 90.2 3.6 I 61.08 67.71
18.07.2009-22.07.2009 90.4 84.6 III 61.26 67.77
15.08.2009-16.08.2009 55.6 10.8 I 30.14 54.21
25.08.2009-26.08.2009 81.0 23.8 I 52.62 64.96
01.10.2009-04.10.2009 132.2 28.6 I 100.80 76.24
03.04.2010-06.04.2010 166.2 0.0 I 133.68 80.43
13.06.2010-14.06.2010 36.6 0.0 I 14.90 40.71
18.06.2010-21.06.2010 43.0 36.6 II 8.20 19.06
01.07.2010-02.07.2010 96.8 6.2 I 67.21 69.44
05.07.2010-08.07.2010 48.4 96.8 III 24.14 49.87
20.07.2010-22.07.2010 25.2 15.4 I 7.12 28.24
28.07.2010-30.07.2010 42.8 26.2 II 8.10 18.92
03.08.2010-06.08.2010 119.0 12.6 I 88.17 74.09
26.08.2010-28.08.2010 82.8 10.4 I 54.26 65.53
03.09.2010-09.09.2010 120.4 1.0 I 89.50 74.34
24.09.2010-26.09.2010 83.8 0.0 I 55.18 65.85
06.10.2010-09.10.2010 51.0 9.4 I 26.28 51.53
15.10.2010-18.10.2010 163.4 18.6 I 130.96 80.15
30.10.2010-02.11.2010 88.0 6.2 I 59.04 67.09
08.11.2010-09.11.2010 77.0 0.0 I 48.98 63.61
06.12.2010-10.12.2010 125.8 0.0 I 94.66 75.25
20.05.2011-21.05.2011 40.8 0.0 I 18.08 44.32
12.06.2011-14.06.2011 66.8 0.0 I 39.85 59.66
25.06.2011-26.06.2011 71.0 0.0 I 43.58 61.39

05.07.2011-08.07.2011 101.0 25.2 I 71.15 70.44
30.07.2011-02.08.2011 143.2 4.0 I 111.39 77.78
17.09.2011-18.09.2011 37.6 26.4 II 5.71 15.18
21.06.2012-22.06.2012 76.6 0.0 I 48.62 63.47
01.07.2012-04.07.2012 48.0 64.6 III 23.81 49.60
03.08.2012-06.08.2012 44.0 23.0 I 20.59 46.79
01.09.2012-06.09.2012 210.4 0.0 I 176.92 84.09
02.09.2012-04.09.2012 45.6 48.0 III 21.87 47.95
08.09.2012-10.09.2012 57.2 45.6 III 31.50 55.07
18.09.2012-19.09.2012 46.2 10.6 I 22.35 48.37
24.09.2012-26.09.2012 42.6 32.4 I 19.48 45.74
01.10.2012-03.10.2012 34.6 44.4 II 4.47 12.92
23.04.2013-25.04.2013 49.4 56.2 III 24.96 50.52
11.06.2013-13.06.2013 75.8 18.4 I 47.90 63.19
23.06.2013-24.06.2013 44.8 20.2 I 21.23 47.38
14.07.2013-16.07.2013 31.2 74.4 III 11.03 35.35
19.07.2013-22.07.2013 101.2 31.2 I 71.33 70.49
04.08.2013-06.08.2013 83.4 0.0 I 54.81 65.72
11.10.2013-12.10.2013 41.0 0.6 I 18.24 44.48
21.10.2013-27.10.2013 507.2 0.0 I 471.38 92.94
20.11.2013-22.11.2013 27.2 0.0 I 8.37 30.76
25.05.2014-27.05.2014 158.6 0.0 I 126.30 79.63
15.07.2014-16.07.2014 65.2 42.2 II 21.12 32.40
18.07.2014-22.07.2014 65.8 67.4 III 38.97 59.23
28.07.2014-06.08.2014 89.2 0.0 I 60.15 67.43
15.08.2014-19.08.2014 137.6 58.2 III 105.99 77.03
21.08.2014-23.08.2014 52.8 111.2 III 27.78 52.61
28.08.2014-07.09.2014 240.2 52.8 III 206.25 85.86
04.09.2014-07.09.2014 161.4 58.6 III 129.01 79.93
18.09.2014-20.09.2014 33.6 26.2 I 12.71 37.84
11.10.2014-14.10.2014 201.0 0.0 I 167.69 83.43
Such cyclonic storms are very common during late July, August, September and October
months. The average annual runoff in the Mini Watershed was estimated to be 510.04 mm
which corresponds to about 37.41% of average annual rainfall of the study area (Table 4.0). It
was also observed from the data, the runoff varies widely from 8.9% (2005) to 61.63% (2014).
The linear diagram (Figure 3.2) represents the annual rainfall-runoff relationship during 2005-
2014, which is indicating that the overall increase in runoff and increase in the rainfall trend of
the study region.
Table 4.0. Trends in rainfall and runoff in the study area
Year Rainfall
Runoff
mm %
2005 1304.6 116.08 8.90
2006 1740.0 410.15 23.57
2007 1299.6 378.26 29.11
2008 1446.8 663.21 45.84
2009 992.10 337.74 34.04
2010 1626.2 910.38 55.98
2011 1210.6 289.76 23.93
2012 1214.6 369.61 30.43

2013 1343.4 729.24 54.28
2014 1453.8 895.97 61.63
Average 1363.2 510.04 37.41
Figure 3.2. The linear diagram represents trends in rainfall and runoff during 2005-2014
3. SUGGESTIONS
The suitable locations for Check dams are suggested by using the maps of slope and Drainage
network. The suitable locations of Check Dams are located geographically where slope meets
the junction of drainage network (Figure 3.0). These locations are Check dams are shown in
Figure 3.0.
Figure 3.0. Suitable locations for Check Dams and Artificial Recharge structures

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