20320140503016

International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print),
ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME
151
A STUDY ON VARIATIONS IN WATER PRODUCTIVITY BY USING GIS
BASED EPIC MODEL
Santhosh Ram(1)
(1)
Assistant Professor , M.E, Department of Civil Engineering, SRM University,
Ramapuram, Chennai.
ABSTRACT
Rapid growth of human population increases demand for the agricultural products, in order to
ensure food security over coming decades the total food production should be increased with
available limited water resources. The gross production will increase with improving Crop water
productivity by studying and understanding the major driving factors that greatly influence on it.
The lower Bhavani system (LBP) is taken as study area. The high variability in rainfall, hot
climate and change in irrigation water quality leads to challenge for agriculture. Among the
distributaries in the LBP Kugalur and Mangalapatti distributary were selected from the head and tail
reaches of this system. The analysis of irrigation water quality and simulation of crop water
productivity (CWP) are the main core of this study. The simulation of CWP done by usage of
(GEPIC) GIS based Environmental Policy Integrated Climate Model. GEPIC is the agro-ecosystem
simulation model to evaluate spatial and temporal dynamics of crop water productivity and yield in
daily time step. The water quality analysis is done by collecting the samples in the study area and
analyzing the quality parameters in the laboratory.
The productivity can be increased by identifying various driving factors which are solely
responsible for the optimum crop yield such as soil parameters, climatic factors, land use pattern,
cropping pattern, quality of irrigation water and management factors like amount of irrigation and
fertilizer usage. The influence of variation of these driving factors on the CWP was analyzed.
Through this work, high water productivity was obtained for Mangalapatti distributary compared to
Kugalur distributary. The sugarcane has higher water productivity of 3.742 kg/m3
in Tail-Head
reach. It shows that water productivity variations are based on the variations in the influencing factor
mainly the management activities like crop selection, amount of irrigation water applied, fertilizer
application and farm management. The water quality results show that irrigation water in the selected
distributaries is highly suitable for agricultural crops.
Keywords: Water Productivity, GIS Based EPIC Model, Water Quality Analysis.
INTERNATIONAL JOURNAL OF CIVIL ENGINEERING
AND TECHNOLOGY (IJCIET)
ISSN 0976 – 6308 (Print)
ISSN 0976 – 6316(Online)
Volume 5, Issue 3, March (2014), pp. 151-159
© IAEME: www.iaeme.com/ijciet.asp
Journal Impact Factor (2014): 7.9290 (Calculated by GISI)
www.jifactor.com
IJCIET
©IAEME

152
1. INTRODUCTION
Rapid growth of population and limited quality water resources, there is a need to increase
better management and some effective strategies of water resources in worldwide. Formulations for
maintaining or improving the environment are based on an effective strategy of productivity.
Therefore, wastes and nonproductive uses must be carefully scrutinized to identify potential savings.
The detailed study on effective concepts and better analysis are helps to improve the production.
Over the past few years, the concept of water productivity become more important in the
agriculture production, and turns the focus to irrigation water from land as an effective factor of
agricultural production. The main purpose of this study is to shows the conceptual framework for
calculating crop yield, crop water productivity and impacts of variations in some major driving
factors of productivity like irrigation water quality, climatic soil and crop parameters.
The term water productivity refers to the magnitude of output or benefit resulting from the
input quantum of water as applied on a unit base. The concept is based on “more crop per drop” or
“producing more food from the same water resources” or “producing the same amount of food from
less water resources”.
In the domain of agriculture, it is expressed as the net consumptive use efficiency in terms of
yield per unit depth of water consumed per unit area of cultivation.
1.2 Crop water productivity
Agricultural water productivity can be expressed as a physical productivity in terms of yield
over unit quantity of water consumed (Kg per unit volume of water) in accordance with the scale of
reference that includes or excludes the losses of water or an economic productivity replacing the
yield term by the gross or net present value of the crop yield for the same water consumption
(Rupees per unit volume of water). The irrigation water productivity is a ratio between yields of
irrigated crop to the amount of irrigation water applied.
The variability in quality of irrigation water is directly based on both types and amount of
dissolved salts in that water. The domestic and industrial discharge is main sources for this salt and
they follow the flow path of the water when it’s introduced in that water. The salt content in soil
increasing with increases of total salt content of the irrigation water. The evaporation and
consumptive use of salts and minerals in the irrigation water leads to ultimate sink of irrigated soil
and crop grown on that soil. So, the quality of irrigation water consider as an important driving
factors for sustainable management of water productivity and soil resources.
Irrigated agriculture is dependent on an adequate water supply of usable quality. The
evaluation of quality of water based on the chemical and physical characteristics of that water and
only rarely is any other factors considered important. Here attempt has been made to assess the
irrigation water quality of Lower Bhavani Project distributaries.
The quality characteristics is analyzed in the present investigations are as follows: Negative
logarithm of hydrogen ion concentration (pH), Electrical Conductivity (EC), Chloride (Cl), Sodium
(Na) and Potassium (K), Calcium (Ca), Magnesium (Mg), Carbonates (CO3) and bicarbonates ratio
(HCO3).
1.3 Objectives of this Study
• To estimate the water productivity for several important crops.
• To perform water quality analysis at specific locations.
• To compare the variations of water quality and other important factors

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2 MATERIALS AND METHOD
2.1 Study Area
The Bhavani is an important tributary of the Cauvery River in its mid-reaches in Tamil Nadu.
It originates in the Silent Valley forest in Kerala and flows in a south-easterly direction for 217 km
until it joins the Cauvery at a town named Bhavani. A major portion (87 per cent) of this area is
situated in Tamil Nadu. The Lower Bhavani Reservoir was constructed across the confluence of
Bhavani and Moyar providing storage of 906 M m3. The Bhavanisagar dam was executed during
1948-1955 and the canal system was constructed in 1956.
The LBP Canal was originally designed for the ayacut area of 83,975 hectare, and the ayacut
is spread over in Sathyamangalam, Gobi, Bhavani, Erode, Perundurai and Kangayam taluks. In
addition to the above ayacut, an extent of 1,012 hectare is also benefited in Karur district. The
catchment area for this system is 4200 km2.
The Lower Bhavani Basin lies between 110 15‟ N and 110 45‟ N latitudes and 770 00‟ E and 770 40‟
E longitudes. The area is comprised of hilly regions and plain terrain with maximum and minimum
altitudes of 1487 m and 215 m above mean sea level (MSL) respectively.
In this system most of the command area localized was heavily porous, red soil, gravelly
mixed with pebbles its leads to a heavy seepage losses. An allowance for transmission losses of
33.33% was made in the design. The Fig 2.1 shows the Bhavani basin map.
(Sources: IWMI Research report 129, 2009)
Fig 2.1: Bhavani Basin Map
The climate of the study area is dry, except during the monsoon season. The first two months
of the year are pleasant. The north-east monsoon gets vigorous only during October or November.
The average rain fall of the basin is 715 mm.
This basin has a well developed dendritic to sub dendritic drainage system, which indicates
the presence of rock in uniform resistance. The area has a steep gradient with the drainage towards
the river and also the rocky substratum depth with an overburden of 72.97 m of porous gravelly soil,

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the seepage is much and finds its way into local drainage. The topography of this region mainly
controls the occurrence of groundwater, land use and drainage pattern. Scattered hillocks of
moderate elevation occur within the uplands. The plains area is characterized by gentle undulations
with a general gradient east and south-east. The terrain slopes towards south-east. The basin area
includes reserve forest; build up lands, agricultural field and barren lands. The Lower Bhavani Study
Area Map shown in Fig 2.2.
(Sources: Hand Book of Junior Engineer (PWD))
Fig 2.2: Lower Bhavani Study Area Map
The main canal covers a total command area of 83,772 ha (2, 07,000 acres).The main canal
has three major distributaries taking off at 53 km, 101km and 119km, 69 distributaries ,196 minor
distributaries and 118 sluices. Below the distributaries the water courses carry the water to the field
channels, which directly irrigate lands. Up to watercourse, the maintenance responsibilities lie with
PWD, the field channels are maintained by the farmers themselves. The canal will be thrown open
for Irrigation from August 15th and the water will be allowed for Irrigation up to 15th of December
(Turn 1) to raise wet crops and after December 15th and up to March 15th (Turn 2) water will be
allowed for dry crops in rotation method. In first turn, allowing the supply of 24 TMC for wet crops
is found to be optimal period for wet crops. But it is felt , in second turn for dry crops, with a total
permissible quantity of 12 TMC is found to be inadequate due to the prevailing of hot summer and it
is recommended to raise the quantity from 12 TMC to 14 TMC for the turn.
The major crops are paddy, banana, groundnut and sugarcane. Wet season cropping pattern
(August - December) is mostly influenced by paddy crop. Dry season is normally meant to grow
irrigated dry crops like millets, pulses, cotton, oilseeds etc., however sugarcane and banana are
annual crops and hence grown only by farmers with assured ground water facilities.
For this study the distributaries are selected from the head and tail reaches of the main system
respectively.

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Table 2.1 shows the details of selected distributaries.
Table 2.1: Details of Selected Distributaries
S.NO
Name Of
The
Distributary
Reach Length(km)
Ayacut area (ha)
Turn I Turn II
1 Kugalur Head 15.054 2012.978 1948.226
2 Mangalapatti Tail 6.207 700.463 595.200
2.2 GIS Based EPIC Model
GEPIC is a GIS-based agroecosytem model integrating a bio-physical EPIC model
(Environmental Policy Integrated Climate) with a Geographic Information System (GIS). The
GEPIC model can be used to simulate the spatial and temporal dynamics of the major processes of
the soil–crop–atmosphere-management system. The GEPIC version 0.1(2009) is used for this study
to simulate the crop water productivity.
Model needs set of input data to assess the water productivity for the number of individual
crops selected in the head and tail reach of the distributary command area. The all input data should
be in the raster data format. It can be done by using ArcGIS software.
Following data are needed to simulate the crop water productivity in GEPIC model,
• Soil physical parameters
• Crop parameter
• Land use
• Climatic data
• Information about location
DEM
Slope
• Management data
Irrigation
Fertilizer application
This model generates the results in daily, monthly production for the corresponding crop. It
can simulate the yield, crop water productivity, evapotranspiration, irrigation requirement, harvest
index. The validation of the model is done by comparing the model output with field data. The model
is assured if the validation gives good result. The model output is in the form of raster image format
and text format.
The model can simulate the various output parameters in daily time steps as follows,
• Yield (kg/ha)
• Crop water productivity (kg/m3)
• Biomass (kg/ha)
• Evapotranspiration (mm)
• Irrigation water requirement (mm)

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2.3 Methodology
To performance the water quality analysis and simulation of crop water productivity would
be the main task of this study. Previous work carried out on important driving factors of water
productivity were analyzed in the context of improving the productivity. Hence this study has been
planned to analyze the response of crop water productivity through variations in some important
driving forces of it.
This chapter is organized into four sections. The first section: data collection. The second
section: analyze the crop water productivity for varies crops. It contains crop selection and usage of
GEPIC model. The third section: performance the irrigation water quality analysis and comparing the
existing quality with standards. The fourth section: analyze the variations in various driving factors
of crop water productivity.
This study need various data related to the factors of water productivity. For easy collection
and working it grouped into two categories as primary data and secondary data. The approach used
for this data collection is primary surveys. Two distributaries in Lower Bhavani project would be
selected for the study. There are Kugalur and Mangalapatti distributary respectively from head and
tail reach of the main canal. The primary data such as crop details, land use, fertilizer and irrigation
management are can be acquire by conducting questionnaire survey. The farmers consider for the
survey in each distributary based on the random sampling method. The information would be
obtained through detailed questionnaires in systematic manner. This data would be a source to
compare and evaluate the productivity with help of secondary data.
Some of the main features consider for questionnaire survey giving below:
• Background of the farmer
• Crop details
• Irrigation management activities
• Problems facing
• Water and land resources
The secondary data such as required soil, climate (rainfall, maximum and minimum
temperature), and site location data would be collected from Public Works Department (PWD) and
Agricultural Engineering Department. The DEM data collect from SRTM model for the required
boundary. Flow data will collect from the LBP flow measurement relevant sources.
The suitable major crops are selected crops based on the soil type, irrigation water quality,
crop varieties and fertilizer usage. Data regarding primary and secondary data (soil, climate) are
verified and given as input for GEPIC model.
The irrigation water (canal water, well water) quality assess by analyzing the quality parameters as
per Irrigation Water Quality. The water samples from the selected distributaries will be collect and
test in the laboratory.
The main reason for analyzing the water quality is as follows,
• To know the existing quality of Water
• To compare it with the standards
• To evaluate the impact of water quality on crop water productivity
The collected samples will be analyzed for the level of water quality parameters and compare
with general guide lines for irrigation water provided by Irrigation Water Quality index. The impact
of the quality of water in crop water productivity will be evaluated. The standard guide lines for
intercept the quality of irrigation water is shown in table 2.2,

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The following water quality parameters is used for analyze the irrigation water quality,
• pH
• Electrical conductivity(EC)
• Total Dissolved Solids(TDS)
• Sodium and Potassium
• Calcium
• Magnesium
From above quality parameters the sodium absorption ratio (SAR) will be calculated by ratio
of sodium [Na+] to calcium [Ca++] and magnesium [Mg++].
SAR
3. RESULTS AND DISCUSSION
3.1 Assessment of Irrigation Water Quality
The values of selected water quality parameters in collected samples were assessed in
laboratory by using appropriate procedures and the results are compared with Indian standards for
irrigation water. The EC and TDS values represent the salinity hazard, one can conclude that the
agricultural fields around the selected two distributaries have the best quality of irrigation water
when salinity hazards are considered. .
The SAR and EC result shows that quality of irrigation water in selected distributaries is
good when infiltration and permeability problems are considered. Based on sodium, potassium and
pH results are also shows that the water in that area is suitable for irrigation. From this assessment in
those areas the CWP is not varied based on the quality of irrigation water.
3.2 Assessment of Water productivity by using model
The simulated water productivity results show that the productivity of water varies with
respect to variations in the driving factors. Based on the cropping pattern the water productivity
values are differed. The sugarcane has the higher water productivity value in the all different reaches
of Kugalur and Mangalapatti distributaries compared to the other selected crops. The average water
productivity values of sugarcane in all reaches is 3.478 kg/m3
and its range varies from 3.742 kg/m3
in the Tail-Head reach to 3.172 kg/m3
in the Head-Head reach of the selected distributaries. The
physical water productivity value for sugarcane is high in all reaches due to higher yield than other
two selected crops.
The lower water productivity value is obtained for groundnut in different reaches of two
selected distributaries. The average physical water productivity of groundnut is 0.385 kg/m3
and its
value varies from 0.362 kg/m3
in Tail-Tail reach to 0.402 kg/m3
in the Head-Tail reach of selected
distributaries. The groundnut productivity value is lower due to low yield obtained compared to other
two crops in all reaches of Kugalur and Mangalapatti distributaries. The average water productivity
of paddy is 0.91 kg/m3
and its range varies from 0.824 kg/m3
in Head-Head reach to 1.021 kg/m3
in
the Tail-Head reach of two selected distributaries.
In the order of higher to lower water productivity values, the crops are listed as sugarcane,
paddy and groundnut. The Fig 5.4.1 shows the variations in the simulated water productivity based
on crop selection in the different reaches of Kugalur distributary and Mangalapatti distributary.

158
4. CONCLUSION
The water productivity is mainly dependant on the amount of yield from the crop and amount
of water applied to the crop. These two factors are mostly responsible for the variations in the water
productivity. Other than these factors the climatic conditions, soil characteristics, timing of irrigation
and management activities like fertilizer applications were influenced on water productivity. The
amount of fertilizer application and the timing of fertilizer application were also considered
important for crop yield. The following driving factors are responsible for the variation in water
productivity:
• Soil type, climatic conditions at the location, elevation of the field and quality of irrigation
water supplied to the field are responsible for the variations in water productivity.
• Management activities like fertilizer application, tillage practices, crop selection, method of
irrigation water application, weeds and pest control in the field.
• Farmer’s knowledge on new techniques in agriculture, economic status of the farmer and
number of labours used.
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