chapter 6 estimation results and discussion -...
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Chapter 6 Estimation Results and Discussion
6.1 Rationale
6.2 Precipitation
6.3 Land use/land cover
6.4 Normalized differential vegetation index
(NDVI)
6.5 Surface runoff
6.6 Evapotranspiration (ET)
6.7 Recharge modeling results
6.8 Groundwater abstraction (PG)
6.9 Irrigation return flow (IRF)
6.10 Components groundwater balance
6.1 Rationale
Precise estimation of groundwater balance components plays a pivotal role in
management of groundwater resources especially in arid and semiarid areas with
declining groundwater levels. In agricultural economy like India there has been a
tremendous increase in the population which resulted in decline of natural resources
like groundwater. Conventionally, groundwater balance components are estimated
using point data measurements, involving larger uncertainties due to heterogeneous
landscape, hydroclimate and lithology. Inadequate data poses a challenge to policy
makers and hydro-geologists in planning and management of water resources. Due to
limited availability of water resources in semiarid hard rock terrains and enormous
groundwater abstraction for agricultural activities, severe deficit of water is faced by
southern India. It is indispensable to understand the mechanisms and driving forces
behind major components of the water balance to allow reliable estimation of the water
resources.
With the advancements in remote sensing science, a number of techniques are
available to assess new sources for distributed spatial data for certain parameters
including: evapotranspiration (Bastiaanssen et al. 1998; Su 2002; Jia et al. 2003; Han
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and Yang 2004), rainfall (Herman et al., 1997; Mitra and Bohra 2009; Nair et al. 2009;
Mitra et al. 2013) and runoff (Melesse and Shih 2002; Huang et al. 2006; Rao and
Chakraborti 2000; Bo et al. 2011; Rolland & Rangarajan 2013). Remote sensing offers
data in spatial format at larger scale rather than point format, therefore minimizing the
cost and the uncertainties. This chapter provides details of the estimation results of
major GWB components in the study area.
6.2 Precipitation
The average annual precipitation in the area is 812 mm. About 90% of the
precipitation occurs in SW monsoon season (June-October) in the study area. Two
precipitation datasets were used for the study viz., satellite based TRMM data and in-
situ instrumental data generated by Mandal Revenue Office (MRO) of Gajwel Mandal,
Medak district.
Figure 6.1 Monthly rainfall distributions for the year 2008-2009 derived from TRMM and MRO measurement.
Twenty four temporal rainfall variability maps from both the datasets (TRMM and
MRO) were generated in this study for the year (2008-09). However, no spatial
variability in the rainfall pattern was observed with TRMM as well as in-situ data, as
there is only one rain gauge in the watershed and the resolution of TRMM is low
(0.25°x0.25°) in comparison to watershed area. The monthly distribution of rainfall
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measurements using TRMM and MRO data for the studied time interval (Figure 6.1)
shows total satellite based rainfall from June 2008 to May 2009 is 864 mm out of
which 842 mm is from SW monsoon and 22 mm from NE monsoon. The in-situ
rainfall for the same period is 806 mm out of which 787 mm is from SW monsoon and
19 mm from NE monsoon.
Figure 6.2 Correlation of TRMM and MRO derived monthly rainfall data for the year 2008-2009.
Figure 6.3 Correlation of TRMM and MRO derived daily rainfall data the for year 2008-2009
R² = 0.7795
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The monthly distribution of TRMM and MRO data shows good correlation (r2= 0.78).
However, some quantitative discrepancies between the two datasets at monthly scale
were observed (Figure 6.2). For instance, rainfall measurements by TRMM for July
and August 2008 are 211 mm and 350 mm, while M R O estimates precipitation as
136 mm and 364 mm, respectively. Correspondingly, rainfall is estimated by TRMM
as 32 mm and 22 mm in October and November 2008 but M R O provides an
estimation of 10 mm and 09 mm, respectively. An annual discrepancy of ~7% was
observed between two datasets. Hence it has been observed that the TRMM estimates
are generally higher than the in-situ measurements by MRO during the recorded
period. This discrepancy can be attributed to very low density of rain gauge stations
(only one measurement point at Gajwel town) and low spatial resolution of the TRMM
data. It is worth mentioning that there is also a considerable discrepancy in daily
variation (r2=0.093) of rainfall intensity and number of rainy days between the two
data sets (Figure 6.3). The TRMM show high number of rainy days (70 days) with less
rainfall intensity while as in-situ measurements show less number of rainy days (40
days) and high rainfall intensity.
The downloaded TRMM data files in ASCII format were converted into tiff images
using ArcGIS 9.3. As the main motive of this study was to generate seasonal and
annual rainfall maps, monthly based rainfall themes were merged to seasonal and
annual maps (details of procedure are given in chapter four; section 4.2.4). TRMM
data also showed negligible spatial variation due to low spatial resolution (0.25°x
0.25°). In-situ rainfall data of Gajwel and other neighboring watersheds was
interpolated using inverse distance weighted (IDW) method in ArcGIS. The IDW
method is more efficient when the actual data points are less (Purushotham et al.
2012). Nevertheless it was found that the neighboring rain gauge stations are far from
the study site, hence no change in spatial variability of rainfall was observed in the
watershed.
The accuracy of rainfall dataset is very crucial for precise estimation of GWB
components. Bauer et al. (2002) suggested that TRMM has tendency to
overestimate rainfall due to the fact that it can sometimes misidentify a variety of earth
surfaces for precipitating clouds. Nicholson et al. (2003) reported an excellent
agreement between gauge measurements and TRMM (3B43, PR, and TMI) data on
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monthly to seasonally timescales in West Africa. In Indian context studies made by
(Rahman and Sengupta; 2007; Nair et al. 2009) conclude that TRMM 3B43 dataset
shows good correlation with rain gauge data. Narayanan et al. (2005) reported that the
satellite algorithm does not pick up very high and very low daily average rainfall
events resulting in data discrepancy with rain gauge (IMD) data.
While undertaking this study, it was comprehended that the rainfall measurements
by both the methods are still a subject of debate in terms of data resolution. Therefore,
it is important to mention that spatial resolution of TRMM sensor needs to be
addressed in near future to help minimize over or under estimation of rainfall. It is
also necessary that data density of in-situ measured rainfall stations should be
improved by either grid based and/ or village level based installation of rain
gauges. These approaches will reduce the data ambiguity and in turn will help in
accurate estimation of rainfall vis-à-vis groundwater management.
In view of the above statements, it is suggested that both the data sets to be used
independently for estimation of groundwater balance component viz., runoff and
recharge in the present study to generate a less biased estimation scenario. The
seasonal and annual rainfall themes generated from both datasets in this study
were used as input for runoff and recharge modeling.
6.3 Land use/land cover
To understand the spatial extent of various land use/land cover classes in the study area,
IRS LISS-IV imagery of October 2008 was employed using supervised classification in
ERDAS Imagine 9.1 (Figure 6.4). The results of land use mapping reveal that 84%
of land is occupied by different agricultural classes like irrigated fields (13%), orchids
(9%) and other rainfed crops like maize cotton, pulses etc. (62%). Water storage tanks
occupy 3% of the area, 6% of land is under forest cover, 3% is built-up and 4% is
wasteland. Figure 6.4 reveals various land use/land cover classes in the study area.
Irrigated fields mostly consist of paddy crop requiring maximum exploitation of
groundwater. The pie diagram given in Figure 6.5 shows the percentage of various
land use/land cover classes in the study area.
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Figure 6.4 IRS LISS-IV derived land use/land cover map of the study area.
The major land use/land cover categories of the study area include the agricultural
land, built up land, forests, waste land and water bodies as described below.
Agricultural land: The major cropping area in the Gajwel watershed is in the valley
fills and in the flat areas. Groundwater and a few storage tanks across the higher order
streams are the main source of irrigation for these crops. Crops are grown both in
kharif and rabi seasons because of the groundwater irrigation. The surface storage
tanks across higher order streams provide irrigation to a limited extent in the some of
the upland regions of the study area.
Irrigated lands: These land use classes include paddy and a small proportion of other
high water demanding crops like vegetables and flowers that are grown in both kharif
and rabi season. In this type of cropping, land is irrigated by rain water and
groundwater. Groundwater irrigation is huge in rabi season. This type of cropping
pattern covers 11.0 km2 (5.9 km2 in rabi) area in kharif season in the watershed. Other
agricultural land includes the areas that are cultivated in kharif season. In this season,
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maize, cotton and pulses cover most of the cultivated land in the watershed. These
crops are generally affected by the prolonged dry spells resulting in the reduction in
yield and sometimes the total destruction of the crops. This agricultural land holds
largest share and occupies an area of about 51.84 km2 which is around 62 percent of
the watershed.
Orchids: The orchids in this area include grapes, mango, sweet lime and guava in
addition to teak plantations. This land use category occupies an area of 7.09 km2
which covers around 9 percent of the total watershed area.
Built-up land: A built-up land is defined as an area of human habitation that has
been developed as a result of non-agricultural use. The major category of this kind
which can be easily identified by satellite images in the study area includes residential
areas, industrial areas, institutions and transportation network. The important
settlements which have been identified within the study area include Gajwel,
Dacharam, Sangupally, Rangampeth, Kasaram, Jalgaon, Rajredpally, Giripally,
Sangapur and Bayaram. Apart from these areas, some minor habitations were also
identified. The total area under this category comes to about 2.8 km2 which
corresponds to about 3 percent of the total watershed area.
Wasteland: About 4 percent (3.0 km2) of the study area is degraded which is not
used for agricultural purpose. These areas can be brought under cultivation with
appropriate soil and water management.
Forest: These lands cover an area of 5.6 km2 which is about 6 percent of the
watershed and characterized with shallow soil depth, moderate to steep slopes with
eucalyptus plantation of varying height and densities. Maximum concentration of this
land cover is found in the south-western parts of the watershed.
Rocky outcrops: These are the exposed granitic outcrops and boulders which are
devoid of any vegetation and are present mostly in the southern region of the study
area. The exposed rock outcrops are mostly spheroidal in shape which occurs in
cluster and sometimes in isolation. This land cover extends on an area of 0.13 km2.
Water bodies: Areas with impounded water can be put under this category. They
include natural as well as man-made water sites. Many tanks in the study area receive
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water supply mainly through runoff. Tanks of varying size can be seen scattered in
the watershed and occupy 2.7 km2 which accounts for 3% of watershed area.
Figure 6.5 Percent distribution of land use/land cover classes in the study area.
Table 6.1 Summary of land use accuracy assessment.
Land use influences processes like infiltration, runoff, groundwater abstraction and
evapotranspiration. Land use map was used as input parameter for runoff modeling,
Forest 6%
Waste land 4% Built-up
3%
Orchids 9%
Irrigated fields 13% Other
agriculture 62%
Water 3%
Class Name
Reference Totals
Classified Totals
Number Correct
Producers Accuracy
Users accuracy
Forest 4 4 4 100.00% 100.00% Wasteland 5 4 3 60.00% 75.00% Outcrops 0 0 0 --- --- Orchid 2 2 2 100.00% 100.00%
Built-up 3 3 3 100.00% 100.00% Paddy Fields
4 4 4 100.00% 100.00
Rainfed Crops
11 10 9 81.82% 90.00%
Scrub Lands 49 51 47 96.08% 92.45% Water Tanks 2 2 2 100.00% 100.00%
Totals 80 80 74
Overall Classification Accuracy = 92.31% Overall Kappa Statistics = 0.85
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estimation of irrigation return flow and groundwater abstraction. It was also used for
validation of ET maps. Land use map show a positive correlation with ET and
groundwater abstraction maps. The irrigated areas demarcated in land use map show
highest ET values as well as highest values of groundwater abstraction. Similarly
built-up shows the lowest values of ET and groundwater abstraction. Forests show
moderate ET and low groundwater abstraction values. Extensive field reconnaissance
was carried out for the validation of generated land use/land cover map. The validation
was carried out using accuracy assessment technique in ERDAS Imagine 9.1 based on
80 random land use points. A high precision of 92.31% with overall Kappa statistics
of 0.85 was observed. The summary of accuracy assessment is given in Table 6.1.
6.4 Normalized differential vegetation index (NDVI)
As revealed by normalized differential vegetation index (NDVI) results, the vegetation
conditions during and after monsoon is good which start declining due to non-
availability of rainfall in post monsoon season. NDVI maps generated for the month of
October (monsoon) shows good vegetation cover in all the agricultural and forest land
use type (Figure 6.6a-d). With the start of post monsoon season vegetation in rainfed
areas starts declining and only irrigated and forest areas show good vegetation cover.
During summer months from March to May the vegetation in the study area gets
drastically reduced. On visual inspection of NDVI images almost entire watershed
(excluding forests) is vegetation deficit in peak summer months of April and May.
NDVI maps show positive correlation with the land use and ET maps.
NDVI maps were employed for validation of land use map using visual interpretation.
Moreover vegetation index directly represents the potential areas of evapotranspiration
therefore validating the generated ET maps of the study area. Many workers have
validated evapotranspiration by incorporating the vegetation index (NDVI), because
the amount of vegetative cover affects transpiration (Sandholt and Andersen 1993;
Carlson et al. 1995). Inter-seasonal variability of the vegetation indices is directly
attributed to water availability in the form of rain and groundwater in the area. The
total vegetation scenario in the area keeps on changing with the seasons. During
monsoon, vegetation is highest as huge amount of water is available in the form of
precipitation that promotes agriculture and natural vegetation. While as post monsoon
and summer months do not receive sufficient amount of rainfall and the only available
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source of water is groundwater and agriculture is confined to limited pockets in the
form of irrigated lands. Therefore, NDVI is an effective vegetation index to demarcate
vegetation conditions of the area and can be effectively used to validate ET maps.
Figure 6.6 NDVI based vegetation scenario in the study area during different seasons.
6.5 Surface Runoff Daily runoff was computed using National Resources Conservation Services Curve
Number (NRCS-CN) based ArcCN-Runoff model. Since this study is established at
seasonal and annual scale, the daily runoff data was merged into monthly, seasonal
and annual themes using ArcGIS 9.3. Runoff was found to be highest during summer
monsoon (June to October) season as most of the rainfall takes place during this period
of the year. Infact it is worth mentioning that no runoff was observed between
November 2008 to May 2009 (18.2 mm rainfall from MRO; and 22.6 mm rainfall
from TRMM) in the study area. The annual runoff ranges from 0-200 mm with most
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of the areas showing 50 mm (4.2 Mm3) of runoff i.e., 5.7% of the total annual rainfall
(864 mm) using TRMM data. Moreover, the runoff ranges from 0-230 mm with an
average of 60 mm (5 Mm3) i.e. 7.4% of the annual rainfall (806 mm) using MRO data.
While the values of runoff show a wide range, the mean was calculated using image
statistics in ERDAS Imagine 9.1.
Results reveal that MRO based rainfall shows higher runoff values than satellite based
TRMM data. It was observed that intensity of daily TRMM rainfall is low in
comparison to MRO rainfall data. Runoff being a direct result of rainfall intensity,
therefore shows higher values with MRO rainfall than TRMM based rainfall data.
Figure 6.7 and 6.8 show the spatial variability of the runoff in the study area. Runoff
shows maximum values for settlements and wastelands and minimum values for tank
sites, this is obvious because settlements and waste land do not favour high recharge as
the soil cannot hold water in absence of prominent vegetation cover whereas on other
hand tanks act as a sink for recharge in the area. Other vegetated areas support a good
recharge and less runoff.
Surface runoff is one of the essential GWB components and any discrepancy in
estimation of runoff could lead to erroneous estimation of actual amount of recharge to
ground. In absence of stream flow data, the surface runoff in the study area was
estimated using the NRCS-CN method. As this study focuses on spatio-temporal
variability of GWB components, GIS based ArcCN-Runoff model which works on
principle of NRCS-CN method but generates data on spatial scale was a preferred for
this study. This model has been used been globally used by many workers to estimate
surface runoff from ungauged agricultural watershed (Zhan and Huang 2004; Bo et al.
2011; Hernandez-Guzman & Ruiz-Luna 2013).
The main factors that contribute to runoff in the areas with low slope are rainfall, land
use/land cover, hydrological soil groups and antecedent soil moisture conditions
(AMC). NRCS curve number can be successfully applied in the areas with slope of less
than 5% with high accuracy and curve number adjusted for steeper slopes (Ebrahimian et
al. 2009). Curve number (CN) is used to determine the quantities of rainfall infiltrating
into an aquifer and surface runoff. A high curve number means high runoff and low
infiltration, whereas a low curve number means low runoff and high infiltration. Curve
number is a function of land use, hydrologic soil group (HSG) and antecedent soil
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moisture condition (AMC). The land use map was prepared from satellite data with
ground accuracy of 92.31% with eight land use classes, the soil map was digitized and
classified to three soil types that fall in “C” group of hydrological soil group (HSG)
and support low infiltration rate (0.05-0.15 inch hr-1). This precipitation data was used
to design 5-day AMC and on the basis of daily rainfall analysis, it was observed that the
watershed has both AMCI and AMCII conditions based on the previous five day rainfall
measurement (AMC1 if 5-day rainfall is < 23 mm inch and AMCII if 5-day rainfall is
between 23-40 mm; USDA 1972). Therefore, new CNs were designed based on local
hydro-metrological condition; built-up (CN- 81), other agricultural land (CN- 78), irrigated
crops (CN- 75), outcrops (CN- 90), forest (CN- 73), orchid (CN- 70), wastelands (CN- 90)
and water (CN- 0). With an area of 2.7 km2 and storage capacity of 5.4 Mm3 storage tanks
cover about 3% of the watershed and act as additional source of water. Tank network in the
study area is spread along the drainage system to capture the maximum surface runoff. As
most ~90% of the runoff is captured by the tanks only small amount water actually result in
drainage discharge at watershed outlet.
Figure 6.7 Annual spatial distribution of runoff in the study area computed from TRMM rainfall data.
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All the above mentioned data sets were processed in GIS based ArcCN-Runoff model
to generate daily runoff and this daily runoff data was merged to generate monthly,
seasonal and annual runoff as per the requirement of this study (details of runoff
modelling are given chapter five; section: 5.5). The generated runoff maps were used as
input in recharge model to compute seasonal and annual recharge.
Figure 6.8 Annual spatial distribution of runoff in the study area computed from MRO rainfall data.
6.6 Evapotranspiration (ET)
Evapotranspiration was retrieved using SEBAL algorithm and Landsat TM/ETM+
satellite data. The SEBAL model calculates ET as a function of latent heat for each
image pixel (30 m) from the energy balance equation using more than 30
computational steps as discussed in details in chapter five (section: 5.2-5.4).
𝛌𝐄𝐓 = 𝐑𝐧 − 𝐇− 𝐆 − − −−−−− (𝟔.𝟏)
Where; λET is the latent heat flux, Rn is the net radiation flux at the land surface, H is
the sensible heat flux to the atmosphere and G is the soil heat flux, all expressed in
(Wm-2).
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Instantaneous evapotranspiration (ETinst) values are acquired at satellite overpass time.
These instantaneous data sets are then extrapolated to daily ET (ET24) data. The focus
of this study was to analyze and estimate ET on seasonal and annual scales. Therefore,
ETinst data was extrapolated to daily evapotranspiration data (ET24) which was in turn
converted to monthly ET values in SEBAL. This was achieved by the solving the net
radiation budget, soil heat flux and sensible heat flux equations that control and govern
the processes of evapotranspiration (details of ET modeling are given in chapter five;
section: 5.2-5.4 and Bastiaanssen et al. 1998a and 1998b).
Figure 6.9 Spatial distribution of evapotranspiration for kharif season in the study area.
The monthly ET themes were merged in ArcGIS to generate seasonal and annual ET
maps of the study area (Figure 6.9-6.11). Simulating the SEBAL algorithm for annual
ET retrieval, it was found that ET is largest GWB component and forms ~80% of the
annual rainfall.
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Figure 6.10 Spatial distribution of evapotranspiration for rabi season in the study
area.
The estimated ET results are given with respect to two rainfall datasets viz., TRMM
and MRO. The results reveal that ET values at 78% and 83% of the total annual
rainfall with reference to TRMM (864 mm) and MRO data (806 mm) respectively.
This shows that ET is main natural processes by which water is consumed from
watershed. Average annual ET in the watershed ranges from 450-749 mm, highest
evapotranspiration values are observed during kharif season with ET values ranging
from 224-406 mm. The mean annual ET of the study area was calculated using image
statistics in ERDAS Imagine 9.1 and was found to be equal to 674 mm. The stronger
extra-terrestrial solar radiation, increased precipitation and cultivation of paddy and other
rainfed crops during kharif explain why evapotranspiration is higher during this period of
year. During rabi season ET values range from 226-343 mm, which is less in comparison
to kharif but very high when compared to low precipitation amount in the season. This is
attributed to extensive pumping of groundwater for irrigation which promotes ET as can
be observed from land use and NDVI maps. In both kharif and rabi seasons maximum
values of ET are found in irrigated areas, followed by agricultural lands and forests;
settlements and wastelands show lowest ET values in the study area.
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Figure 6.11 Annual spatial distribution of evapotranspiration in the study area.
One of the main and challenging objectives of this study was to quantify actual
evapotranspiration. ET is the process in which water is transferred from the surface to
the atmosphere as a combination of soil and water evaporation and vegetation
transpiration. While evaporation is a result of only physical processes like diffusion
and convection, transpiration is controlled by biological process like photosynthesis.
Therefore, ET estimation involves both transpiration and evaporation and hence
SEBAL is preferred choice over conventional methods as it computes both evaporation
and transpiration. One of the main advantages of SEBAL for this type of application is
the determination of actual ET at pixel level. The error in using potential
evapotranspiration for actual evapotranspiration is revealed when the conveyance
system water balance indicates that the irrigated lands cannot be transpiring at the
potential rate. Although satellite based ET retrieval is widely used all over the globe there
are certain problems which need to be addressed like high cloud cover images in monsoon
seasons actually cause a data gap. Satellite images for the month of June and August
2008, showed a cloud cover of over 90%. Therefore, ET was calculated from July and
September imagery as vegetation and climatic conditions do not show any major change.
However, it can lead to minor errors in actual ET retrieval if not computed efficiently.
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ET was also calculated from Penman-Monteith method, since this method gives only
point measurements and do not represent the actual conditions at a specific pixel, the
relationship is only used as an indicator of correlation between conventional and
SEBAL derived ET within the study area. To see the correlation of spatial ET with
point based ET data, ET measurements were carried using Penman-Monteith. Allen et
al. (1998) proposed calculation of actual evapotranspiration (ETact) by first estimating
the reference evapotranspiration (ETref) and then applying a corresponding crop
coefficient. Reference evapotranspiration is defined by Allen et al. (1998) as the rate of
evapotranspiration from a hypothetical crop. Daily potential ET values were computed
using this method. These values were then converted to reference ET and actual ET
values employing crop coefficient method. The details of reference ET retrieval are given
in section 5.2-5.4 of chapter five (section: 5.4).
It was difficult to assign a single crop coefficient for complex vegetated areas with wide
inter seasonal variation in vegetation cover. Therefore, the crop coefficient values were
assigned based on land use, NDVI and crop calendar of the area. Crop coefficient (KC)
value of 0.6 for months of June and July, 0.7 for months of Aug to Oct, 0.5 for months of
Nov to Feb, 0.4 for month of March and for summer months of April and May with
almost negligible vegetation cover a KC value of 0.1 was assigned. The results reveal
that ET for kharif and rabi are 529 mm and 410 mm respectively making annual ET
for one hydrological year equal to 939 mm.
In view of the results obtained by Penman-Monteith method using a single crop
coefficient value (Kc) for whole watershed, it was observed that actual ET
measurements using this single reference point for varied vegetation cover can lead to
erroneous measurements by assigning equal weightage of actual transpiration to water
bodies, agricultural land, barren and built-up areas. Which either leads to over
estimation of ET in case of barren and built-up areas or underestimating ET for water
bodies and agricultural land. In comparison SEBAL estimates the actual ET at a
resolution of 30 m with incorporation of vegetation, leaf area index surface and air
temperature, surface albedo and other parameters that affect the amount of actual
evapotranspiration (details given in chapter five; section: 5.4). Thus it is observed that
satellite derived actual evapotranspiration measurements are more precise than
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conventional methods and can be a landmark in precise estimation and management of
groundwater resources.
6.7 Recharge modeling results
There are numerous methods/models available for recharge estimation viz., SWAT
(Perrin et al. 2012), WetSpa (Wang et al. 1996; Batelaan and Smelt 2007), water table
fluctuation method (Marechal et al. 2006) and tracer techniques (Rangarajan and
Athavale 2000). As this study aims to estimate spatial distribution of recharge at
watershed scale using geospatial data sets, a simple spatial water balance approach
after Khalaf and Donoghue (2012) was found to be the best approach. In this method,
seasonal/ annual recharge was estimated from GIS based recharge model working on
water balance method, where seasonal/annual ET and runoff were subtracted from
seasonal/annual precipitation. The observation period (year) was divided into kharif
(June-October) and rabi (November-May) seasons, where evapotranspiration (ET)
derived from SEBAL and ArcCN derived runoff (Q) was subtracted from the
precipitation (P) on both seasonal and annual scale.
𝐑 = 𝐏 − (𝐄𝐓 + 𝐐) −−−−−−−−− 𝟔.𝟐
Where; R is recharge; P is rainfall; ET evapotranspiration and Q is runoff, all
expressed in mm.
As there is negligible rainfall in the rabi season, no runoff and rainfall recharge was
observed during this period. Therefore, the kharif recharge was taken as annual
recharge in the study area. The main focus of this study was to evaluate the actual
recharge scenario in the watershed due to groundwater over exploitation through 1134
bore wells extensively used for irrigation.
The results obtained using this approach confirm that a small portion of precipitation
with an average of 16.3 % and 9.6% using TRMM and MRO based rainfall data sets
respectively contribute to groundwater in form of groundwater recharge. However, it
may be noted that these values only represent average recharge with reference to
average annual rainfall, while as the actual distribution of recharge ranges from 20-300
(11.8 Mm3) mm and 20-210 (6.6 Mm3) mm using TRMM and MRO rainfall data sets
respectively (Figure 6.12 and 6.13). Settlements and wastelands show minimum
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recharge values because of enhanced runoff due to sparse vegetation cover. Irrigated
fields show moderate recharge in comparison to other agricultural lands, which may be
directly attributed to high evapotranspiration rate of these precincts. Tanks show
maximum recharge rate this is attributed to minimum amount of runoff in from tank
sites
Rain is limited mostly to monsoon months; 90-95% of rainfall occurs between June to
October as shown by rainfall data (Figure 6.1). The concentration of rainfall within
June and October enhances the potential for recharge in these months. For rest of the
year it is observed, that there is almost negligible rainfall recharge.
Figure 6.12 Annual spatial distribution of recharge using TRMM data in the study
area.
Although the estimated recharge results show a spatial distribution of recharge and
don’t represent a point value but in order to correlate the results with existing methods
average values of the estimated recharge were computed. The average recharge values
derived in the present method shows a good correlation with the existing recharge
measurement in hard rock terrains by CGWB 1998 (12%); Marechal et al. 2006 (13-
19%); Dewandel et al. 2008 (12-19%) and Perrin et al. 2012 (05-12.5%) of average
annual rainfall. However, the recharge estimated from two data sets show a
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considerable discrepancy which may be attributed to the variation of about ~7% in
amount of rainfall estimated from the satellite (864 mm) and in-situ (806 mm) data
sets during the studied time interval.
Figure 6.13 Annual spatial distribution of recharge using MRO data in the study area.
This study has comprehended the present rainfall dataset inefficacy in terms of poor
rain gauge distribution and low TRMM spatial resolution. This data inefficiency due to
poor rain gauge distribution can be minimized by increasing the rain gauge
distribution. It is equally important to enhance the spatial resolution of rainfall
estimation satellites. To reduce the biasness in the recharge estimation both the
satellite and in-situ data sets have been used individually to compute recharge along
with the precisely estimated ET and runoff from SEBAL and ArcCN-Runoff models.
Therefore, it is suggested that geospatial data based recharge estimation is efficient
than the point measurements as the recharge is estimated at spatio-temporal scale. The
distribution and variation of the recharge in a watershed computed at spatial scale, is
more effective in planning and management of groundwater resources rather than
assuming average recharge value for whole area. This cost effective method can be
applied over wide areas with minimum field data requirement. Although the results
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estimated with this method are in agreement with the established results this method
being new in this region should therefore be further validated in south Indian scenario
with more experiments and testing.
6.8 Groundwater abstraction (PG)
Groundwater abstraction is a major process responsible for depleting sub-surface
water in the areas marked by extensive irrigation. Two methods were applied to
estimate the net groundwater abstraction in the study area viz., land use method and
borewell inventory method. The amount of annual groundwater abstraction estimated
using land use method is 230 mm i.e. 19.3 Mm3 while as annual groundwater
abstraction computed from well inventory method is 243 mm i.e. 20.4 Mm3.
In land use method the area of irrigated crops (~92% of paddy) was computed from
satellite derived land use map as 11 km2 and 5.9 km2 in kharif and rabi seasons. The
mean daily crop water requirement for different crops has been evaluated at the
seasonal scale by existing values established (details are given in chapter four; section:
4.4.5). The number of irrigation days for kharif and rabi were calculated to be 120 and
105 based on existing literature (Perrin et al. 2008 and 2012), crop pattern and crop
calendar (Fig. 2.9 chapter two) of the study area. The mean seasonal water requirement
for irrigated crops was deduced to be 9 and 12 mm for kharif and rabi seasons
respectively.
The annual groundwater abstraction computed from land use method was computed as
230 mm with 141 mm in kharif and 89 in rabi season. The amount of abstracted
groundwater was calculated from land use method by applying following relationship:
𝐏𝐆 = 𝐏𝐠𝐢 × 𝐒𝐢 × 𝐍𝐝 − − − −− (𝟔.𝟑)
Where; PG is abstracted water, Pgi is daily input of water (mm day-1) m3, Si= area of
cultivated crop (m2), Nd=no. of irrigation days in season, kharif and rabi.
The groundwater abstraction from well inventory method was computed using
following relationship after Marechal et al. (2006):
𝐏𝐆 = 𝐏𝐠 × 𝐏 × 𝐍𝐝 × 𝐍𝐰−−− (𝟔.𝟒)
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Where; PG is abstracted water (mm), Pg is daily pumping hrs, p is pumping rate (hr-1),
Nd is number of irrigation days and Nw is number of irrigation wells.
The annual groundwater abstraction from well inventory method is estimated as 243
mm with 130 and 113 mm in kharif and rabi seasons respectively. The data collected
from MRO (2009) reveals that study area has 1940 borewells out of which 1134 are
used for irrigation purposes, which was verified during field reconnaissance. The field
experiments and existing data from MRO confirm that groundwater is abstracted at an
average of 10 m3 hour-1 for an average of 8 hours per day and the pumping is
intensively carried out for 225 days in a year. In order to understand the spatial
variations of groundwater abstraction, the well inventory data at multi-village level
(merged data of 2-3 villages; details given in chapter four; section: 4.4.5) was
interpolated using IDW interpolation method in ArcGIS 9.3 (Figure 6.14).
Figure 6.14 Merged village wise distribution of groundwater abstraction by well
inventory method.
The village level abstraction map prepared by interpolation of borewell inventory data
shows the distribution of groundwater withdrawal and its variation at watershed scale.
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Moreover, spatial distribution of groundwater abstraction can be also visualized from
the irrigated areas on land use map (Figure 6.4) in the study area.
The results reveal that both land use and well inventory methods show a good
correlation and it is therefore suggested that land use method can be efficiently used to
compute of groundwater abstraction in the areas with insufficient well data. Land use
method is cost efficient for estimating groundwater abstraction especially in Indian
scenario. Where well inventory data like pumping duration and pumping hours mostly
requires very precise field observation and in-turn increases the field investment. Flow
meter method is the direct method to compute groundwater pumping; in which flow
meters are attached to every individual well to estimate the groundwater withdrawal.
This method although being direct method of groundwater withdrawal estimation is
economically not promising in Indian scenario for example in the study area 1134 flow
meters need to be installed which instead of sustainable management issues will raise
the cost of water supply. Therefore it is comprehended from this study that land use
method can be efficiently and economically used to compute the groundwater
abstraction.
As established by the results from this study irrigated agriculture uses the largest share
of groundwater, to meet the high crop water demand in absence of perennial surface
water and long dry periods from nearly mid-October to mid-June. This huge amount of
abstraction with more crop water requirement along with increased ET has resulted in
severe encumbrance on groundwater resources in the study area. The current
groundwater withdrawal trend is unsafe for future groundwater sustainability in the
area. As the results of this study are in consistency with the results given in similar
hydro-climatic areas by other workers like Marechal et al. 2006; Dewandel 2008;
Perrin et al. 2008 it is therefore highly important to maximize the surface water usage
from tanks, promote rainfed crops and reduce the cropping of high water demanding
crops especially in dry rabi season to minimize the high groundwater abstraction in
this study area in particular and other areas in general.
6.9 Irrigation return flow (IRF)
Part of the abstracted groundwater is added back to the aquifer in terms of irrigation
return flow and has been estimated to be ~ 40-50% of the total abstraction (Dewandel
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et al. 2008). Rest of the abstracted water (~ 50-60%) serves as elixir to agriculture and
as a major GWB component for evapotranspiration.
Estimation of irrigation return flow (IRF) mostly requires heavy field investments and
can only be conducted in experimental farms. IRF has been estimated at 48-50% in
semiarid hard rock terrains of southern India by Marechal et al. (2006) and Dewandel
et al. (2008) for paddy. As per field reconnaissance and land use data paddy shares the
dominant irrigated agriculture (~ 92%). Irrigation return flow is governed by land use
and soil type. Considering the similar agro-climatic scenario of our study area with
respect to Maheshwaram watershed of Andhra Pradesh, IRF was assumed to be 47%
(Marechal et al., 2006; Dewandel et al., 2008). Excess groundwater abstraction is
mostly carried out to fulfill the water requirement of paddy crop, since it requires a few
centimeters of standing water in the field and this result in a good amount of return
flow.
Many workers have attempted to estimate IRF for paddy crop. Jalota and Arora (2002)
estimated it to be 51% in Northern India; 59% in Taiwan (Chen et al. 2002) and the
value estimated by the Andhra Pradesh Ground Water Department stand at 60%
(APGWD, 1977). Marechal et al. (2006) and Dewandel et al. (2008) estimated the
IRF to be 50% and 48% respectively, in semiarid hard rock areas of Andhra Pradesh.
For vegetables and flowers, the estimated irrigation return flow coefficients have been
estimated as 25% and 12% respectively. These results are consistent with 20% average
assessed by the Central Ground Water Board (GCWB 1998) for such crops.
Land use is a key parameter that governs the amount of groundwater abstraction and
irrigation return flow. To estimate IRF in the study area both land use and well
inventory based groundwater abstraction data was used as input data (details given in
chapter four; section: 4.4.6). The relationship given by Marechal et al. (2006) and
Dewandel et al. (2008) for has been used to compute IRF.
𝐈𝐑𝐅 = 𝐂𝐟 × 𝐏𝐆 − − − −−−(𝟔.𝟓)
Where; IRF is irrigation return flow (mm), Cf (0.47) is irrigation return flow
coefficient and PG (230 mm by land use method and 243 mm well inventory method)
is groundwater abstraction (mm).
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The results reveal that annual IRF using land use and well inventory data are 108 (9
Mm3) and 114 mm (9.5 Mm3) respectively, revealing a good correlation of the
estimated results.
As land use of the study area governs the amount of groundwater withdrawal both land
use method and well inventory method directly or indirectly depends on land use.
Therefore this study has impartially demonstrated the efficiency of land use method
for understanding the spatial variability of IRF vis-à-vis its relationship with different
crops. The study also emphasizes the role of IRF as an important factor for accurate
assessment of GWB components in the study area.
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