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

0

50

100

150

200

250

300

350

400

JUN

JUL

AU

GSE

PO

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EC JAN

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nfal

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Rainfall-MRO Rainfall-TRMM

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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

0

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nfal

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R² = 0.0939

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nfal

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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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