J Indian Soc Remote Sens DOI 10.1007/s12524-015-0541-6

RESEARCH ARTICLE

Integrated Assessment of Groundwater for Agricultural Use in Mewat District of Haryana, India Using Geographical Information System (GIS) Mamta Mehra 1 & Bakimchandra Oinam 2 & Chander Kumar Singh 1

Received: 23 February 2015 / Accepted: 20 December 2015 # Indian Society of Remote Sensing 2016

Abstract Groundwater is the primary source of irrigation in the Mewat District of Haryana, India. Thus the population of Mewat is considerably reliant on groundwater for meeting their livelihood and domestic needs. An integrated framework was developed using Geographical Information Systems (GIS) for the assessment of groundwater resources for agricultural use. Groundwater quality was assessed using weighted index method and classified into good (36 %), moderate (47 %) and poor (17 %) zones. Groundwater potential on the other hand was analysed using the weighted overlay approach using eight independent variables. The groundwater potential was further classified into good, moderate and poor zones which occupied 29, 61, and 10 % of the study area respectively. Groundwater vulnerability was assessed using DRASTIC method and was found low, moderate and high in 46, 23 and 31 % of the region respectively. A positive correlation value of 0.23 was observed between groundwater quality and potential. Correlation between groundwater quality and vulnerability was found −0.31. Multivariate clustering method was used to integrate the results of groundwater quality, potential and vulnerability in the study area. The integrated groundwater map was classified into five zones in the study area. The result was validated with the soil fertility, irrigation source, and crop yield. Integrated assessment of groundwater resources in the study area; thus provides useful information to decision

* Chander Kumar Singh [email protected]

1

Department of Natural Resources, TERI University, New Delhi, India

2

Department of Civil Engineering, National Institute of Technology, Imphal, Manipur, India

makers for undertaking measures for sustainable groundwater resource management in the study area. Keywords Groundwater quality . Groundwater potential . Groundwater vulnerability . Integrated assessment . GIS . DRASTIC

Introduction Agriculture in India is mostly dependent on irrigation through groundwater and canals. More than 50 % of irrigation demand is met through groundwater resources in India (CGWB 2014; Mall et al. 2006) which is further strengthened by the fact that 70–80 % of the value of irrigated production in India comes from groundwater irrigation (Surinaidu et al. 2013). However, over the period of time groundwater resources of India have been depleted in order to meet the continuous increasing demands of different developing sectors, agriculture being one of them. The advent of green revolution, no doubt, has helped India to increase the agriculture production. However this increase was observed at the cost of degradation of groundwater resource (Radhakrishna 2009). A continuous decline in groundwater level has been observed in many parts of India (Shah 2010; Jin and Feng 2013). World Bank (2011), study has shown that nearly 29 % of blocks in India are falling under semi-critical, critical, or overexploited categories in terms of groundwater. According to satellite based evidences the groundwater over the Indian states of Rajasthan, Punjab, Haryana, and Delhi is declining at a mean rate of 4.0 ± 1.0 cm/year (Rodell et al. 2009). Besides rapid groundwater depletion, India is also facing serious challenges of geogenic and anthropogenic groundwater contamination. Around 200 district spreads across 19 states of India have groundwater contamination (Murty and Kumar 2011) ranging from high

J Indian Soc Remote Sens

salinity (Misra and Mishra 2007), heavy metals (Singh et al. 2011a), fluoride (Singh et al. 2011b, 2012), and arsenic (Chakraborti et al. 2003). Inefficient use of chemical fertilizers and pesticides has further contaminated groundwater with nitrate and dissolved salts (Kaur et al. 2009). Thus groundwater vulnerability should be looked not only from the quantitative but also through qualitative perspective. For assessing groundwater quality for irrigation several methods have been used, worth mentioning are weighted index method (Stigter et al. 2006; Reza and Singh 2010; Singh et al. 2011c; Adhikari et al. 2012). Groundwater potential has been modelled in GIS using several multicriteria decision analysis methods (MCDM) (Mukherjee et al. 2012; Mallick et al. 2015). DRASTIC method has been widely used to model the groundwater vulnerability because of its cost effectiveness, easy operation, minimum data requirement, and clear explanation of groundwater vulnerability (Wu et al. 2014). Thus it is extremely important to understand groundwater resources in a holistic manner in order to utilize and formulate its efficient usage and planning. The current study attempts to understand groundwater resources in context of its use for Fig. 1 Study area - Mewat district, Haryana, India

irrigation in the Mewat district of Haryana, India. The study demonstrates integration of groundwater potentiality, quality and vulnerability using mulvariate clustering approach to decipher integrated groundwater assessment map.

Study Area The study was conducted in the Mewat district of Haryana located between 27°30′11″ to 28°21′01″ N and 76°54′46″ to 77°16′01″ E -a part of the hot dry semi-arid eco-subregion. It has five development blocks, Taoru, Nuh, Nagina, F.P Jhirka, and Punhana (Fig. 1). The study area has a total geographical area of 1499.46 km2. The study area receives an average annual rainfall of 594 mm with large fraction of rain being received in a short period of 2–3 months during monsoon (Singh et al. 2007). Analysis of past rainfall data shows huge variability both in temporal and spatial extent (Fig. 2). A slight decreasing trend (R2 = 0.034) has been observed in the annual rainfall (1966– 2010). The rainfall is slightly higher in block F.P Jhirka in

J Indian Soc Remote Sens

Fig. 2 Annual rainfall, block wise decadal rainfall and groundwater depth in the study area

comparison with other blocks, however significant differences are prominent within blocks in recent years (Fig. 2). Groundwater reservoirs in the district are largely made up of alluvium of quaternary age with fractures, joints and crevices of hard rocks. The deeper layers of the parent material are composed of sand, clay and kankar (Arya et al. 1999; Khan 2007). Variation in groundwater depth in different blocks over period from 1975 to 2007 was analysed (Fig. 2) and continuous increase in groundwater depth was observed with maximum increase in block Taoru (R2 = 0.827). However, the average groundwater depth changed from 4.02 ± 2.75 m (1975) to 10.45 ± 7.55 m (2007). Variation in block level groundwater depth is attributed to groundwater extraction, which further depends on groundwater quality. Groundwater quality is good in Taoru block and in villages along the foothills of Aravallis (Arya et al. 1999; Khan 2007; Thomas et al. 2012). Groundwater in the shallow aquifer in about 55 % area of the study area has moderate to high salinity and is unfit for irrigation. High amounts of dissolved solids and high proportion of alkali salts (bicarbonates) limit the use potential of groundwater for agriculture in large parts of the study area (Arya et al. 1999). Unlined drains and canals are further degrading groundwater due to seepage in nearly 39.6 % of the region (Kaur et al. 2009). Groundwater salinity is existing problem in some of the areas whereas groundwater withdrawal is leading towards ingress of saline water in freshwater resources (Khan 2007; Thomas et al. 2012). Thus it is equally important to understand groundwater vulnerability with respect to surface/ subsurface contamination of groundwater. Water availability and quality in time and space being primary constraint to crop intensification, cropping pattern reflect how farmer optimally use in terms of crop production strategies. The gross cropped area in the region is 214719 ha with cropping intensity of 150 % out of which net sown area is 151180 ha. Pearl millet and sorghum are monsoonal crops grown in 27.11 % of the net cultivated land while the remaining area is left fallow to be cropped in post-monsoon. Other crops like barley, sesame, cluster bean and pulses are grown in very small area. Nearly 66 % of the net cultivated area remains fallow during the monsoon contributing to greater soil erosion and reduced biomass availability and nearly 14 % area remains fallow in post-monsoon.

Methodology The broad methodological framework to analyse groundwater resource for agriculture is given in Fig 3. The details on data extent and source are given in Table 2. The methodological details for groundwater quality, groundwater potential and groundwater vulnerability modelling along with integrating all these outputs together to arrive at final integrated groundwater assessment map are discussed one by one in detail. Groundwater Quality Modelling Groundwater quality was modelled using well accepted weighted index method (Stigter et al. 2006; Singh et al. 2011c; Adhikari et al. 2012). Based on the literature review; sodium adsorption ratio (SAR), electrical conductivity (EC), and residual sodium carbonate (RSC) were identified as the key parameters to represent groundwater quality for irrigation use in India (Adhikari et al. 2012; Nag and Ghosh 2013). Relative weights of 0.5 (SAR), 0.3 (EC), and 0.2 (RSC) against an individual weight of 5 (SAR), 3 (EC), and 2 (RSC) were assigned (Adhikari et al. 2012) based on their relative implications on quality for irrigation (Eq. 1). .X n W i ¼ wi wi ð1Þ i¼0 Here, Wi stands for relative weight of parameter and wi for individual weight of parameter. Once the weights are assigned, quality rating (Qi) of the parameter was calculated using Eq. 2. The standard values of the parameter considered for quality was considered as per USSL classification (1954).   Ci *100 ð2Þ Qi ¼ Si Here, Qi stands for quality rating of individual parameter, Ci for concentration of individual parameter in each sample and Si for standard concentration value of each parameter for irrigation use. A sub-index (SIi) value was calculated for individual groundwater quality parameters (Eq. 3) which was later

J Indian Soc Remote Sens

Fig. 3 Methodology for integrated assessment of groundwater resource for agriculture

used for computing groundwater quality index (GWQI) using Eq. 4. SIi ¼ W i*Qi GWQI ¼

X

ð3Þ ½ðSISAR Þi þ ðSIEC Þi þ ðSIRSC Þi

ð4Þ

where SISAR is sub-index SAR, SIEC is sub-index EC, and SIRSC is sub-index RSC. The GWQI was generated for 57 locations for which the water quality data was available. The entire area was then reclassed into 5 classes (very good, good, moderate, poor, and very poor) based on the values of GQWI. Groundwater Potential Modelling Many methods have been developed in the past to delineate potential groundwater recharge zones in an area. Most of them have used GIS based approach (Dar et al. 2010). In the present study, groundwater potential was modelled using a PCA (principal component analysis) based weighted overlay index method (Mukherjee et al. 2012; Mallick et al. 2015). Eight

thematic layers were evaluated parameters (Table 1) to delineate groundwater potential zones. The eight variables were subjected to PCA to determine the relative influence of the variables and thus the ranking was done based on principal component loading plots. The experts opinion were taken into account while assigning weight and ranking which was compiled from a questionnaire survey given to the hydro-geologist and groundwater experts in this region. It was observed from the PCA that soil type is the most important parameter impacting groundwater recharge potential in the study area. Thus a maximum percent influence was assigned to soil types, where higher ratings were given to coarser soil types and lower to finer soil types. Soil texture determines the runoff/ recharge, coarse texture soil have more permeability than fine textured soils such as clay. LULC was observed as second most important parameter followed by slope, lineament density and geomorphology. However, DEM, drainage density and rainfall were assigned relatively lower ranking based on the results of PCA. The rates to individual class of each parameter were based on their relative importance to groundwater potential in the area based on expert’s opinion. Rates

J Indian Soc Remote Sens Table 1

Data extent and source

Groundwater variable

Modelling method

Groundwater quality

57 tube well data (2012) Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), and Residual Sodium Carbonate (RSC) Weighted overlay index Soil type 1:100,000 scale using PCA Landuse & Land cover 56 m resolution

Groundwater potential

Data type

Data resolution

Weighted index

Groundwater vulnerability DRASTIC

Validation

Data source CGWB

HARSAC NRSC

Slope Lineament Density

30 m resolution 30 m resolution

ASTER DEM ASTER DEM

Geomorphology DEM

1:100,000 scale 30 m resolution

HARSAC ASTER DEM

Drainage Density

30 m resolution

ASTER DEM

Rainfall Depth to groundwater (meter)

0.25 degree resolution 78 tube well data (2010)

CGWB CGWB

Net recharge (calculated using slope, rainfall and soil permeability)

Slope (30 m resolution) ASTER DEM, Rainfall (0.25 degree resolution) CGWB Soil permeability (1:100,000 scale) HARSAC

Aquifer media

1:100,000 scale

HARSAC

Soil media Topography Impact of vadose zone

1:100,000 scale 30 m resolution 1:100,000 scale

HARSAC ASTER DEM HARSAC

Hydraulic conductivity Soil fertility Irrigation source Crop yield

1:100,000 scale 1552 data points 1552 data points 50 data points

HARSAC Sehgal foundation Sehgal foundation Department of Agriculture, Mewat

1

Central Ground Water Board

2

National Remote Sensing Centre (NRSC) developed landuse map using Advanced Wide Field Sensor (AwiFS) sensor data

3

http://asterweb.jpl.nasa.gov/gdem.asp

4

Haryana Space Applications Centre (HARSAC), Haryana Agriculture University (HAU), Government of India

were assigned on a scale of (1–5), where rate of 1 is assigned to that class which has minimum impact on groundwater potential and vice-versa. In the LULC, higher rates were assigned to water and agricultural class while lower rates were given to built-up and wastelands. Similarly, in geomorphology, high rate was assigned to eolian alluvium and low rate to structural hills. Rates to rainfall, lineament density and drainage density classes were assigned in linear proportion to the scaling of 1–5; however the rates in reverse orders were assigned to DEM and slope classes, as higher elevation and slopes leads to more runoff and less recharge (Table 2). Groundwater Vulnerability Modelling Groundwater vulnerability was modelled using DRASTIC method. DRASTIC involves evaluation of relative importance of its seven thematic parameters [Groundwater depth (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C)] in order to map groundwater vulnerability.

The DRASTIC Index at any one cell or polygon on the map is determined as: DRASTIC Index ¼ DrDw þ RrRw þ ArAw þ SrSw þ TrTw þ IrIw þ CrCw

ð5Þ

Here; ‘r’ stands for rate and ‘w’ stands for weights of individual parameter class range or media type. Each parameter was thus assigned a constant relative weight ranging from 1 to 5 (Table 3) with most significant parameter having higher weightage (Aller et al. 1987). In the study, we used the weights assigned by (Aller et al. 1987). However, rates to different classes of each of the seven parameters were assigned based on expert’s opinion in regional context (Table 3). The rates of these individual classes were scaled from 1 to 5 (in order of low to high significance). A high rating was given to shallow groundwater depth, as percolation of contaminants would be more rapid to shallow water than compared to deep groundwater. Net recharge impacts groundwater vulnerability by two ways; one by contaminants transport and

J Indian Soc Remote Sens Table 2

Groundwater potential modeling parameters percent influence, class range and ratings

Parameter (weight in parentheses)

Class / Class range for ratings

Soil type (25)

1

2

3

Clay loam to clayey/ Clay loam to silty clay

Loam to silty loam

Sandy loam to loam Sand to sandy loam

Landuse & landcover (20) Built up

Barren/ Wasteland

Slope, degree (15)

15–25 1.1–4

26–63 0–1

Lineament density, km/Km2 (15) Geomorphology (10)

7.2–14 4.1–8

4

5

Forest/ Agriculture

Sand to loamy sand/ Coarse sand to fine sand Water body

3.6–7.1 8.1–13

0–3.5 14–21

Paleoabandoned channels Eolian alluvium

DEM (5) Drainage density (5)

Dome type residual hills/ Linear ridge/ dykes Piedmont alluvium Structural Hills 349–460 288–348 238–287 0–0.01 0.02–1 1.1–3

200–237 3.1–5

145–199 5.1–9

Rainfall, mm (5)

469–501

569–608

609–665

502–537

538–568

another by elevating groundwater level and thereby contaminating water more frequently. Thus, a high rate was assigned to areas with higher net recharge rate. Net recharge was calculated using the slope, rainfall and soil permeability information of the study area using Eq. 6 (Piscopo 2001). Recharge Value ¼ Slope % þ Rainfall þ Soil Permeability

ð6Þ

Aquifer media, represented by the consolidated and unconsolidated rock structures are responsible for governing the Table 3

flowpath and flowlength of the aquifer system. Flowlength determines processes, such as sorption, reactivity and dispersion, to occur; however flowpath decides the rate at which contaminants reach the groundwater interface. Thus a high rate was assigned to eolian alluvium type aquifer than structural hills. Soil depending on its texture influences water infiltration rates thereby regulates movement of contaminants in the soil profile. Soil texture dominated by fine-textured materials, such as silts and clays, having low soil permeability restricts contaminant movement while coarse soil texture dominated by sand having high soil permeability thus higher

DRASTIC parameters weights, class range and ratings

Parameter (weight in parentheses)

Depth to groundwater, m (5) Net recharge (calculated using slope, rainfall and soil permeability) (4) Slope % for recharge Rainfall for recharge Soil permeability for recharge Aquifer media (3) Soil media (2)

Topography, slope % (1) Impact of vadose zone (5) Hydraulic conductivity, mm/h (3)

Class / Class range for ratings 1

2

3

4

5

>20 3–5

15–20 5–7

10–15 7–9

5–10 9–11

0–5 11–13

>33 <500 Very slow

10–33 500–700 Moderate

2–10

<2

High

Very High

Dome type residual hills/ Linear ridge/ dykes Piedmont alluvium Structural Hills Clay loam to clayey/ Loam to silty loam Sandy loam to loam Clay loam to silty clay >50 20–50 10–20 Sandy mud Muddy sand 1–2 2–4 4–6

Paleoabandoned Eolian alluvium channels Sand to sandy loam Sand to loamy sand/ Coarse sand to fine sand 5–10 6–8

0–5 Sand >8

Source: Aller et al. 1987 (weights for each parameter) and expert’s opinion based on local situation (rates for each class of the parameter)

J Indian Soc Remote Sens

recharge. Thus, a high rate was assigned to coarse textured soil. Topography describing the slope and slope variability of an area controls contaminants runoff and retention on the land surface, thus a plain topography with greater water retention rate promotes greater infiltration of contaminants into the soil profile. Hence, a high rate was assigned to flat surface. Unsaturated zone above the water table referred as vadose zone determines the attenuation characteristics of the aquifer material. Thus influence of vadose zone is quite similar to the characteristics of aquifer and soil media. Similar to soil type, impact of vadose zone is found to be higher in coarser textured soil type, thus a higher rate was assigned. Hydraulic conductivity on the other hand governs groundwater flow depending on the intrinsic permeability of the material and on the degree of saturation (Aller et al. 1987). Thus higher the conductivity greater will be the movement of contaminant. The vulnerability was reclassified using quantile classification. Method for Integrated Assessment of Groundwater Quality, Potential and Vulnerability Multivariate clustering, an unsupervised classification method was used to integrate groundwater quality, potential and vulnerability results in the region. The Maximum Likelihood Classification (MLC) method, that considers both the variance and covariance of the class signatures when assigning each cell to one of the classes represented in the signature file, was used. The output raster maps of groundwater quality, potential and vulnerability were taken as an input into the MLC method for generating an Iso (Iterative self-organizing) cluster signature. The algorithm separates all cells into the user-specified (as it is an unsupervised classification method) number of distinct unimodal classes in the multidimensional space of a multiband raster. Thus, the iteration process for updating the mean values of identified class continues until the user defined number of iterations is reached or until less than 2 % of the cells change from one cluster to another relative to the new means within an iteration. For each of the class the MLC calculates the probability of the cell belonging to that a particular class. This probability and weighting logic is based on Bayesian decision rules. The actual probability values for each cell and class are determined from the means and covariance matrix for each class (stored in the signature file).

Results and Discussions Groundwater Quality Modelling The value of groundwater quality index varies from 25 to 886 in the study area. Final output was reclassified

into five classes using the indicators of good quality groundwater for irrigation use (Fig. 4). The classified map shows that 6 and 30 % of the area has very good to good quality groundwater respectively, whereas 47 % area was found under moderate quality. 12 and 5 % area have poor and very poor groundwater quality respectively. Similar findings on groundwater quality have been reported by (Arya et al. 1999) and (Khan 2007) where groundwater quality was reflected in context of salinity (Table 4). The result was also validated with the correlation between groundwater quality and depth in the district. A negative correlation of 0.52 clearly shows greater extraction of groundwater in good quality zones. EC was found moderate to poor in 90 % of the study area. This clearly shows that, groundwater quality for agriculture is largely affected by the salinity problems. Groundwater Potential Modelling Result The groundwater potential map (Fig. 5) clearly shows that groundwater potential in the study area falls largely under low to high category. There is very less percentage (0.01 %) of the study area that has very low or very high groundwater potential. Groundwater potential was found low, moderate and high in 29, 61, and 10 % of the study area respectively. The result was validated with the drawdown of individual wells and a correlation of 0.595 was obtained between the modelling result and total amount of water withdrawn from the well. Groundwater Vulnerability Modelling Groundwater vulnerability (Fig. 6) was found low to very low in 20 and 26 % of the study area respectively. However 23 % of the study area was found to be moderately vulnerable, whereas vulnerability was found to be high to very high in 17 and 14 % of the area, respectively. Multiple regression analysis was performed between the seven parameters of DRASTIC and vulnerability index. A good correlation was obtained between DRASTIC parameters and its final output with a R2 = 0.857. Statistical analysis of mean value shows that out of the seven parameters of DRASTIC modelling; depth, recharge, soil media and aquifer media are the key governing parameters of DRASTIC index used for predicting groundwater vulnerability. Moreover, analysis of coefficient of variation clearly shows that net recharge is the lowest contributor to variation in D R A S T I C in d e x , w h i l e m a x i m u m va r i a t i o n in DRASTIC index was contributed by hydraulic conductivity. Thus, net recharge was observed as the key

J Indian Soc Remote Sens Fig. 4 Groundwater quality map of the study area

parameter, impacting groundwater vulnerability the most in the area which was also validated by other researchers (Rahman 2008). Net recharge also influences depth to groundwater; rainfall is the key factor influencing the recharge rate. Groundwater vulnerability result was validated with the EC, SAR, and RSC point data values. The analysis shows that 68 % of water samples having high concentration of EC was found under moderate to high vulnerability class in the study area. Similarly, concentration of SAR and RSC was found high in 66 and 69 % of

Table 4

Groundwater quality results

Source

Good quality area (%)

Moderate to poor quality area (%)

Present study (Arya et al. 1999) (Khan 2007)

36 26 25

64 58 55

water samples corresponding to medium to high groundwater vulnerability class in the study area. Integrated Assessment of Groundwater for Agriculture The integrated assessment groundwater map, classified into 5 zones was generated using the multivariate clustering method is shown in Fig. 7. The characteristics of each zone with respect to their groundwater quality, quantity and vulnerability are given in Table 5. These five zones are distinct from each other. Zone 1 can be considered a good area for agriculture as it has good quality groundwater with moderate potential as well as lesser vulnerability to surface/subsurface contaminants. Zone 2 on the other hand has moderate quality groundwater with moderate potential, but has very low to low vulnerability to surface/ subsurface contaminants, while zone 3 has good to moderate quality of groundwater, but the area has moderate vulnerability to surface/ subsurface contaminants with low to moderate potential for groundwater recharge. However, Zone 4, which also has

J Indian Soc Remote Sens Fig. 5 Groundwater potential map of the study area

good to moderate quality groundwater is under the threat of surface/subsurface contaminant pollution as it lies in highly vulnerable area. Zone 5 which has poor quality groundwater with high vulnerability to surface/ subsurface pollutant is also not good for the study area, as these areas have the potential sites for the lateral and vertical transfer of pollutants to the good quality zone. The integrated assessment map was validated with four variables i.e. soil fertility, source of irrigation, mustard yield and wheat yield (Table 5). The validation results shows high fertile soils with good yield of both mustard and wheat crops in Zone 1 that has good groundwater quality with lower vulnerability of surface/ subsurface contaminants whereas opposite contrast is observed in Zone 5 that has poor quality groundwater with moderate to high vulnerability to surface/subsurface contaminants. Since, groundwater in Zone 5 is unfit for irrigation, the farmers in this Zone are largely dependent on rainfall for their irrigation needs. A correlation analysis was also conducted in all three groundwater variables. A positive correlation value of

0.23 was observed between groundwater quality and potential. Correlation between groundwater quality and vulnerability was found to be −0.31. Similarly a negative correlation of −0.49 was observed between groundwater potential and vulnerability. Current cropping patterns in the study area reflect constraints and opportunities which the farmers see in relation to given natural resource endowments and the level of technological and related policy and infrastructural support is available. Water availability and quality in time and space being primary constrain to crop intensification, cropping pattern reflect how farmer view its optimal use in terms of crop production strategies. The gross cropped area of the district is 214719 ha with cropping intensity of 150 % (in comparison with 175 % of Haryana state), out of which net sown area is 151180 ha. Pearlmillet and Sorghum are the main rainy season crops grown over some 27.11 % of the net cultivated land while the remaining area is left fallow to be cropped only in the post rainy season. Rainy season crops do not receive any irrigation and depend totally on seasonal

J Indian Soc Remote Sens Fig. 6 Groundwater vulnerability map of the study area

rainfall in majority of the study area except some areas where groundwater is available in good quality and in some pockets of Punhana and Nuh having access to canal water. The cultivation of rice in these pockets are on continuous rise, currently representing nearly 5.95 % of the net cultivated area Wheat and mustard are the main post rainy season crops grown on stored soil moisture and limited amount of water available from groundwater sources accounting for some 85.32 % of the net cultivated area. Although being more remunerative farmers’ preferences is to put maximum area under wheat crop, groundwater availability and quality considerations limits the area which can be devoted to wheat and mustard is thus the next choice. Other crops like barley, sesame, clusterbean, and pulses are grown in very small acreage area. Thus nearly 66 % of the net cultivated area remains fallow during the rainy season contributing to greater soil erosion and reduced biomass availability and nearly 14 % area remains fallow in post rainy season. The area under vegetable crop has grown over the

past years in areas where ground water availability and quality are more favourable.

Conclusions The integrated assessment of groundwater resources has shown that groundwater resources in the study area are being impacted by all three variables i.e., groundwater quality, potential and vulnerability. The interactions of these variables together can impact the decision making of farming communities with respect to their cropping pattern and use of groundwater resources. However, in the absence of sustainable measures for groundwater resources management, groundwater quality and quantity is deteriorating with increasing vulnerability to surface/subsurface contaminants which is further being aggravated with increasing vulnerability due to climatic parameters. The delay in onset of rainfall or less than average rainfall has forced

J Indian Soc Remote Sens Fig. 7 Integrated groundwater assessment map

farmers to use saline groundwater, that not only impacts the soil health but also impacts the crop yield. However, on the other hand groundwater extraction is higher in areas with good quality. The problems would worsen when the good quality zone also possesses higher vulnerability to surface/subsurface contaminants. This will further deteriorate the quality of groundwater in these pockets. Unavailability of groundwater quality

Table 5

data for heavy metals has limited the scope of this study to agriculture. Thus, an integrated assessment of groundwater resources is extremely important in areas where groundwater is the key source of irrigation. The integrated assessment describes the spatial variability of groundwater quality, potential and vulnerability in the region. The assessment also provides the strength and challenges in

Characteristics of integrated groundwater map

Zone Area Modeling variables (km2) Groundwater Groundwater quality potential

Groundwater vulnerability

Soil fertility

Irrigation source

Mustard yield Wheat yield (Kg/5 m2) (Kg/5 m2)

1 2 3 4 5

Very low to low Very low to low Moderate High to very high Moderate to high

High to moderate Moderate Moderate Moderate Low

Primarily groundwater Groundwater and rainfed Groundwater and rainfed Groundwater and rainfed Primarily rainfed

4.22 4.10 3.87 3.88 3.39

225 437 243 332 218

Very good to good Moderate Good to moderate Good to moderate Poor to very poor

Moderate Moderate Low to moderate Low to moderate Low to moderate

Validation variables

10.65 10.18 9.92 10.34 9.18

J Indian Soc Remote Sens

each of the identified zones. The study thus provides a sound basis for the planning of sustainable groundwater resources management in the region. Acknowledgments Authors would like to acknowledge the guidance of Dr I P Abrol, Director, Centre for Advancement of Sustainable Agriculture, New Delhi, India for his support and suggestions in this research. Authors are also thankful to HARSAC, Department of Agriculture, Department of Economics and Statistics, Mewat, CGWB office, Sehgal foundation, KVK Mewat, and soil testing lab, Karnal, Haryana for sharing data and useful information. We are also grateful to the farming community of Mewat district for their kind cooperation and active participation in the field data collection.

References Adhikari, K., Chakraborty, B., & Gangopadhyay, A. (2012). Assessment of irrigation potential of groundwater using water quality index tool. Environmental Research Journal, 6(3), 197–205. Aller, L., Lehr, J. H., Petty, R., & Bennett, T. (1987). Drastic: a standhrdized system to evaluate ground water pollution potential using hydrugedlugic settings. Arya, V., Singh, S., Kumar, A., Rao, T., Chaudhary, B., Rao, G., Saroha. G. P., Sharma. M. P., Singh. A., Lal. N., Kumar. U, (1999). Mapping of soil and water resources of Mewat area: Problems and their management using remote sensing techniques. Hisar: Haryana State Remote Sensing Application Centre, Haryana Agriculture University. CGWB (2014). Dynamic ground water resources of India, (as on 31st March 2011). Central Ground Water Board Ministry of Water Resources, River Development & Ganga Rejuvenation Government of India. Chakraborti, D., Mukherjee, S. C., Pati, S., Sengupta, M. K., Rahman, M. M., Chowdhury, U. K., et al. (2003). Arsenic groundwater contamination in Middle Ganga Plain, Bihar, India: a future danger? Environmental Health Perspectives, 111(9), 1194. Dar, I. A., Sankar, K., & Dar, M. A. (2010). Remote sensing technology and geographic information system modeling: an integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin. Journal of Hydrology, 394(3), 285–295. Jin, S., & Feng, G. (2013). Large-scale variations of global groundwater from satellite gravimetry and hydrological models, 2002–2012. Global and Planetary Change, 106, 20–30. Kaur, R., Minhas, P., Jain, P., Singh, P., & Dubey, D. (2009). Geo-spatial analysis of land–water resource degradation in two economically contrasting agricultural regions adjoining national capital territory (Delhi). Environmental Monitoring and Assessment, 154(1–4), 65–83. Khan, S. (2007). Groundwater information booklet: Mewat District. Haryana: Government of India, Indian Ministry of Water Resources Central Groundwater Board, cgwb. gov. in/District _Profile/Haryana/Mewat. pdf. Mall, R. K., Singh, R., Gupta, A., Srinivasan, G., & Rathore, L. S. (2006). Impact of climate change on Indian agriculture: a review. Climatic Change, 78(2–4), 445–478. doi:10.1007/s10584-005-9042-x. Mallick, J., Singh, C. K., Al‐Wadi, H., Ahmed, M., Rahman, A., Shashtri, S., & Mukherjee, S. (2015). Geospatial and geostatistical approach for groundwater potential zone delineation. Hydrological Processes, 29(3), 395–418. Misra, A. K., & Mishra, A. (2007). Study of quaternary aquifers in Ganga Plain, India: focus on groundwater salinity, fluoride and fluorosis. Journal of Hazardous Materials, 144(1), 438–448.

Mukherjee, P., Singh, C. K., & Mukherjee, S. (2012). Delineation of groundwater potential zones in arid region of India—a remote sensing and GIS approach. Water Resources Management, 26(9), 2643– 2672. Murty, M., & Kumar, S. (2011). Water pollution in India: an economic appraisal. India infrastructure report (285–298). Nag, S., & Ghosh, P. (2013). Variation in groundwater levels and water quality in Chhatna Block, Bankura district, West Bengal—a GIS approach. Journal of the Geological Society of India, 81(2), 261–280. Piscopo, G. (2001). Groundwater vulnerability map explanatory notes— Castlereagh Catchment. Parramatta: Australia NSW Department of Land and Water Conservation. Radhakrishna, B. (2009). Some thoughts about Norman Borlaug and the green revolution. Geological Society of India, 74(6), 665–668. Rahman, A. (2008). A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Applied Geography, 28(1), 32–53. Reza, R., & Singh, G. (2010). Assessment of ground water quality status by using water quality index method in Orissa, India. World Applied Sciences Journal, 9(12), 1392–1397. Rodell, M., Velicogna, I., & Famiglietti, J. S. (2009). Satellite-based estimates of groundwater depletion in India. Nature, 460(7258), 999–1002. Shah, T. (2010). Taming the anarchy: Groundwater governance in South Asia. Routledge. Singh, D., Chauhan, D., Khan, M., Sharma, R., Singh, N., Malik, P., et al. (2007). Strategic research & extension plan of District-Mewat Haryana. Haryana: Agricultural Technology Management Agency. Singh, C. K., Shashtri, S., Mukherjee, S., Kumari, R., Avatar, R., Singh, A., & Singh, R. P. (2011a). Application of GWQI to assess effect of land use change on groundwater quality in lower Shiwaliks of Punjab: remote sensing and GIS based approach. Water Resources Management, 25(7), 1881–1898. Singh, C. K., Rina, K., Singh, R., Shashtri, S., Kamal, V., & Mukherjee, S. (2011b). Geochemical modeling of high fluoride concentration in groundwater of Pokhran area of Rajasthan, India. Bulletin of Environmental Contamination and Toxicology, 86(2), 152–158. Singh, C. K., Shashtri, S., Singh, A., & Mukherjee, S. (2011c). Quantitative modeling of groundwater in Satluj River basin of Rupnagar district of Punjab using remote sensing and geographic information system. Environmental Earth Sciences, 62(4), 871–881. Singh, C. K., Kumari, R., Singh, N., Mallick, J., & Mukherjee, S. (2012). Fluoride enrichment in aquifers of the Thar Desert: controlling factors and its geochemical modelling. Hydrological Processes, 27(17), 2462–2474. Stigter, T., Ribeiro, L., & Dill, A. C. (2006). Application of a groundwater quality index as an assessment and communication tool in agroenvironmental policies–two Portuguese case studies. Journal of Hydrology, 327(3), 578–591. Surinaidu, L., Bacon, C., & Pavelic, P. (2013). Agricultural groundwater management in the Upper Bhima Basin, India: current status and future scenarios. Hydrology and Earth System Sciences, 17(2), 507– 517. Thomas, N., Sheler, R., Reith, B., Plenner, S., Sharma, L. M., Saiphy, S., Basu, N., Muste, M. M. (2012). Rapid assessment of the fresh-saline groundwater interaction in the Semi-arid Mewat District (India). World Bank. (2011). India groundwater governance. Washington: World Bank. Wu, W., Yin, S., Liu, H., & Chen, H. (2014). Groundwater vulnerability assessment and feasibility mapping under reclaimed water irrigation by a modified DRASTIC model. Water Resources Management, 28(5), 1219–1234.