Effectiveness of basin morphometry, remote sensing, and applied geosciences on groundwater recharge potential mapping: a comparative study within a small watershed

Abstract

A multidisciplinary approach using the integrated field of geosciences (e.g., geomorphology, geotectonics, geophysics, and hydrology) is established to conduct groundwater recharge potential mapping of the Kunur River Basin, India. The relative mean error (RME) calculation of the results of three applied techniques and water table data from twenty-four observation wells in the basin over the 2000-2010 period are presented. Nine sub-basins were identified and ranked for the RME calculation, where the observation wells-based ranking was taken as standard order for comparison. A linear model has been developed using six factors (drainage density, surface slope, ruggedness index, lineament density, Bouguer gravity anomaly, and potential maximum water retention capacity) and a grid-wise weighted index. In a separate comparative approach, the sub-basin and grid-wise analyses have been conducted to identify the suitable spatial unit for watershed level hydrological modeling.

Keywords

groundwater / basin morphometry / SCS curve number / geosciences / geoinformatics

Introduction

Groundwater being an important resource, an understanding of the hydro-geological processes and identification of the potential areas for its withdrawal and recharge is important ( Samadder et al., 2011). Long-term over-exploitation of groundwater has created many challenges to effective water management in India ( Shankar et al., 2011). As compared to the rest of the world, a large quantity of groundwater is continuously used for agricultural purposes in India, reaching about 61% during 2000–2001 to 2006–2007 ( Shah, 2009, 2011). Irrigation using groundwater resources has increased at a very rapid rate since the 1970s as is evident from the growing number of groundwater irrigation facilities (wells, tube wells, etc.) in the country ( Minor Irrigation Census, 2001). According to Kumar and Raj (2013), the per capita availability of water in India has declined from 3,000 m³/year to 1,800 m³/year from 1951 to 2010. The share of surface water has also declined from 60% in the year of 1950 to 30% on the first decade of the 21^st century respectively (CGWB, 2010).

However, suitable methods for mapping potential groundwater recharge areas is still problematic. The purpose of the current investigation is to determine, test, and demonstrate the methods for a successful and efficient mapping of the groundwater recharge potential over a small drainage basin. The present study deals with basin morphometry based on topographical sheets (1: 50,000), remote sensing techniques, SCS Curve Number method, and the integrated field of geosciences to consider suitable method towards mapping the Kunur River Basin (KRB). The study has also evaluated the effectiveness of sub-basin scale and grid based mapping in groundwater research.

Basin morphometry and groundwater

Basin morphometry is one of the key elements to study the surface and sub-surface hydrological conditions in any drainage basin ( Obi Reddy et al., 2004; Subba Rao, 2009; Martins and Gadiga, 2015). Prioritization techniques have been widely used to prepare the sub-basin scale groundwater potentiality map in India ( Krishnamurthy et al., 1996; Biswas et al., 1999; Khan et al., 2001; Suresh et al., 2004; Nooka Ratnam et al., 2005; Thakkar and Dhiman, 2007; Srinivasa et al., 2008; Javed et al., 2009; Avinash et al., 2011; Banerjee et al., 2011; Avinash et al., 2014) and throughout the world ( Esper Angillieri, 2008; Elmahdy and Mohamed, 2015). Quantitative assessment of basin morphometric parameters (linear, areal, and relief) help reveal information about lithology, hydrological regime, and operating exogenic/endogenic processes within the basin, which are very useful to correlate with groundwater development ( Avinash et al., 2011; Magesh et al., 2013; Saravanan and Manjula, 2015; Zhang et al., 2015).

According to Strahler (1964), a lower number of streams in a sub-basin indicates the maturity of that topography and a higher number of streams (first and second orders) indicates the sub-basin is prone to erosion. A bifurcation ratio (R_b) within the range of three to five indicates no influence of geological structure over the basin ( Strahler, 1964). According to Pakhmode et al. (2003), a watershed having a lower R_b value indicates uniform underlying material and systematic stream networking. Permeability of the underlying rock controls the length and number of streams within the basin; a lower number of relatively longer streams are observed over permeable rock strata and vice-versa ( Pakhmode et al., 2003; Rajwant and Sharma, 2015; Saravanan and Manjula, 2015).

The shape of a basin plays an important role in defining the hydrologic condition of a watershed ( Horton, 1932; Hajam et al., 2013). A high value of the elongation ratio (E_R) indicates active denudational processes associated with high infiltration capacity and low surface runoff over the basin, whereas a low E_R value indicates a higher elevation of the basin and increased susceptibility to high head-ward and tectonic lineament erosion ( Obi Reddy et al., 2004; Manu and Anirudhan, 2008; Rajwant and Sharma, 2015). According to Schumm (1956), an E_R value close to 1.0 indicates a region of low relief; values ranging from 0.6 to 0.8 are generally associated with regions of high relief and steep ground slopes. Hence, the E_R value of KRB indicates the study area is one of high relief and steep slopes.

The circularity ratio (C_r) is more influenced by stream length, stream frequency (S_f), and the gradients of streams of different orders rather than the slope conditions and drainage pattern of a basin (Strahler, 1964). Low, medium, and high values of C_r define young, mature, and old stages of tributaries in a basin respectively. If the C_r value is 1.0 then the basin will be a perfect circle in shape and discharge quantity will be high ( Miller, 1953). The compactness coefficient (C_c) is defined as a ratio between the length of the drainage basin boundary (the perimeter) and the perimeter of a circle having the same area, where the resultant value 1.0 indicates a perfectly circlular basin ( Gravelius, 1914; Hidore, 1964; Reddy, 2005).

For drainage density (D_d), Horton (1945) stated that lower values of D_d indicate permeable strata under dense vegetation and low relief, whereas higher values of D_d indicate impermeable rocks under sparse vegetation and the areas with mountainous relief. The high value of stream frequency indicates greater surface runoff and a steep ground surface ( Horton, 1932, 1945). The interaction between basin hydrology, vegetation cover, soil texture and structure, and channel spacing statistically are determined by the drainage texture (D_t) ( Horton, 1945; Smith, 1950; Schumm, 1956). Smith (1950) had classified D_t as coarse (<4 per km), intermediate (4–10 per km), fine (10–15 per km) and ultra-fine (>15 per km). Here, the KRB deals with the coarse texture surface due to the lower value of D_t (<1.0).

According to Schumm (1956), C_m, the constant of channel maintenance, is the inverse characteristic of drainage density (D_d), which depends on rock type, permeability, climatic regime, vegetation cover, and relief, and also the duration of erosion. The length of overland flow (L_o) is used to describe the length of surface water flow before its merging in the nearest stream channels ( Horton, 1945). According to Horton (1945), L_o plays a crucial role in controlling the hydrological and physiographical developments of drainage basins. Several other relief aspects, e.g., basin relief (B_r), relief ratio (R_r), ruggedness index (RI), basin average slope (S), and stream gradient (S_g), also control the hydrological characteristics of any basin ( Schumm, 1956; Strahler, 1957; Hadley and Schumm, 1961; Sreedevi et al., 2005; Avinash et al., 2014; Saravanan and Manjula, 2015).

Remote sensing and groundwater

The operational uses of satellite data in water resource monitoring were developed in the early 1980s ( Ramamoorthi, 1983). Since then , the optical remote sensing and radar imageries have not been able to penetrate beyond the uppermost surface layer (~1 m) ( Schultz, 1997; Wagner et al., 2007). The Australian Water Resources Assessment (AWRA), the Center for Remote Sensing and Mapping Science (CRMS) at the University of Georgia (UGA), the Indian Space Research Organization (ISRO), the Center for Space Research (CSR) at University of Texas, and the Groupe de Recherche de Géodésie Spatiale (GRGS) are all continuously working to improve the accuracy and spatial detail of hydrological model estimates and to validate the new updated technique in groundwater storage (GWS) change monitoring.

Recently, the Gravity Recovery and Climate Experiment (GRACE), is being used to collect daily and monthly variations of sub-surface water storage by measuring the anomaly in earth’s gravity with a combination of land surface models (e.g., multilayer perceptron (MLP)) and artificial neural network (ANN) models ( Longuevergne et al., 2010; Long et al., 2013, 2014). Using GRACE data, water science researchers can eliminate questions about the accuracy of traditional monitoring techniques, those that depend on site measurements or model simulations, and are typically costly and time-consuming ( Jiang et al., 2014). Wahr et al. (2004) reported that GRACE data are able to detect the changes of total water storage (TWS) within the accuracy of 1.5 cm on a wide range of spatial scales (minimum footprint>200,000 km²) and seasonal time scales.

Since the beginning of twenty-first century, GRACE has been an excellent option for hydrological research, but its limitation is that it is suitable for continental-scale study only (>200,000 km²) not at smaller, regional scales. In this study, multispectral optical imageries are used for micro level hydrological modeling in the Kunur River Basin. According to Meijerink (1996), the synergism of satellite imagery and airborne geophysical data can be a valuable asset in the early stages of groundwater exploration and in groundwater modelling. The imaging sensors can sometimes detect subsurface features such as buried channels and dry vadose zones without vegetation ( McCauley et al., 1982; Koopmans, 1983; Drury, 1993; Prakash and Mishra, 1993; Roy and Ray, 1993; Chaudhary et al., 1996; Meijerink, 1996; Ravindran and Jeyram, 1997; Godebo, 2005).

During the last two decades, many works have been published on the potential application of remote sensing to hydrological data extraction ( Rango and Ritchie, 1996; Guo et al., 2015; Menenti et al., 2015). Thermal bands, multispectral imageries, and synthetic aperture radar (SAR) data have been used to collect information regarding groundwater potential and deficit regions along with the flow systems of groundwater ( Meijerink, 1996; Shi et al., 1997; Elbeih, 2015). Ringrose et al. (1998) studied the possible association of near-surface groundwater and vegetation characteristics using a combination of remote sensing data and GIS techniques. De Alwis et al. (2007) studied the application of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) techniques to delineate saturated surface areas and hydrological active areas (HAA) for water resource planning. Van Dijk and Renzullo (2011) introduced spatial water resource monitoring systems (SWRMS) to provide valuable information on water management with the help of satellite observations.

Materials and methods

Selected area for case study

The Kunur River Basin (KRB) is a small watershed (~916.40 km²) within the interfluve of the Ajay and Damodar Rivers (Fig. 1). It lies in the northeastern part of the Barddhaman District in West Bengal, India. The Kunur River is a major right bank tributary of the Ajay River. KRB covers almost 33% of the lower Ajay river basin. The river originates in the western upland of the Barddhaman District at an altitude of 125 m. It runs for a distance of 114 km towards the east. The elevation of the catchment basin ranges from 131 m on the sandstone plateau to 20 m at the confluence point with the Ajay River ( Roy and Mistri, 2013). The forest cover of this basin is almost 31.35% of the area; water covers around 10.35%, 13.82% is for human settlement, 41.74% for agricultural land, and 2.73% of the area comes under the category of barren land or unsuitable areas for agriculture (based on Landsat 8 Image, dated on 26th April, 2013; processing by Q-GIS v.2.4, at 97.56% of accuracy). From the spatial science perspective, this basin is tropical; the Tropic of Cancer (23°30′ E) passes through the KRB from West to East.

From 1901 to 2003, the average annual rainfall in the region was 1,380 mm with a mean annual temperature of 25.8°C ( IMD, 2014). According to the National Climate Centre (2006) of India Meteorological Department, the total amount of rainfall during the month of September has increased throughout the South Bengal region (56.50 mm at 95% significance level). Seasonal rainfall is increased during the monsoon (91 mm), post-monsoon (26 mm), and annual (95 mm) rainfall respectively, at 90% level of significance for the last 100 years (1901-2003).

The study area consists of six major aquifer systems with varying characteristic of bed thickness, groundwater depth, and water yield capacity (Table 1 and Fig. 1-inset) (CGWB, 2014). The laterite aquifer covers 58% of the basin area and extends from the middle to the top of the basin. It has a low yield capacity (20-60 m³/day). The downstream area of the basin is covered by alluvial aquifers (37.88% of the total basin area) with high yield capacities. The upper edge of the basin consistes of sandstone, shale, and fine-grained siltstone with coal seams. An eastward sub-surface flow of groundwater is present with ranging range of electric conductivity from 500 to 2000 micro-Siemens/cm. CGWB (2014) states two major administrative units, Mangalkote and Bhatar, in the downstream area of KRB are in a semi-critical zone because of the groundwater susceptibility (CGWB, 2014).

Morphometric indices

The boundary of the KRB has been delineated using Survey of India (SoI) topographical maps (73 M/6, M/7, M/10, M/11, M/14, and M/15; scale of 1:50,000, 1967 edition) and Landsat 7 ETM+ images. All topographical maps were geo-referenced into a Universal Transverse Mercator (UTM) projection, WGS 84, Zone 45 North using 16 ground control points (GCPs) distributed at all the corners for each map in<0.002 root mean square (RMS) error. The entire KRB has been split into nine major sub-basins (A to I) based on the land surface characteristics (more than 60% area under a single category), drainage pattern, and variation of relief. To identify the sub-basin-wise variation of groundwater recharge potential, eighteen morphometric indices have been calculated for all nine sub-basins using geoinformatics technology. Applied techniques for these eighteen indices have been explained in Table 2 with proper references. The role of individual morphometric indices on groundwater development has been explained in the same table with bivariate correlation, where the positive sign indicates groundwater recharge potential is increasing with higher morphometric values of a particular parameter and an inverse condition for the negative sign.

Applied remote sensing and hydrology: SCS curve number method

The hydrological behaviour of a watershed is primarily controlled by the rate of infiltration and the amount of direct runoff ( Wagener et al., 2004; Ajmal et al., 2015). To incorporate the effects of land cover types ( Falkenmark, 1994; Ringrose et al., 1998) and hydrological soil groups ( Anbazhagan et al., 2005) on the rate of infiltration and rainwater retention, the Soil Conservation Services (SCS) Curve Number (CN) method has been applied to estimate basin-wise potential maximum water retention capacity of the soil during heavy rainfall. The SCS curve number method is a simple, widely used, and efficient method for determining the approximate amount of direct runoff and water loss from a rainfall event over an area of interest, in which the effect of land use and land cover, various soil characteristics, and antecedent moisture conditions are also considered ( Chow, 1964; United States Department of Agriculture et al., 1999; Anbazhagan et al., 2005; Shi et al., 2009). According to United States Department of Agriculture et al. (1999), this technique helps to estimate the water retention capacity of soil for a particular region, which is a vital indicator for groundwater development. The SCS curve number method was originally developed by the Soil Conservation Service ( 1972) for the development of agricultural sector in the United States ( Chow, 1964). A weighted CN has been calculated to obtain the potential maximum soil retention (S) capacity with intersecting land cover types and hydrological soil groups ( Gundalia and Dholakia, 2014).

The CN values of different lands within the each sub-basin and grid have been derived from the reference table of CN calculations followed by Chow (1964). For this, basin-wise and grid-level land cover types have been extracted from direct observations using recent Google Earth images after superimposing the basin boundaries and grids over the image through Q-GIS. Once the curve numbers were identified for different land units, the weighted curve numbers were calculated for each basin and grid using Eq. (1).

(1) \mathrm{WCN=}{\displaystyle \frac{{\displaystyle \sum \left(C{N}_{\mathrm{1}}\times a_1+C{N}_{\mathrm{2}}\times a_{\mathrm{2}}+C{N}_n\times a_n\right)}}{{\displaystyle \sum a}}},

where WCN is the weighted curve number, CN₁ is the curve number for particular land unit 1, a₁ is the area for that particular land unit 1, ∑a is the sum of the total area. Next the derived WCN has been used to calculate the value of S, using Eq. (2). Eq. (3) has been used here to calculate the direct runoff from these basins and grids:

(2) S={\displaystyle \frac{\mathrm{25400}}{\mathrm{WCN}}}-\mathrm{254},

(3) Q={\displaystyle \frac{{\left(P-\mathrm{0.2}S\right)}^{\mathrm{2}}}{P+\mathrm{0.8}S}},

where Q denotes estimated direct runoff (mm), and P is maximum storm rainfall within a day (mm).

Integrated approach with geosciences and applied remote sensing

An integrated approach has been followed in the groundwater recharge potential mapping of the KRB. Geomorphology (drainage density, slope, ruggedness index), morphotectonics (lineament), geophysical parameters (Bouguer gravity anomaly), applied remote sensing, hydrological models (SCS Curve Number), and digital topography using topographic maps (1: 50,000), ASTER DEM (30 m), multi-spectral imageries (Landsat 8) and field mapping were all used. The approach deals with six most promising and widely used indices to delineate the zones of optimal groundwater recharge (Table 3).

The geomorphological features related to morphometric indices and all streams have been digitized from topographical maps at 1: 50,000 scale produced by the Survey of India (SoI) between 1967 and 1972. In addition, the online mapping system using ‘Plug-in layer tool in Q-GIS software’ with ‘Google Satellite Image’ was a useful technique for the extraction of features related to relief and linear geomorphology. ASTER DEM was used to prepare the slope map (in degrees) and the ruggedness index of the KRB using Q-GIS. A web-based mapping technique was applied through the ‘Layer from WM(T)S server’ mapping tools in Q-GIS to get the arrangement of lineaments from the recently uploaded web version (http://bhuvan5.nrsc.gov.in/bhuvan/wms) in the thematic map services of the National Remote Sensing Centre (NRSC), India. According to NRSC (2014), it is a collaborative work of GIS cell of ISRO and the Standing Committee on Geology to prepare a national level geomorphological and lineament map on 1: 50,000 scale using a three-level classification system based on the origin of landforms. To prepare the density maps of digitized streamlines and lineaments, Kernel Function in Arc GIS v.9.3 has been run with the cell size of 30 m×30 m. We have also integrated the Bouguer gravity data to emphasize underlying rock density and its impact on the groundwater recharge. The map of Bouguer gravity anomalies was provided by the National Geophysical Research Institute or NGRI (1978), Hyderabad, India, with 5 mGal contour intervals, which has been converted into raster data using Q-GIS to extract the data in our study. Gridwise potential maximum retention of rainwater has also been calculated using the SCS Curve Number Method.

The above-mentioned factors were weighted and used to calculate a recharge potential score for the groundwater recharge zone. To consolidate all the data into a single framework and also to eliminate errors from the sub-basin wise generalizations, the KRB was divided into a set of grids (2,500 m per side). A total of 176 grids and their points were arranged over the study area and were used to extract the data from all six input maps. Map-wise data were classified into four groups based on their quartile (Q_{1, 2, 3}) values, thereafter, a weighted score (W_s) was assigned to each group within the range of 1–4 based on the relation with groundwater recharge. ‘W_s 1’ represents a very low potential zone for groundwater recharge and ‘W_s 4’ indicates optimum potentiality of water infiltration, whereas, W_s 2 and W_s 3 represent low and medium potential zones respectively (Table 3).

The cumulative weighted score for each grid (1-176) is computed using a weighted linear combination method by Eq. (4).

(4) P_{\mathrm{r}(1-176)}=D{D}_{\mathrm{w}\mathrm{r}}+S{S}_{\mathrm{w}\mathrm{r}}+R{I}_{\mathrm{w}\mathrm{r}}+L{D}_{\mathrm{w}\mathrm{r}}+BG{A}_{\mathrm{w}\mathrm{r}}+S_{\mathrm{w}\mathrm{r}},

where P_{r (1-176)} is the groundwater recharge potential index or cumulative weighted score of respective grids or points; DD, SS, RI, LD, BGA, and S are the score of drainage density (km/km²), surface slope (°), ruggedness index (RI/km²), lineament density (km/km²), Bouguer gravity anomaly (mGal), and potential maximum water retention capacity (mm), respectively. The subscripts w and r refer to the weight of a theme and the reference number of individual points of the same theme, respectively. The generated vector file with points of cumulative weighted data has been interpolated using the inverse distance weighted (IDW) method ( Philip and Watson, 1982) at the second power, with a variable search radius and considering the 12 closest points to prepare the final version of groundwater recharge potential map of the KRB. The potential groundwater recharge map in the basin has been divided into five grades, namely ─ excellent, good, moderate, low, and poor.

Model calibration by relative mean error calculation (RMEC)

To assess the efficiency of the results derived from the applied models for groundwater recharge monitoring, RMEC has been used to quantify the proximity and parallel conformity between observed and simulated rankings of sub-basins (Eq. (5)). The result, which has been derived from the seasonal water table fluctuation, is used as the reference/observed ranking in the comparison.

(5) \mathrm{RME}={\displaystyle \frac{\left(B{R}_{\mathrm{o}\mathrm{b}}-B{R}_{\mathrm{c}\mathrm{a}}\right)\times \mathrm{100}}{B{R}_{\mathrm{o}\mathrm{b}}}},

where BR_ob is the sub-basin wise observed rank based on the seasonal water table fluctuation and BR_ca denotes the simulated rank of each basin based on their rank in three different techniques for groundwater recharge capability.

Results and analysis

Potentiality of groundwater recharge using basin morphometry

An individual, morphometric parameter based, sub-basin wise ranking method has been followed in respect to the role of these parameters on groundwater recharge (Tables 4 and 5). In ranking style, the 1st priority shows the most potential of groundwater storage while the last priority, i.e., 9th shows most impermeable area for groundwater recharge. With the sum of all ranks of each sub-basin for each parameter, a compound rank has been calculated, where a low value denotes the first priority and a high value indicates the least priority for groundwater recharge capability (Table 5).

Table 5 shows that the sub-basins I, D, and C, hold the first three ranks (1, 2, and 3) out of the nine sub-basins. Hydro-morphologically, these three sub basins are the most suitable for groundwater recharge. They share the characteristics of low stream frequency, drainage density, and drainage texture and high content of channel maintenance and high length of overland flow. Additionally, low basin relief low relative relief, low ruggedness index, low slope; and low stream gradient help to generate potential recharge zones within the KRB. Spatially, about 33.37% area of the total KRB has been identified as the most suitable zone for groundwater recharge. Hydro-morphologically, results for A, B, and F are due to the inverse conditions in comparison with the sub-basins I, D, and C. These sub basins are the least suitable areas for groundwater recharge. Spatially, about 24.73% area of the total KRB comes under this heading. The rest of the basin is considered as a moderate zone for groundwater development.

Potentiality of groundwater recharge using SCS curve number method

Greater contact time between rainfall and the ground surface increases the rate of infiltration and amount of groundwater ( Raghunath, 2013; Ahmad et al., 2015); these processes are also positively correlated with maximum water retention capacity ( Chow, 1964; Shi et al., 2009; Raghunath, 2013). The result derived from the SCS curve number method shows the typical variation in water retention capacity and the amount of direct runoff due to the variation of land use/land cover characteristics over the sub-basins (Table 6). Sub-basin D has the maximum retention capacity (107.41 mm) with low (78.02 mm) direct runoff capacity. The current result shows that Sub-basin D has favorable conditions for groundwater recharge. Alternatively, in Sub-basin A, the largest percentage of rainwater (67.19%) is transferred into direct runoff (107.50 mm) and so the sub-basin experiences very low (56.86 mm) water retention capacity. It indicates the existence of a confined soil layer and lack of infiltration capacity.

Potentiality of groundwater recharge from integrated geosciences

Drainage density and groundwater recharge

In the KRB, drainage density (DD) ranges from 0.08 km/km² to 1.17 km/km², with an average density of 0.48 km/km² (Fig. 2(a)). The overall value illustrates the permeable nature of the basin surface strata: a coarse-drainage density (Ramalingam and Santhakumar, 2001). The lower drainage density at the edges of the basin helps rainwater to percolate or infiltrate towards the interior of the basin, a natural hydraulic gradient ( Roy and Mistri, 2013).

Ruggedness index and groundwater recharge

Ruggedness Index (RI) is a combined result of basin relief (R) and drainage density (D_d) that indicates the structural complexity of the terrain ( Schumm, 1956). A higher value of RI indicates a zone of high topographic relief with steep, sloping ground and high drainage density (Ramalingam and Santhakumar, 2001; Avinash et al., 2014; Saravanan and Manjula, 2015). The inverse relationship of e RI with groundwater development suggests that a lower value of RI is favorable for rainwater infiltration. Over the KRB, RI values range from 2.55/km² to 16.07/km² with an average of 7.03/km² (Fig. 2(b)). Maximum RI values were calculated for the extreme northwestern and southeastern parts of the basin. Part of the middle basin had a medium to high RI value. Lower drainage density is typically associated with less rugged terrain.

Surface slope and groundwater recharge

Surface slope is the key indicator of all geomorphological studies and is controlled by climato-morphogenic processes working on areas with underlying rocks of varying resistance (Ramalingam and Santhakumar, 2001; Saravanan and Manjula, 2015). The understanding of slope properties is essential to identifying the spatial variation of surface runoff capacity and its travel time ( Sreedevi et al., 2005). Flat and gently sloping ground promotes the maximum capacity of water infiltration and groundwater development, whereas a steeply sloping ground encourages speedy runoff and no infiltration ( Subba Rao et al., 2001). In the present study area, the slope ranges from 1.22° to 7.75°, with an average 3.90° (Fig. 2(c)). A maximum portion of the land is identified as gently sloping ground and suggests good capacity for water infiltration. The basin has also steeply sloping land over the far upstream areas with additional small areas throughout the basin. The steeplysloping ground promotes immediate runoff of rainwater instead of infiltration ( Subba Rao, 2009).

Lineaments and groundwater recharge

Lineaments provide easy pathways for groundwater movement and enhance groundwater recharge potential ( Haridas et al., 1994, 1998; Sankar, 2002). The lineament density map is a measure of the quantitative length of linear features expressed in a grid. The lineament density of an area can indirectly reveal the groundwater potentiality of that area since the presence of lineaments usually denotes a permeable zone ( Gopinath and Seralathan, 2004). In the KRB, a total of 20 lineaments have been traced which vary from 2.33 km to 15.33 km in length, with an average of 6.75 km (Fig. 2(d)). There are two major trends for lineaments in the basin – SW to NE and NW to SE. There is major concentration of lineaments in the middle of the Kunur basin along with the upper catchment area. The zones of lineament intersection have high capability of groundwater recharge ( Gopinath and Seralathan, 2004), but there is no such intersection point in the KRB. In the lineament density map (Fig. 2(d)) value ranges from 0.013 km/km² to 0.250 km/km², where the average density is 0.114 km/km². Higher density areas have been observed over the middle part of the basin, the northeastern part of the downstream area, and the northwestern part of the upstream region.

Bouguer gravity anomaly and groundwater recharge

Understanding the underlying rock density is important for mapping the groundwater recharge area ( Smith et al., 2004). According to Reddy (2005), the rate of infiltration is controlled by the fundamental properties of geological materials, mainly their density. However, the rate of infiltration is also controlled by the combined influence of gravity and capillary forces: gravity helps to move the excess water by deep percolation and builds up the groundwater table ( Raghunath, 2013). Rock density plays an inverse relationship with the permeability of rock, and a positive relation with the porosity of rock (Reddy, 2005). Nevertheless, the permeability of rock positively controls the groundwater recharge capacity. The spatial variation of rock density beneath the surface has been widely estimated by gravity data ( Prasad et al., 2005). According to Smith et al. (2004), gravity has the potential to be a new source of important remote sensing data for catchment-scale hydrological modeling. However, the usefulness of this data has not yet been demonstrated properly. Among the obtained gravity data from geophysical estimations, the Bouguer gravity data is more accurate and aids in standard geological interpretation of the land ( USGS, 1997). According to Verma (1985), in an area underlain by rock with relatively high density, the Bouguer anomaly reflects a higher gravity and vice-versa. Bouguer anomalies are negative over elevated regions and show an inverse relationship with topography ( USGS, 1997). The most important unknown source of the gravitational anomaly is the effect of the irregular underground distribution of different density rocks ( USGS, 1997). According to Long and Kaufmann (2013), if the gravity anomaly is well defined, the excess or missing mass can be computed directly from the gravity data.

In the current study, the Bouguer gravity anomaly varies from -35 mGal to+15 mGal over the KRB (Fig. 2(e)). The minimum gravity value (-35 mGal) is observed over the extreme eastern part of the basin with a concentrated elliptical depression near the confluence zone with the Ajay River. Therefore, the bedrock of the downstream area may consist of young to old alluvium because of their lower density (1.50-2.0 g/cm³) ( Dobrin, 1976). Isolines of Bouguer gravity in the downstream area show a very rapid decrease of underlying rock density; within ten kilometres, gravity decreases from+10 mGal to -30 mGal. The high negative gravity values indicate the presence of low-density rock below the Ajay and Kunur Interfluve (AKI) region ( Roy and Sahu, 2015). This gravitational depression might be the result of underlying sandy or unconsolidated sediment with a high percentage of granular material (Sahu and Saha, 2014).

Underlying heavy dense rock has been perceived in the middle of the basin from the maximum gravity anomaly. The lithological condition of this tract has been revealed by the panel diagram based on 12 exploratory boreholes (Fig. 3). The depth of the bedrock increases rapidly towards the downstream direction of the Kunur River ( Roy and Sahu, 2015). The panel diagram over the palaeo-hydrological zone of the AKI region shows a large proportion of lithofacies consisting of sand and alternating thin layers of clay. ‘Tikuri’ (87.78581°E/23.54674°N) is an important borehole point in this zone for its location in the palaeochannels in the interfluve of the Ajay and Kunur Rivers; 86.50% of the 185 m of drilling at Tikuri consists of sandy material ( Niyogi, 1985).

SCS curve number method and groundwater recharge

The SCS curve number method explores the close relationship between the land cover characteristics and the water infiltration capacity of different hydrological soil groups of the KRB. Maximum soil retention capacity (>108.85 mm) has been identified over the forest-dominated areas and the lowest retention capacity (<55.76 mm) has observed over the murrum quarrying areas and expanding urban area of Durgapur Municipal Corporation (DMC) in the upper catchment (Fig. 2(f)). Recent satellite images are showing that 31.35% of the basin area is covered by dense sal (shorea robusta) forest and around 42% is under the agricultural land. Sal forests have great influence on altering hydrological behaviors of any area and induce high water retention capacity and less runoff (NIH, 1996-1997). The area with forest cover experiences higher water infiltration capacity than other land covers (Table 7). The dense sal forest cover in the middle of the basin increases the capacity of water infiltration and helps to intercept up to 25.30% of rainfall (NIH, 1996-1997).

Groundwater recharge potential zoning

The fusion derived map shows the regional variability of groundwater recharge potential over the KRB (Fig. 4). Four windows (W1–W4) have been selected for better understanding the causes of this scenario. Five potential groundwater recharge zones have been identified in the basin, where excellent zones for groundwater recharge have been found over the northeast part of the downstream area (W1) and in the northern part of upper catchment area (W2).

During the investigation of the background causes for the excellent recharge zones in the lower basin area, Landsat ETM+ images identified the presence of numerous palaeochannels in the band combination of 574 (Fig. 5(a)). This combination involves no visible light bands and provides the best atmospheric penetration capacity with better absorption rates in the water bodies. It may be used to find textural and moisture characteristics of soils. During a field survey near Guskara (87.73952°E; 23.49121°N), a suspected palaeochannel was identified over the floodplain area of the AKI region (Window 1; Fig. 5(a)-inset). Palaeochannels provide high infiltration capacity and work as a sub-surface pathway for groundwater movement in the sedimentary basin ( Mallinson et al., 2010; Kolker et al., 2013). Therefore, this region of the KRB has a high groundwater recharge potential. Due to the significant role of palaeochannels on groundwater recharge, a comprehensive study of the palaeohydrology in India has begun from a series of works on palaeo-discharge estimation ( Sridhar, 2007; Sridhar et al., 2013), palaeochannel mapping ( Zankhna and Thakkar, 2014), and reconstruction of palaeochannel morphology with palaeo-hydrological attributes ( Khan and Tewari, 2011). Samadder et al. (2011) have mapped the locations of palaeochannels over the western Ganga plains to detect potential zones for groundwater research. Another excellent zone for groundwater recharge in the KRB is in the northern part of the upper basin area (Window 2; Fig. 5(b)) due to the presence of dense sal forest cover. A positive relationship between forest cover and groundwater recharge capacity has been noticed in the basin. Some zones of moderate to good recharge capacity over middle of the basin area also exist due to the dense to open sal forest.

Due to human activities, a notable part of the basin area comes under the poor to low category of groundwater recharge zones. Two windows have been identified as the poorest areas for groundwater recharge (Window 3 and 4 in Fig. 4). Window 3 shows an extensive urban area of the Durgapur Municipal Corporation (DMC). Its increasingly paved land covers are the major cause for low infiltration capacity (Fig. 5(c)). In Window 4 (Fig. 5(d)), extensive agricultural practices have reduced the infiltration capacity (7.20 cm·hr^-1) of the soil. The higher Bouguer gravity value also indicates that underlying dense rock plays an important role in developing poor to low categories of groundwater recharge zones (Fig. 2(e)).

Areas with good to excellent zones of recharge are also calculated for each sub-basin to prepare a ranking table (Table 8). Basin I holds first position with a maximum area (48.29%) under the good to excellent zone of recharge category. Basin H ranks second with 33.77%, followed by Basins D (25.06%), E (20.62%), A (16%), F (14.07%), B (5.51%), G (4.02%), and C (3.30%).

Discussion

Groundwater level data from twenty-four wells for the Pre-monsoon and Post-monsoon time periods of the last decade (2000-2010) have been used to calculate the average seasonal fluctuation of the water table over the KRB (Fig. 6). In order to generate a water table map, the data have been interpolated using the Kriging (NOTE: may be Kreiging, not Kriging) method. According to Saraf and Choudhury (1998), seasonal fluctuation of the water table is directly related to groundwater recharge. Subtraction of the Pre-monsoon water table from Post-monsoon water table image yields a water level fluctuation scenario. Maximum fluctuation (-3.5 m to -4.0 m) has been observed over the zones of W1 and W2 and in the middle of the basin, which are identified as excellent regions for groundwater recharge in Fig. 4. Contour lines of water table depth show that the Post-monsoon depth to the groundwater table (bgl) rose between 3.5-4.0 m from the Pre-monsoon level. Monsoon-fed rainfall is the major source of recharge in this region; the maximum infiltration capacity of rainwater in these zones recharges the groundwater table in the KRB. The minimal groundwater table fluctuation (<2.0 m) has been observed over the identified poor zones for groundwater recharge, e.g., W3 and W4. The existence of extensive impervious land cover within W3 and the agricultural fields of W4 are the major causes for this situation. The visual relationship between Fig. 4 and Fig. 6 emphasizes this point on potential groundwater recharge zone identification.

In the present study, areas with groundwater fluctuation levels of more than 2.5 m bgl have been identified as suitable zones for groundwater recharge. Based on this, the areal coverage of good recharge capability zones have been calculated for each sub-basin separately and ranked (Table 8). This ranking order is the result of direct observation from the last ten years; therefore, this order has been taken as a standard level for calculating the relative mean error (RME) among the applied three techniques. Sub-basin I once again holds the first rank because the total area (100%) of this basin comes under the>2.5 m bgl fluctuation level. Respectively, the remaining sub-basins are ranked from second, Sub-basin F, (90.61% covering area>2.5m) through D (85.86%), H (68.73%), E (66.02%), G (51.63%), A (27.63%,), B (17.48%), and C (12.22%). The values from the RME calculation indicate that individual technique-based groundwater investigation is less effective than the multidisciplinary approach (Table 8). In order to most effectively conduct groundwater recharge potential mapping of the KRB, an integrated approach is more suitable than just using basin morphometry and the SCS curve number method. In this study, it has been also proved that the application of morphometric variables is more reliable than the simple remote sensing based hydrological model (SCS Curve Number). The efficiency of a geosciences base integrated study is also significant due to the higher correlation value (0.68 at 0.05% level of significance) between point wise observed groundwater table data and the cumulative recharge capability score from this technique.

Conclusions

The integrated field of geosciences confirms the better acceptability than basin morphometry and SCS curve number method for the potentiality mapping of groundwater recharge. Though, GRACE data has been proved as better alternative in recent days but this study proves surface geomorphological and geological characteristic and multispectral remote sensing data should be major ingredient in groundwater recharge area delineation for micro level study. This study also demonstrated that grid wise weighted index derived potentiality mapping is more feasible than sub-basin scale prioritization technique. The present study shows that the most effective groundwater recharge potential zones are locating downstream and within the forest belt of this basin. The role of forest cover on the development of groundwater table has also been proved. The negative impact of surface paving on the development of this valuable natural resource has been found over the DMC. The positive role of palaeochannels on groundwater recharge and development has been pointed out. Application of Bouguer gravity data and lineament density help to identify the effect of sub-surface conditions on the rate of water infiltration as well as groundwater development. Overall, the study helps to distinguish those places which should be preserved as pathways for recharging groundwater for future utilization.

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