Integrating a GIS-Based Multi-Influence Factors Model with Hydro-Geophysical Exploration for Groundwater Potential and Hydrogeological Assessment: A Case Study in the Karak Watershed, Northern Pakistan

Abstract

The optimization of groundwater conditioning factors (GCFs), the evaluation of groundwater potential (GWpot), the hydrogeological characterization of aquifer geoelectrical properties and borehole lithological information are of great significance in the complex decision-making processes of groundwater resource management (GRM). In this study, the regional GWpot of the Karak watershed in Northern Pakistan was first evaluated by means of the multi-influence factors (MIFs) model of optimized GCFs through geoprocessing tools in geographical information system (GIS). The distribution of petrophysical properties indicated by the measured resistivity fluctuations was then generated to locally verify the GWpot, and to analyze the hydrogeological and geoelectrical characteristics of aquifers. According to the weighted overlay analysis of MIFs, GWpot map was zoned into low, medium, high and very high areas, covering 9.7% (72.3 km2), 52.4% (1307.7 km2), 31.3% (913.4 km2), and 6.6% (44.8 km2) of the study area. The GWpot accuracy sequentially depends on the classification criteria, the mean rating score, and the weights assigned to GCFs. The most influential factors are geology, lineament density, and land use/land cover followed by drainage density, slope, soil type, rainfall, elevation, and groundwater level fluctuations. The receiver operating characteristic (ROC) curve, the confusion matrix, and Kappa (K) analysis show satisfactory and consistent results and expected performances (the area under the curve value 68%, confusion matrix 68%, Kappa (K) analysis 65%). The electrical resistivity tomography (ERT) and vertical electrical sounding (VES) data interpretations reveals five regional hydrological layers (i.e., coarse gravel and sand, silty sand mixed lithology, clayey sand/fine sand, fine sand/gravel, and clayey basement). The preliminary interpretation of ERT results highlights the complexity of the hydrogeological strata and reveals that GWpot is structurally and proximately constrained in the clayey sand and silicate aquifers (sandstone), which is of significance for the determination of drilling sites, expansion of drinking water supply and irrigation in the future. Moreover, quantifying the spatial distribution of aquifer hydrogeological characteristics (such as reflection coefficient, isopach, and resistivity mapping) based on Olayinka’s basic standards, indirectly and locally verify the performance of the MIF model and ultimately determine new locations for groundwater exploitation. The combined methods of regional GWpot mapping and hydrogeological characterization, through the geospatial MIFs model and aquifer geoelectrical interpretation, respectively, facilitate decision-makers for sustainable GRM not only in the Karak watershed but also in other similar areas worldwide.

Optimized Pilot Point Emplacement Based Groundwater Flow Calibration Method for Heterogeneous Small-Scale Area

“…The RMS is traditionally the primary calibration measure for the groundwater flow model. The square root of the average square of the residual error represents the difference between the onsite observed value and the numerically simulated hydraulic head (Equation ( 2)) [39].…”

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

“…The use of remote sensing data in hydrological investigations can give information on spatial and temporal scales, which is necessary for effective analysis, prediction, and validation of hydrological models [30]. Satellite imagery's capacity to span vast spatial scales is vital for depicting physiographic and structural characteristics, which are essential requirements for delineating potential groundwater zones [30] and have been used by various researchers [35][36][37][38][39][40][41].…”

“…Integrated GIS and RS with AHP prove that it is a handy tool for delineating potential groundwater zones by reducing time and cost [4,17,32,37,41,44,45,[48][49][50]. Researchers adopt various techniques for delineation potential groundwater zones: statistical method [51], influence factor (IMF) [36,38,40,52], groundwater modelling, the combination of GIS, RS with AHP [37,41,45,49], and GIS-based machine learning [39,53,54]. Many researchers have adopted these methods since they have been demonstrated to be reliable and effective.…”

“…As groundwater is dynamic, RS data integration into the GIS is convenient for identifying the potential groundwater zones (GWPZ). [41] x x x x x x x x Karimi-Rizvandi et al, [39] x x x x x x x x x x Aslan and Çelik, [17] x x x x x x x x x Jhariya et al, [40] x x x x x x x x Sarwar et al, [52] x x x x x x x Nasir et al, [36] x x x x x x x Kumar et al, [37] x x x x x x x x x x Dilekoglu and Aslan, [49] x x x x x x x x x Khan et al, [38] x x x x x x x x x Abijith et al, [44] x x x x x x x x Achu et al, [32] x x x x x x x Ajay Kumar et al, [50] x x x x x x x Ahmad et al, [47] x x x x x x x x Arefin, [31] x x x x x x x x x x Arya et al, [56] x x x x x x x x x x Bhunia, [57] x x x x x x x Qadir et al, [58] x x x x x x Lentswe and Molwalefhe, [48] x x x x x Sl-Slope, Lu-Land use, Ge-Geology, Li-Lithology, Gm-Geomorphology, Rf-Rainfall, SPi-Stream Power Index, El-Elevation, So-Soil, Ld-lineament density, Rd-River density, If-Infiltration TWi-Topographic wetness index, STi-Sediment transport index, Cu-Curvature, Dd-Drainage density, Al-Altitude, Dr-Distance from the river, Aq-Aquifer, Dg-Depth to groundwater.…”

Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan

“…Satellite imageries data provides rapid and beneficial reference information on parameters including geology (rocks), topography (earth surface), LCLU and guidelines, control generation, and flow of groundwater [27,28]. The integrated utilization of remote sensing and geographic information system (GIS) are being widely used for groundwater prospecting, as remote sensing offers good functionality for mapping the groundwater recharge area, and can differentiate and identify various structures including rocks, vegetation, and water [29]. Moreover, integration of different techniques can solve problems associated with the hidden subsurface heterogeneity and identify different structures like rocks, water, and vegetation [16,[30][31][32].…”