Delineating Groundwater Recharge Potential through Remote Sensing and Geographical Information Systems

Abstract

Owing to the extensive global dependency on groundwater and associated increasing water demand, the global groundwater level is declining rapidly. In the case of Islamabad, Pakistan, the groundwater level has lowered five times over the past five years due to extensive pumping by various departments and residents to meet the local water requirements. To address this, water reservoirs and sources need to be delineated, and potential recharge zones are highlighted to assess the recharge potential. Therefore, the current study utilizes an integrated approach based on remote sensing (RS) and GIS using the influence factor (IF) technique to delineate potential groundwater recharge zones in Islamabad, Pakistan. Soil map of Pakistan, Landsat 8TM satellite data, digital elevation model (ASTER DEM), and local geological map were used in the study for the preparation of thematic maps of 15 key contributing factors considered in this study. To generate a combined groundwater recharge map, rate and weightage values were assigned to each factor representing their mutual influence and recharge capabilities. To analyze the final combined recharge map, five different assessment analogies were used in the study: poor, low, medium, high, and best. The final recharge potential map for Islamabad classifies 15% (136.8 km2) of the region as the “best” zone for extracting groundwater. Furthermore, high, medium, low, and poor ranks were assigned to 21%, 24%, 27%, and 13% of the region with respective areas of 191.52 km2, 218.88 km2, 246.24 km2, and 118.56 km2. Overall, this research outlines the best to least favorable zones in Islamabad regarding groundwater recharge potentials. This can help the authorities devise mitigation strategies and preserve the natural terrain in the regions with the best groundwater recharge potential. This is aligned with the aims of the interior ministry of Pakistan for constructing small reservoirs and ponds in the existing natural streams and installing recharging wells to maintain the groundwater level in cities. Other countries can expand upon and adapt this study to delineate local groundwater recharge potentials.

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

“…These twenty-seven sub-factors were designated based on the relative influence of each data layer, determined through practical experience and knowledge of factors affecting GWPZ mapping. They were then adjusted based on their proximity to specific sub-categories [ 54 ]. The five major conditioning factors chosen are hydrological criteria, morphometric criteria, permeability, terrain distribution, and anthropogenic factors (see Table 1 and Supplementary Fig.…”

“…Previous research had been limited to topography and a few conditioning parameters for quantifying GWP mapping. However, the new GEE cloud provides more parameters to aid groundwater assessment, evaluation, and conservation [ 54 ]. carried out GWP zoning using the GEE platform, integrating GIS and RS techniques by incorporating fifteen groundwater recharge monitoring parameters for Islamabad, Pakistan.…”

Remote sensing and geographic information system (GIS)-based high-resolution mapping of potential groundwater recharge zones on the hard rock terrains of the Cameroon volcanic line (CVL)

“…Groundwater is considered the main solution to the water shortage in the area, although the resource is barely exploited. The first pre-requisite for aquifer formation is precipitation percolation to recharge underground formations [1][2][3]. Therefore, exploration and sustainable management of aquifers demands a good mapping of groundwater recharge zones.…”

“…The method consists in mapping various factors that affect GWR pot and integrating them to produce a GWR pot map [e.g. 3,6,10,11,12,13,14,15,16]. The integration is done by multiplying each individual raster by their relative weights, which is a measure of their impact on the GWR pot of the area.…”

“…Fig 3. Integrating factors using superposition principle of weighted overlay analysis to produce GWR pot map of study area…”