Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach

The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km2 of the study area, whereas the poor groundwater potential zone covers 180.05 km2. Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%)

Temporal Change Dynamics of the Hydrometeorological Conditions of Upper Subarnarekha River Basin (SRB) Using Geospatial Techniques

Understanding the dynamics of any river basin requires a comprehensive analysis of factors such as urbanization, socioeconomic growth, deforestation, agricultural practices, and mining activities. This study aims to investigate the climatic and land use variations and their implications on the hydrometeorological conditions of the upper Subarnarekha River Basin (SRB). Decadal Land Use and Land Cover (LULC) alterations were assessed for the years 2001, 2010, and 2020. Further, climatic variations were studied using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation data and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) temperature data (2001–2020). Conventional groundwater level data from the India-Water Resource Information System (WRIS) for the same timeframe were also integrated to explore groundwater level fluctuations. Such temporal variations were examined using Theil Sen’s Median Trend and Mann-Kendall tests. The study also determines how LULC changes and climate variability influence groundwater level in the upper SRB during pre-monsoon, monsoon, and post-monsoon seasons. Results showed higher precipitation and temperature in the southeastern basin region. A strong connection between rainfall and groundwater levels was inferred, with rainfall exhibiting a non-significant upward trend (9.83 mm/year), while temperature shows a persistently significant increasing trend. These observations emphasize the importance of monitoring the hydrometeorological behavior of the basin, underlining its critical role in ensuring the long-term sustainability of water resources

Groundwater vulnerability and contamination risk assessment using GIS-based modified DRASTIC-LU model in hard rock aquifer system in India

Groundwater vulnerability and risk assessment is an essential step for preventing and controlling contamination in any area. DRASTIC model with modification incorporating land use (LU) widely used for assessing groundwater vulnerability worldwide. The present study used this modified model termed as DRASTIC-LU considering LU as a significant parameter in addition to other conventional DRASTIC parameters. Geographic information system employed to prepare and integrate different parameter maps led to demarcate the risk zones in a catchment of Damodar River, Jharkhand, India. Final DRASTIC-LU map was categorized as low (19.50%), moderate (34.02%), high (29.90%) and very high (16.58%) risk zones of groundwater contamination. Validation of models done through Spearman’s rank correlation coefficient (ρ) and receiver operating characteristic curve using nitrate concentration in groundwater, which indicated DRASTIC-LU (ρ = 0.893; AUC = 71.65%) had a better agreement than DRASTIC model (ρ = 0.7818; AUC = 67.36%). Thus, such modification in DRASTIC improves the performance of the model