Groundwater potential assessment of an alluvial aquifer in Yamuna sub-basin (Panipat region) using remote sensing and GIS techniques in conjunction with analytical hierarchy process (AHP) and catastrophe theory (CT)

•GWP was assessed using AHP and Catastrophe Theory (CT) in Panipat.•Modified data standardisation of data for some of the variables in CT.•New GW resource potential map was prepared for the validation using field data.•CT yielded better results than the AHP on Validation.•Poor GWP in CN and SE was due to human actions and in NW due to geological factors. The economic and population growth worldwide are moving once invisible resource, “groundwater” into the headlines. The importance of groundwater in India can be realised from the fact that the majority of the irrigated food production is dependent on it. In Haryana state of India, where the present study has been conducted, the groundwater draft is more (13.9 × 109 m3) against the net availability of recharge (10.3 × 109 m3). Therefore, in Panipat region of Yamuna sub basin an attempt has been made to evaluate the groundwater potential zones, so that the groundwater stress areas can be sustainably managed. The present research accentuated the expediency of geospatial technology and selection of multi-criteria decision making (MCDM) techniques to evaluate the groundwater potential of the study area. Two MCDM methods namely Analytical Hierarchy Process (AHP) and Catastrophe Theory (CT) for the weighted overlay were employed in combination with remote sensing and GIS techniques for the demarcation of groundwater potential zones of the region. These models were validated with a groundwater resource potential map i.e. validation map prepared using field data by classifying the output maps into four categories on the basis of quantiles. Both the models used for the identification of groundwater potential zones gave similar results in terms of areal extent whereas the spatial distribution of groundwater prospect of AHP and CT was significantly distinct and CT method yielded better results on validation. Further, sensitivity analysis was carried out to know the effect of weights assigned to the input indicators/variable in both the MCDM techniques.