Geospatial and geostatistical approach for groundwater potential zone delineation

Abstract

AbstractOver the past few decades, groundwater has become an essential commodity owing to increased demand as a result of growing population, industrialization, urbanization and so on. The water supply situation is expected to become more severe in the future because of continued unsustainable water use and projected change in hydrometeorological parameters due to climate change. This study is based on the integrated approach of remote sensing, geographical information system and multicriteria decision‐making techniques to determine the most important contributing factors that affect the groundwater resources and to delineate the groundwater potential zones. Ten thematic layers, namely, geomorphology, geology, soil, topographic elevation (digital elevation model), land use/land cover, drainage density, lineament density, proximity of surface water bodies, surface temperature and post‐monsoon groundwater depth, were considered for the present study. These thematic layers were selected for groundwater prospecting based on the literature; discussion with the experts of the Central Ground Water Board, Government of India; field observations; geophysical investigation; and multivariate techniques. The thematic layers and their features were assigned suitable weights on Saaty's scale according to their relative significance for groundwater occurrence. The assigned weights of the layers and their features were normalized by using the analytic hierarchy process and eigenvector method. Finally, the selected thematic maps were integrated using a weighted linear combination method to create the final groundwater potential zone map. The final output map shows different zones of groundwater potential, namely, very good (16%), good (35%), moderate (28%) low (17%) and very low (2.1%). The groundwater potential zone map was finally validated using the discharge and groundwater depth data from 28 and 98 pumping wells, respectively, which showed good correlation. Copyright © 2014 John Wiley & Sons, Ltd.