Characterization of groundwater variability using hydrological, geological, and climatic factors in data-scarce tropical savanna region of India

Study Region: State of Odisha, a data-scarce tropical savanna region in eastern India. Study Focus: This study evaluated the temporal variability in depth to groundwater (DTW) in the study region with heavily stressed aquifers during 1995-2015 using the modified Mann Kendall test. Subsequently, Shannon's entropy assessed spatial variability in DTW and determined the dominant Hydrological, Geological, and Climatological (HGC) factor regulating the observed spatio-temporal variability taking land use/ land cover (LULC), geomorphology, lithology, topography, and rainfall as HGC factors. New Hydrological Insights: The overall and seasonal trend analysis revealed that the study region possessed both rising and declining trends with a slightly higher percentage of wells with a rising trend. The spatial distribution of trends and the associated magnitude accentuated the unforeseen groundwater temporal variability and higher-order susceptibility of DTW to rising and declining trends. The marginal entropy revealed the higher-order spatial variability associated with deeper DTW and vice versa. Evaluation of the HGC factors revealed that LULC could explain the maximum variability in the DTW as a dominant HGC factor. It was found that the impact of LULC features on DTW variability is not straightforward, necessitating impact assessment studies in the location with significant to highly significant trends. This formulated approach can immensely contribute to the planning and management in attaining groundwater sustainability in datascarce regions.

Automated summary

This study evaluated the temporal variability in depth to groundwater in the State of Odisha, India from 1995 to 2015, identifying both rising and declining trends. Land use/ land cover was found to be the dominant factor influencing groundwater depth variability, highlighting the need for impact assessment studies in locations with significant trends. This approach can greatly contribute to planning and management for achieving groundwater sustainability in data-scarce regions.