Geospatial modelling of hydrological drought in Bhima water, Maharashtra, using Google Earth Engine (GEE) and advanced remote sensing and GIS

Hydrological drought poses a serious threat to water security in semi-arid basins, where fluctuating rainfall, high evapotranspiration, and growing water demand intensify resource stress. This study presents an integrated geospatial framework for evaluating drought susceptibility in the Bhima River Basin, Maharashtra, using a Multi-Criteria Decision-Making (MCDM) approach that combines climate-driven drought indices, remote sensing, and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Using the platform of Google Earth Engine (GEE), twelve hydro-climatic and vegetation-based indices—PDSI, SPEI, SPI, RDI, SWEI, NDMI, NDDI, CDI, CI, VCI, TCI, and VHI—were derived and standardised to capture spatial and temporal drought variability. Pairwise comparisons were structured using expert judgment, and fuzzy synthetic extent analysis was applied to compute criterion weights. The results indicated that PDSI (0.20), SPEI (0.18), and SPI (0.15) contributed the highest weights, underscoring the dominant role of precipitation and temperature balance in determining hydrological drought severity. Weighted overlay analysis in GIS produced a Drought Susceptibility Index (DSI) ranging from 0.23 (very low) to 0.81 (very high). Spatial analysis revealed that 31.4% of the basin, particularly regions within Satara, Pune, Ahmednagar, Solapur, and Osmanabad, showed high to very high susceptibility to drought, which is consistent with past hotspots for drought. This study is unusual because it combines fuzzy decision modelling with cloud-based Earth observation analytics to improve regional drought diagnostics and handle expert judgment uncertainty. In drought-prone basins, the created framework provides a strong decision-support system for planning climate resilience and managing water resources sustainably. It also exhibits excellent adaptability and reproducibility.