This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow.

Hydrological drought remains a critical challenge to water security in semi-arid basins, where fluctuating rainfall patterns, elevated evapotranspiration rates, and increasing water demand collectively intensify pressure on limited water resources. This study develops a comprehensive geospatial framework to assess drought susceptibility in the Bhima River Basin, Maharashtra, by integrating climate-based drought indices, remote sensing datasets, and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) within a Multi-Criteria Decision-Making (MCDM) approach. Using the Google Earth Engine (GEE) platform, twelve hydro-climatic and vegetation indices PDSI, SPEI, SPI, RDI, SWEI, NDMI, NDDI, CDI, CI, VCI, TCI, and VHI were computed and standardised to represent both spatial and temporal drought dynamics. Expert-based pairwise comparisons and fuzzy synthetic extent analysis were applied to derive criterion weights. The analysis showed that PDSI (0.20), SPEI (0.18), and SPI (0.15) received the highest weights, highlighting the dominant influence of precipitation variability and temperature balance in shaping hydrological drought severity. Weighted overlay analysis produced a Drought Susceptibility Index (DSI) that ranged from 0.23 (very low) to 0.81 (very high). Spatial interpretation revealed that 31.4% of the basin especially areas across Satara, Pune, Ahmednagar, Solapur, and Osmanabad falls under high to very high drought susceptibility, aligning well with historically drought-affected zones. This study is distinct in its combined use of fuzzy decision modelling and cloud-based Earth observation analytics, enabling robust drought diagnostics and reducing uncertainty associated with expert judgments. The resulting framework offers a scalable, reproducible, and decision-oriented tool for enhancing climate resilience and supporting sustainable water resource management in drought-prone regions.