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<p>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.</p>

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Geospatial modelling of hydrological drought in Bhima watershed, Maharashtra, using Google Earth engine (GEE)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Arkadeep Dutta,
  • Samrat Kamakar,
  • Arup Das

摘要

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.