AI-Integrated Drought Assessment and Governance: A Scientific Evaluation of Maharashtra’s Indicator-Based Drought Declaration Framework Using Remote Sensing, GIS, And Hydro-Meteorological Analytics

Drought is among the most economically damaging and socially regressive hydro-meteorological hazards affecting semi-arid agrarian economies such as the Indian state of Maharashtra. Historically, drought declaration in India relied on the qualitative Anewari–Paisewari–Gridwari system, criticised for subjectivity, temporal lag, and weak reproducibility. Following the Manual for Drought Management (2016), the Government of Maharashtra issued a Government Resolution (GR) dated 07 October 2017 that institutionalised an indicator-based, two-trigger, three-stage scientific protocol. This paper provides an interdisciplinary, technically rigorous, and policy-oriented appraisal of the methodology, integrating climatology, agricultural meteorology, hydrology, groundwater science, geographic information systems (GIS), satellite remote sensing, and disaster governance. Each indicator is deconstructed: the Rainfall Deviation (RFdev) and dry-spell criterion forming Trigger-1; and the impact indicators forming Trigger-2, namely the NDVI deviation, NDWI deviation, Vegetation Condition Index (VCI), Moisture Adequacy Index (MAI), Groundwater Drought Index (GWDI), and Area-Under-Sowing indicator, followed by GPS-geotagged ground-truth verification. The mathematical formulation, physical meaning, sensitivity, and threshold defensibility of each indicator are examined and benchmarked against the India Meteorological Department classification, National Disaster Management Authority guidelines, the U.S. Drought Monitor, the Standardized Precipitation Index, and the European Combined Drought Indicator. While the GR represents a substantive advance over eye-estimation methods, limitations persist, including coarse monthly temporal resolution, spatial-resolution mismatch among inputs, omission of streamflow and reservoir indices, weak socio-economic vulnerability integration, and limited treatment of climate-change non-stationarity. An AI-augmented, IoT-enabled digital drought intelligence framework with a Drought Vulnerability and Resilience Index is proposed to complement the GR.