Floods in Assam, India, regularly cause severe socio-economic losses; however, existing assessments largely rely on hazard-or exposure-based approaches and fail to capture the full multi-dimensional impacts of flood damage. As a result, the measures adopted are mostly short-term (e.g., river training and relief) and are inadequate for long-term flood risk management. This study aggregates multi-dimensional daily flood damage reports from the Assam State Disaster Management Authority (ASDMA) into a district-level Flood Damage Index (FDI) using the Entropy Weight Method (EWM) to minimize expert bias and identify the key drivers of flood damage variability. The results show that housing damage contributes approximately 15–18% of the index, followed by population displacement to relief camps (≈11%) and crop area affected (≈10%). Together, these account for about 40% of the overall flood severity. Eight districts—Barpeta (264%), Goalpara (221%), Nalbari (139%), Darrang (55%), Dhubri (54%), Morigaon (50%), Kamrup (Rural) (10%), and Nagaon (3%)—exhibit FDI values significantly above the state average. A one-month period from mid-July to mid-August is identified as the critical damage-escalation window. While rainfall is the primary driver of flooding, the severity of damage is determined by local factors, including changes in swamp area, structural failures, and community-level preparedness. These findings underscore the need to shift from a top-down, flood-control–centric approach to a more community-centered adaptation framework to reduce district-level flood risk more effectively and sustainably in Assam.
Banerjee et al. (2026) studied this question.