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June 20, 2026International Journal of Disaster Risk Reduction0 citationsOpen Access

Hotspots and Drivers of Flood Damage in Assam, India: A Spatio-Temporal Assessment of Flood-Induced Damage and their Driving Factors

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ABAshes BanerjeeBirla Institute of Technology, MesraSPSrinivas PasupuletiIndian Institute of Technology Dhanbad
Biswajeet Pradhan
Biswajeet PradhanUniversity of Technology Sydney

Key Points

  • This study aims to assess the multi-dimensional impacts of flood damage in Assam and identify key drivers of variability in flood damage.
  • Aggregated daily flood damage reports into a district-level Flood Damage Index using the Entropy Weight Method.
  • Evaluated damage contributions from housing, population displacement, and crop area.
  • Conducted a spatio-temporal analysis to identify critical damage periods and contributing factors.
  • Housing damage contributes approximately 15-18% to the Flood Damage Index.
  • The critical damage period is identified as mid-July to mid-August.
  • Eight districts exhibit FDI values significantly above the state average, with Barpeta leading at 264%.

Abstract

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.

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Cite This Study

Banerjee et al. (2026) studied this question.

synapsesocial.com/papers/6a362f92db0793dc1a53705dhttps://doi.org/10.1016/j.ijdrr.2026.106266
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Flood Governance in the Flood-Prone Districts of UpperAssam, India: An Analysis of Flood Management Policies2025
  2. 2Flood Disaster Management in Assam: Socio-economic Vulnerability and Preventive Measures2025
  3. 3Assessment of flood disaster and management strategies in the lower Brahmaputra valley of Assam2024 · 2 citations
  4. 4Flood damages in India – causes, data analysis, and way forward2026
  5. 5Resilience and Response: Understanding Community’s Policy Perspectives on Flood and Erosion in Assam2026