Flooding is the most pervasive hydro-meteorological hazard in South Asia, with climate change amplifying both frequency and severity. This study integrates Participatory Geographic Information Systems (PGIS) with remote-sensing and geospatial datasets to assess village-scale flood risk in two highly affected blocks, Nagrakata and Dhupguri, within Jalpaiguri District, West Bengal, India. Using community-derived data from 48 households across 24 villages after the October 2025 floods, the research combines lived experiences with modelled flood hazard and exposure data. The PGIS survey revealed that 100% of households experienced post-flood challenges, averaging 5.2 per household, with unsafe drinking water, sanitation failure, and housing damage as dominant issues. Statistical hotspot analysis (Getis-Ord Gi*) identified a high-burden cluster along the Jaldhaka–Diana river corridor, while bivariate mapping demonstrated that high impact was not limited to river-proximate villages but also occurred where poor drainage and inadequate protection intensified vulnerability. Integrating PGIS indicators with population density and modelled hazard produced an Exposure-Adjusted Priority Index, delineating five priority classes and highlighting critical zones for intervention. The results show that participatory GIS provides a robust complement to model-based flood assessments, revealing micro-scale heterogeneity, social vulnerability, and infrastructural gaps invisible in remote sensing data. The study underscores PGIS as an analytical bridge between top-down flood models and community realities, supporting evidence-based local resilience planning and more inclusive flood governance in the Himalayan piedmont region.
Mandal et al. (Wed,) studied this question.