2026-09-03 –, Conference Management Room3
This study develops a GIS-based flood susceptibility model for Nepal’s Sunkoshi Basin integrating OpenStreetMap-derived hydrography within an MCDA framework. AHP-derived weights and sensitivity analysis evaluate model robustness. Results demonstrate the reliability of OSM data for hydrological hazard assessment in data-scarce mountainous environments.
This study develops a GIS-based spatial flood susceptibility model for the Sunkoshi River Basin, central Nepal, explicitly integrating OpenStreetMap (OSM) hydrographic data within a Multi-Criteria Decision Analysis (MCDA) framework. The objective is to evaluate the applicability and reliability of community-generated OSM river network data in hydrological hazard modeling in complex mountainous terrain.
OSM river and stream geometries were extracted and preprocessed to generate two hydrologically significant spatial indicators: (1) drainage density and (2) Euclidean distance from river. Drainage density was computed by normalizing total stream length within defined spatial units, while distance-from-river rasters were derived to quantify flood exposure gradients relative to mapped channels. These OSM-derived layers were integrated with topographic (SRTM DEM), morphometric (slope, aspect, TWI), climatic (PERSIANN-CCS rainfall), vegetation (NDVI), land use/land cover (ICIMOD), and soil datasets.
A total of nine flood-conditioning factors were standardized and reclassified prior to weighting. Criteria weights were determined using the Analytic Hierarchy Process (AHP), employing a structured pairwise comparison matrix and consistency ratio validation to ensure logical coherence in weight assignment. Weighted linear combination (WLC) was then applied to produce a continuous flood susceptibility index, subsequently classified into four discrete risk categories.
To assess model robustness and the relative influence of OSM-derived hydrographic parameters, sensitivity analysis was conducted under multiple alternative weighting scenarios. Comparative analysis of susceptibility outputs quantified the stability of high-risk delineation zones under perturbations in drainage density and distance-from-river weights.
The results demonstrate that OSM hydrographic data can effectively support derivation of hydrologically meaningful spatial predictors for flood susceptibility modeling. In data-scarce regions where authoritative hydrographic datasets are limited or outdated, OSM provides a viable open-data alternative for spatial hazard assessment. This study contributes methodological evidence supporting the integration of volunteered geographic information (VGI) into structured, decision-analytic flood risk modeling frameworks
The primary open-source project essential to this work is OpenStreetMap (OSM), which provided the river and stream network data used to derive drainage density and distance-from-river layers for flood susceptibility modeling. These OSM-derived hydrographic datasets were fundamental to the spatial analysis and sensitivity evaluation presented in this study
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Prativa Thapa, a final-year Geomatics Engineering student at Kathmandu University, is the President of KU YouthMappers and the Sole Lead Mapping Week under the Geomatics Engineering Society (GES), recognized by Annapurna Express newspaper for her impactful leadership. She also serves as the Country Lead of Women Mapping Asia representing Nepal and is an active member of OSGeo Nepal. Through these roles, she stands as a powerful example of women excelling in the geospatial field, with a deep commitment to using open-source tools in her daily work to promote inclusive, community-driven innovation.