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Flood Risk Mapping Nyalenda Slum, Kisumu

πŸ“ Nyalenda, KisumuπŸ“… 2024βœ“ Academic Project
QGISDEM AnalysisFlood ModellingRemote Sensing

Overview

Nyalenda is one of Kisumu's largest informal settlements, situated on low-lying ground adjacent to Lake Victoria. This project used SRTM Digital Elevation Model data and Sentinel-2 satellite imagery to model flood inundation zones and assess vulnerability.

Flow accumulation and depression-filling algorithms in QGIS were used to delineate catchment areas and identify surface water pathways during heavy rainfall events. Historical flood extents were validated against OpenStreetMap flood polygon data and local community reports.

A composite vulnerability index was computed by overlaying flood depth estimates with building density, road accessibility, and proximity to drainage infrastructure.

β—† Key highlights

  • β†’Identified 4 high-risk flood zones covering ~12 hectares
  • β†’Produced inundation maps for 10-year and 25-year return periods
  • β†’Mapped 800+ structures in high-risk zones
  • β†’Recommended 2 priority drainage improvement corridors

Challenges

SRTM resolution (30m) was insufficient for fine-scale urban flood modelling; interpolation techniques were applied to improve accuracy in dense settlement areas.

Outcome & results

Maps presented as part of a coursework module and scored among the top submissions. Work referenced in a peer student publication.

Tools used
QGIS 3.xSAGA GISERDAS ImagineGoogle Earth EnginePython (Rasterio)
Project details
LocationNyalenda, Kisumu
Year2024
CategoryDisaster
OutcomeAcademic Project
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