Integrating capacity deficit into open data flood risk screening in a data-scarce informal settlement in Yaoundé, Cameroon

International Journal of Disaster Risk Reduction, vol. 143, pp. 106336

Abstract

ABSTRACT: Integrated flood risk assessment in informal settlements is hindered by scarce hydrometric, exposure, and socioeconomic data, limiting the ability to identify and target the highest-risk locations for disaster risk reduction. We present an open-data, proxy-based framework for the Ngousso quarter (Yaoundé, Cameroon) that maps hazard, exposure, household vulnerability, and collective capacity on a 100 m grid, and explicitly represents limited collective capacity as risk-increasing capacity deficit (1 − C). Hazard is represented by a digital elevation model (DEM) derived Flood Susceptibility Index (FSI) and evaluated against an independent two-dimensional (2D) hydraulic simulation. The FSI correlates with benchmark hazard (Pearson r = 0.65; R 2 = 0.42) and identifies 86% of benchmark High and Very High hazard cells, supporting conservative screening. Exposure combines population estimates from typology-classified residential buildings in OpenStreetMap (OSM), interpreted from drone orthophotos, with mapped points of interest representing economic activity and critical services. Vulnerability combines typology-based building fragility via stage damage relationships with socioeconomic susceptibility transferred from national survey profiles. Capacity is proxied by indicators of preparedness, response (including accessibility), and recovery. Low-standard informal buildings are about 23 times more likely than high-standard formal buildings to lie in High or Very High hazard zones. Adding (1 − C) increases continuous risk in 88% of grid cells and raises 28% of cells to a higher risk class. The framework enables rapid, interpretable risk screening in data-scarce urban neighbourhoods with mixed formal and informal housing and indicates whether hotspots are driven mainly by hazard, exposure, household vulnerability, or collective capacity.

Authors 5

1. Philip Meier corresponding Aachen Institute of Hydraulic Engineering and Water Resources Management

   RWTH Aachen University

   Affiliation as printed

   Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Mies-van-der-Rohe-Straße 17, 52074 Aachen, Germany

2. S. Harish Aachen Institute of Hydraulic Engineering and Water Resources Management

   RWTH Aachen University

   Affiliation as printed

   Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Mies-van-der-Rohe-Straße 17, 52074 Aachen, Germany

3. Matthias Hirt Aachen Research Institute for Water Management and Climate Future

   RWTH Aachen University

   Affiliation as printed

   Research Institute for Water Management and Climate Future at RWTH Aachen e.V. (FIW), An der Ölmühle 4, 52074 Aachen, Germany

4. Manuel Krauß Aachen Research Institute for Water Management and Climate Future

   RWTH Aachen University

   Affiliation as printed

   Research Institute for Water Management and Climate Future at RWTH Aachen e.V. (FIW), An der Ölmühle 4, 52074 Aachen, Germany

5. Elena‐Maria Klopries Aachen Institute of Hydraulic Engineering and Water Resources Management

   RWTH Aachen University

   Affiliation as printed

   Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Mies-van-der-Rohe-Straße 17, 52074 Aachen, Germany