Integrated Flood Risk System for the Western Cape: Lessons from the September 2023 Floods

Flooding has increasingly posed significant challenges in the Western Cape, South Africa, with the September 2023 floods in Franschhoek underscoring the vulnerability of the region to extreme rainfall events. During this event, the area received over 220 mm of rainfall within 48 hours, resulting in extensive flooding that inundated approximately 500 hectares, displaced over 1,000 residents, and caused substantial damage to infrastructure. This study developed an integrated Flood Risk Information System (FRIS) designed for flood-prone regions in the Western Cape, utilizing Earth Observation (EO) technologies, hydrological modelling, and Geographic Information Systems (GIS).

The system integrated historical flood data, municipal hydrological observations, and real-time environmental variables, including rainfall, river discharge, and soil moisture, to enhance flood risk prediction, monitoring, and response. Hydrological modelling was conducted using the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) and SWAT (Soil and Water Assessment Tool) models. Machine learning algorithms, including Random Forest (RF) and Gradient Boosting Machine (GBM), were implemented to predict flood probabilities. Model outputs were validated against observed data from the local municipality, which included flood extent maps and river discharge measurements.

The system demonstrated high accuracy in predicting flood extents, with the HEC-HMS model achieving a Nash-Sutcliffe Efficiency (NSE) of 0.88 and a Root Mean Square Error (RMSE) of 12% compared to observed discharge data. The machine learning models yielded flood prediction accuracies of 87% (RF) and 91% (GBM) when compared to observed flood extents. Google Earth Engine (GEE) was used to process large EO datasets, allowing for real-time flood mapping and risk analysis.

The FRIS proved instrumental in being able to model the September 2023 floods by providing accurate predictions and mapping, enabling disaster management agencies to target evacuation efforts and allocate resources effectively. However, further improvements are planned, including incorporating finer-resolution rainfall and topographic data, expanding the system’s spatial coverage, and integrating socio-economic indicators to assess community vulnerability better.

This study highlighted the potential of combining EO, GEE, GIS, and advanced hydrological models in improving flood risk management. The FRIS provides a powerful framework for mitigating flood impacts and protecting vulnerable communities, aligning with broader efforts to enhance climate adaptation and resilience in the Western Cape.