From Chaos to Preparedness: Recreating the Durban, April 2022 Floods using Enhanced Hydrological and Hydraulic Modelling for Flood Early Warning Systems.

In April 2022, the city of Durban in South Africa experienced one of its most devastating floods in history, with 300 mm or more recorded in the 24-hour period on 11-12 April. This extreme event led to approximately 430 fatalities and infrastructure damages estimated at 1 billion US dollars.

The primary objective of this study was to calibrate the hydrological model (HEC-HMS) and hydraulic model (HEC-RAS) by using observed precipitation data, high-resolution Digital Elevation Model (DEM) of 10 m, soil maps, land use and landcover (LULC)  maps, and hydraulic structures characteristics of one of the affected areas - Inanda. The methodology involved simulating flood extent, inundation levels and flow hydrographs and subsequently comparing these outputs with observed data obtained from rainfall stations, river gauges and weirs.

The simulated results indicated a discharge of 570 m³/s in the main channel of the Mngeni River. The flood peaked on April 12, 2022, between 11:00 PM and 12:00 PM, aligning closely with observed peak flow discharges recorded by local authorities. The study identified that areas around tributaries of the Mngeni River were more severely impacted than the main channel, with flood inundation depths reaching up to 5 metres. Based on aerial imagery, the Durban Harbour experienced the most extensive flooding in terms of spatial coverage, with water depths of approximately 1 metre. This flooding led to significant disruptions, including the closure of major highways and the port.  

This study successfully calibrated critical parameters required for the development of a Flood Early Warning System (FEWS), reducing the discrepancies between observed and simulated data. To further enhance the accuracy and reliability of future flood prediction models, the study recommends the creation of high-resolution soil maps and more detailed LULC maps specifically tailored for flood-prone. Such advancements could prove to be crucial for strengthening flood preparedness and mitigating risks in similarly vulnerable regions.