In February 2022, the City of Tshwane experienced devastating floods in the Centurion and Mamelodi areas, resulting not only in six fatalities but also significant loss of property and livelihoods. Subsequent to the floods in Tshwane, Ivory Park in Tembisa also experienced flooding that claimed one life, three people were reported missing, and 187 people were left homeless. The impact of these disasters had a profound effect on the local communities, and it is imperative to understand the factors contributing to these flood events so that we can be better prepared in future.
One of the contributing factors to the flooding in these areas is a massive increase in rainfall, from 50-100 mm in February 2021 to 100-200 mm in February 2022. This could potentially be attributed to climate change, but the affected areas have also seen significant urban development which results in an increase in hardening of large areas. This has significantly reduced the run-off capacity in these local catchment areas. A catchment area is described as a topographically represented area within which surface water drains to a common outlet or river. In these instances the Pienaarsrivier flows through Mamelodi, Hennopsrivier flows through Centurion, and Kaalspruit flows through Ivory Park in a northerly direction. The flood-affected areas are all located downstream of the rivers and if there is poor drainage infrastructure it could contribute to larger and more dangerous flood events.
To test the hypothesis that the developing urban landscape, with its increased sealed or hardened surfaces contributes to these events, we turned to open data and ArcGIS Pro. Leveraging the tools in Pro, we were able to processes, quantify and validate the increase in these impermeable surfaces and evaluate their impact on people and communities.
The South African National Landcover (NLC) dataset can be used to identify pervious (permeable) and impervious (impermeable) areas within these catchment areas. The 1990 NLC, together with the corresponding 2020 NLC classes (pervious, impervious, water bodies, wetlands) were analysed in all three affected areas to quantity change. The results show that there has been a significant decrease in pervious areas (grass, veld, etc. where water can be absorbed or retained) due to the large-scale development that has taken place in and around these affected areas – see the images below to visualise this change. As a result, the water does not effectively drain and absorb into the ground, resulting in a more rapid rise in water levels and which contributes to the severity of these flood events.
Simulating the events with a modelled datasets
To enable better awareness and planning for these major events, Esri South Africa has developed the Flood Risk Potential Dataset. This is a unique model that can be used to simulate the rise of water in natural drainage channels created when there is a sudden surge of water through an area. The model uses a Digital Surface Model (DSM) as the input raster and runs the Spatial Analyst Hydrology tools within ArcGIS Pro to calculate the flow direction, flow distance, and flow accumulation of the streams. This helps to determine the depth and extent of the flood in affected areas. The model was run across each of the three affected areas to evaluate the flood risk that could be attributed to them.
In the image below, dark blue shading is used to represent areas of high risk, and the light blue shading represents the areas of lower risk. The image clearly shows that the buildings in the dark blue shaded areas are at risk of being flooded when the river bursts its banks. These are the same areas that were flooded in Nellmapius, Mamelodi earl in 2022.
We can attribute these flood events both to changes in climate, and the impact of development associated with the increased population. This has resulted in more rainwater being channelled into the local river systems, in a more concentrated time, where in the past more of the water would have been absorbed into the ground.
What can you do?
By leveraging this kind of spatial modelling, various role-players such as local municipalities, provincial disaster management, agencies of CoGTA, communities and others can model risk for areas that could potentially be inundated with a potential water level rise. By integrating other datasets, such as dwellings and demographics, these agencies can quickly understand potential impact and, most importantly, can begin planning initiatives to mitigate risk, raise community awareness and plan better for disaster response should these events re-occur.
We can all see the reality around us of increased frequency and size of major flood events, but through the use of data-driven spatial analytics, we can be better placed to deal with the impact of these on people, property or infrastructure in the future.
For more information, please contact Maria.