Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe

Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly (p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that ~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods