Probabilistic Flood Risk Mapping for National Highway Corridors in South Sudan: A Stochastic Hydrological Modelling and Bayesian Network Approach

Flooding constitutes the most pervasive natural hazard threatening South Sudan's national road network, causing annual disruptions estimated at USD 27.8–45.8 million across five priority highway corridors. Despite this, no probabilistic flood risk mapping framework calibrated to South Sudan hydrological conditions has been documented in the peer-reviewed literature. This study develops and validates a probabilistic flood risk mapping methodology integrating stochastic hydrological modelling, two-dimensional hydraulic simulation, Bayesian network failure probability analysis, and multi-criteria risk indexing for five national highway corridors totalling 510 km. Daily discharge records from seven long-term gauging stations were fitted to Gumbel Extreme Value Type I (EV-I) distributions, and Monte Carlo uncertainty propagation (n = 10,000) was applied to quantify model parameter uncertainty. HEC-RAS 2D hydraulic simulations were executed for return periods of 2, 5, 10, 25, 50, 100, and 200 years to generate inundation extent and depth grids at 12-metre resolution using TanDEM-X terrain data. A Bayesian network with seven nodes was developed to model the conditional failure probabilities of road segments as a function of flood inundation depth, embankment height, drainage capacity, and structural vulnerability. Validation against satellite-derived flood extents from Sentinel-1 SAR imagery yielded overall accuracies of 82–91% with kappa coefficients of 0.70–0.81. The Malakal–Renk and Bor–Pibor corridors are classified as Extreme risk, with 66% and 54% of road segments at risk under the 100-year return period event respectively. First-Order Reliability Method (FORM) analysis demonstrates that embankment heights of 2.4–3.2 m above mean annual flood level are required to achiev