Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique

In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these flood drivers are statistically dependent, their joint probability might be misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for so-called compound flooding. We present a globally applicable framework for compound flood risk assessments using combined hydrodynamic, impact, and statistical modeling and apply it to a case study in the Sofala province of Mozambique. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of and dependence between all annual maximum flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. Our case study results show that from all drivers, coastal flooding causes the largest risk in the region despite a more widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observed statistical dependence compared to independence, e.g., 12 % for the 100-year return period. However, the total compound flood risk in terms of expected annual damage is only 0.55 % larger. This is explained by the fact that for frequent events, which contribute most to the risk, limited physical interaction between flood drivers is simulated. We also assess the effectiveness of three measures in terms of risk reduction. For our case, zoning based on the 2-year return period flood plain is as effective as levees with a 10-year return period protection level, while dry proofing up to 1 m does not reach the same effectiveness. As the framework is based on global datasets and is largely automated, it can easily be repeated for other regions for first-order assessments of compound flood risk. While the quality of the assessment will depend on the accuracy of the global models and data, it can readily include higher-quality (local) datasets where available to further improve the assessment.