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 flooddrivers are statistically dependent, their joint probability might bemisrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting forso-called compound flooding. We present a globally applicable framework forcompound flood risk assessments using combined hydrodynamic, impact, andstatistical modeling and apply it to a case study in the Sofala province ofMozambique. The framework broadly consists of three steps. First, a largestochastic event set is derived from reanalysis data, taking into accountco-occurrence of and dependence between all annual maximum flood drivers.Then, both flood hazard and impact are simulated for different combinationsof drivers at non-flood and flood conditions. Finally, the impact of eachstochastic event is interpolated from the simulated events to derive acomplete flood risk profile. Our case study results show that from alldrivers, coastal flooding causes the largest risk in the region despite amore widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observedstatistical dependence compared to independence, e.g., 12 % for the100-year return period. However, the total compound flood risk in terms ofexpected annual damage is only 0.55 % larger. This is explained by thefact that for frequent events, which contribute most to the risk, limitedphysical interaction between flood drivers is simulated. We also assess theeffectiveness of three measures in terms of risk reduction. For our case,zoning based on the 2-year return period flood plain is as effective aslevees with a 10-year return period protection level, while dry proofing upto 1 m does not reach the same effectiveness. As the framework is based onglobal datasets and is largely automated, it can easily be repeated forother regions for first-order assessments of compound flood risk. While thequality of the assessment will depend on the accuracy of the global modelsand data, it can readily include higher-quality (local) datasets whereavailable to further improve the assessment.