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Abstract. 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.

Abstract. Coastal flood risk is a serious global challenge facing current and future generations. Several disaster risk reduction (DRR) measures have been posited as ways to reduce the deleterious impacts of coastal flooding. On a global scale, however, efforts to model the future effects of DRR measures (beyond structural) are limited. In this paper, we use a global-scale flood risk model to estimate the risk of coastal flooding and to assess and compare the efficacy and economic performance of various DRR measures, namely dykes and coastal levees, dry-proofing of urban assets, zoning restrictions in flood-prone areas, and management of foreshore vegetation. To assess the efficacy of each DRR measure, we determine the extent to which it can limit future flood risk as a percentage of regional GDP to the same proportional value as today (a "relative risk constant" objective). To assess their economic performance, we estimate the economic benefits and costs of implementing each measure. If no DRR measures are implemented to mitigate future coastal flood risk, we estimate expected annual damages to exceed USD 1.3 trillion by 2080, directly affecting an estimated 11.5 million people on an annual basis. Low- and high-end scenarios reveal large ranges of impact uncertainty, especially in lower-income regions. On a global scale, we find the efficacy of dykes and coastal levees in achieving the relative risk constant objective to be 98 %, of dry-proofing to be 49 %, of zoning restrictions to be 11 %, and of foreshore vegetation to be 6 %. In terms of direct costs, the overall figure is largest for dry-proofing (USD 151 billion) and dykes and coastal levees (USD 86 billion), much more than those of zoning restrictions (USD 27 million) and foreshore vegetation (USD 366 million). These two more expensive DRR measures also exhibit the largest potential range of direct costs. While zoning restrictions and foreshore vegetation achieve the highest global benefit–cost ratios (BCRs), they also provide the smallest magnitude of overall benefit. We show that there are large regional patterns in both the efficacy and economic performance of modelled DRR measures that display much potential for flood risk reduction, especially in regions of the world that are projected to experience large amounts of population growth. Over 90 % of sub-national regions in the world can achieve their relative risk constant targets if at least one of the investigated DRR measures is employed. While future research could assess the indirect costs and benefits of these four and other DRR measures, as well as their subsequent hybridization, here we demonstrate to global and regional decision makers the case for investing in DRR now to mitigate future coastal flood risk.

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080 if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential in cost-effectively reducing (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.