Improving Global-scale Coastal Flood Risk Estimates By Considering
Spatial Dependence
Abstract
Current large-scale coastal flood risk assessments are typically based
on scenarios considering a range of spatially uniform return periods
(RP). These assessments do not account for the spatial variability of
real flood events, and only estimate average annual losses. In this
study, we address these limitations by developing a novel event-based
probabilistic framework to capture the spatial dependence structure of
coastal floods, and use it to investigate the effects of spatial
dependence on national flood risk estimates globally. We show that the
widely-used RP-based approach gives lower damage estimates for
relatively low return periods while higher damages are estimated for
medium-to-large return periods. The intersection point where lower
damage estimations turn into higher damage estimations varies across
countries and is primarily dependent on local flood protection
standards. We also provide the first global mapping of differences in
risk indicators between these two approaches in terms of expected annual
damages (EAD) and 1-in-200-year damages. We show that spatial dependence
has minor effects on the EAD but the RP200 damage is estimated higher
for 76% of global countries by the RP-based approach. Accounting for
flood protection standards is found to increase these differences.
Lastly, we demonstrate the added value of our approach by showing the
flood damages of the simulation year with the highest combined annual
damages at a subnational scale for each continent. Our framework
provides more accurate large-scale coastal flood risk estimates, which
can aid in better regional planning decisions, more precise insurance
pricing, and improved emergency responses.