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.

Hydrological extremes, such as droughts and floods, can trigger a complex web of compound and cascading impacts due to interdependencies between coupled natural and social systems. However, current decision-making processes typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to better inform effective adaptation planning. This perspective paper aims to bridge this critical gap by presenting methods for assessing the dynamics of the multi-sector compound and cascading impacts (CCI) of hydrological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary roles for analyses building on quantitative (e.g. data-mining, systems modeling) and qualitative methods (e.g. mental models, qualitative storylines). We believe the data-driven and knowledge-driven methods provided here can serve as a useful starting point for understanding the dynamics of both high-frequency CCI and low-likelihood but high-impact CCI. With this perspective, we hope to foster research on CCI to improve the development of adaptation strategies for reducing the risk of hydrological extremes.

In the coming decades, the frequency of coastal flooding will increase due to sea-level rise and changes in climate extremes. We force the Global Tide and Surge Model (GTSM) with a climate model ensemble from the CMIP6 High Resolution Model Intercomparison Project (HighResMIP) to produce global projections of extreme sea levels (defined as tides and storm surge) from 1950 to 2050. This is the first time that an ensemble of global ~25km resolution climate models is used for this purpose, which increases the credibility of projected storm surges. Here we validate the historical simulations (1985-2014) against the ERA5 climate reanalysis. The overall performance of the HighResMIP ensemble is good with mean bias smaller than 0.1 m. However, there is a strong large-scale spatial bias. Future projections for the high emission SSP5-8.5 scenario indicate changes up to 0.1 m or 20% in 10-year return period surge level from 1951-1980 to 2021-2050. Increases are seen in parts of the coastline of the Caribbean, Madagascar and Mozambique, Alaska, and northern Australia, whereas the Mediterranean region may see a decrease. The full dataset underlying this analysis, including timeseries and statistics, is openly available on the Climate Data Store and can be used to inform broad-scale assessment of coastal impacts under future climate change.

In a warmer world, the hydrological cycle will change in intensity and in its geographic behaviour. This, in turn, will change patterns of river flood and the risk associated with them. The Last Interglacial (LIG; 125,000 years ago) is the most recent instance of climate warmer than today - especially in the high northern latitudes-, sea level was higher, ice sheets were smaller and monsoons were stronger. We use daily output from multi-century LIG simulations of an ensemble of paleoclimate models, and study how global precipitation patterns and extremes de­viate from the preindustrial climate. We validate these results by comparing them with the first compilation, to our knowledge, of global LIG precipitation patterns. Successively, we use the daily temperature and precipitation from the paleoclimate models to drive two global hydrological models (PCR-GLOBWB and CWATM), and simulate river discharges at 5-30’ resolution. With this, we force a hydrodynamic model, CaMa-Flood, and produce floods maps for different return periods. At the end of this model cascade, we look into what would happen if a climate similar to the LIG were to materialize in the coming decades: we combine the flood maps with maps of exposure through vulnerability relationships, and to calculate the risk that floods may pose to future people and assets.

Coastal risks are increasing due to the warming of the climate, resulting in rising mean sea levels and changes in storminess. Projections of future coastal flooding rely on global climate models based on greenhouse gas scenarios with inherent large uncertainties. The past warm climate of the Last Interglacial (LIG, ~127,000 years ago) is considered a partial analogue of a future warmer world. Therefore, understanding how coastal systems were affected by changes in atmospheric and relative sea levels during the LIG can inform us about possible future changes. In this contribution we will analyze extreme sea levels and coastal flooding during the LIG. The analysis is based on the hydrodynamic Global Tide and Surge Model (GTSM; Muis et al., 2016, doi: 10.1038/ncomms11969). To simulate storm surges during the LIG GTSM will be forced by 6-hourly wind and surface pressure fields from LIG simulations of IPCC-type climate models. Due to non-linear effects, tides and surge levels will be influenced by changes in mean sea level. Therefore, a key input variable is map of regional mean sea levels during LIG. However, there is still considerable uncertainty on sea level high-stands and regional patterns during the LIG. Using output from a Glacial Isostatic Adjustment model (GIA), we will model tides and surges for a set of plausible scenarios of relative sea levels and assess sensitivities.

We investigate hydrology during a past climate slightly warmer than the present: the Last Interglacial (LIG). With daily output of pre-industrial and LIG simulations from eight new climate models we force hydrological model PCR-GLOBWB, and in turn hydrodynamic model CaMa-Flood. Compared to pre-industrial, annual mean LIG runoff, discharge, and 100-year flood volume are considerably larger in the Northern Hemisphere, by 14%, 25% and 82%, respectively. Anomalies are negative in the Southern Hemisphere. In some boreal regions, LIG runoff and discharge are lower despite higher precipitation, due the higher temperatures and evaporation. LIG discharge is much higher for the Niger, Congo, Nile, Ganges, Irrawaddy, Pearl, and lower for the Mississippi, Saint Lawrence, Amazon, Paraná, Orange, Zambesi, Danube, Ob. Discharge is seasonally postponed in tropical rivers affected by monsoon changes. Results agree with published proxies on the sign of discharge anomaly in 15 of 23 sites where comparison is possible.

Disaster risks are the results of complex spatiotemporal interactions between risk components, impacts and societal response. The complexities of these interactions increase when multi-risk events occur in fragile contexts characterized by ethnic conflicts, unstable governments, and high levels of poverty, resulting in impacts that are larger than anticipated. Yet, only few multi-risk studies explore human-environment interactions, as most studies are hazard-focused, consider only a single type of multi-risk interaction, and rarely account for spatiotemporal variations of risk components. Here, we developed a step-wise, bottom-up approach, in which a range of qualitative and semi-quantitative methods was used iteratively to reconstruct interactions and feedback loops between risk components and impacts of consecutive drought-to-flood events, and explore their spatiotemporal variations. Within this approach, we conceptualize disaster risk as a set of multiple (societal and physical) events interacting and evolving across space and time. The approach was applied to the 2017-2018 humanitarian crises in Kenya and Ethiopia, where extensive flooding followed a severe drought lasting 18-24 months. The events were also accompanied by government elections, crop pest outbreaks and ethnic conflicts. Results show that (1) the fragile Kenyan and Ethiopian contexts further aggravated drought and flood impacts; (2) heavy rainfall after drought led to both an increase and decrease of the drought impacts dependent on topographic and socio-economic conditions; (3) societal response to one hazard may influence risk components of opposite hazards. A better understanding of the human-water interactions that characterize multi-risk events can support the development of effective monitoring systems and response strategies.