Global multi-hazard risk assessment in a changing climate

Abstract

Natural hazards pose significant risks to people and assets in many regions of the world. Quantifying associated risks is crucial for many applications such as adaptation option appraisal and insurance pricing. However, traditional risk assessment approaches have focused on the impacts of single hazards, ignoring the effects of multi-hazard risks and potentially leading to underestimations or overestimations of risks. In this work, we present a framework for modelling multi-hazard risks globally in a consistent way, considering hazards, exposures, vulnerabilities, and assumptions on recovery. We illustrate the approach using river floods and tropical cyclones impacting people and physical assets on a global scale in a changing climate. To ensure physical consistency, we combine single hazard models that were driven by the same climate model realizations. Our results show that incorporating common physical drivers and recovery considerably alters the multi-hazard risk. We finally demonstrate how our framework can accommodate more than two hazards and integrate diverse assumptions about recovery processes based on a national case study. This framework is implemented in the open-source climate risk assessment platform CLIMADA and can be applied to various hazards and exposures, providing a more comprehensive approach to risk management than conventional methods.

Figure 1

Illustration of the spatio-temporally compounding impact of TC and RF. Each exposure point is either impacted by TC (red) only, RF (blue) only, both (purple), or neither (grey). The impact is shown for the warming level $1{}^{\circ }$C for (a) assets and (b) population for a given example year chosen to best illustrate the compounding distribution heterogeneity. Note that the year for (a) is different than for (b). The maps were generated using CLIMADA V4.0.1 (https://zenodo.org/records/8383171).

Figure 2

Exposure points affected by TC, RF and their compound on average per year at a $1{}^{\circ }$C warming level. Panel (a) shows exposure points with at least 1000 dollar damage, and panel (b) describes exposure points with at least 100 people affected on average per year. The maps were generated using CLIMADA V4.0.1 (https://zenodo.org/records/8383171).

Figure 3

Impact return period curves for RF and TC at $1{}^{\circ }$C, illustrating the median and 90th percentile confidence intervals from 1000 samples of 500 years. Panel (a) represents assets, while panel (b) shows the impact on population.

Figure 4

Relative difference in the average 100-year impact, if common physical drivers are not considered or/and if impacts are not caped at the exposure value (assuming full recovery between single events).

Figure 5

Impact return period curves for RF and TC at $1{}^{\circ }$C and $2{}^{\circ }$C, illustrating the median and 90th percentile confidence intervals from 1000 samples of 500 years. Changes in exposure are not considered in this analysis. Panel (a) represents assets, while panel (b) shows the impact on population.

Figure 6

Illustration of a national case study for Vietnam, considering the impact of TC, RF, and HS. Panel (a) shows population exposure points affected by TC, RF, and HS for a spatio-temporally compounding 100-year event at $1{}^{\circ }$C global warming. Panel (b) illustrates the impact return period curve for people affected within the same year by these three hazards at $1{}^{\circ }$C and $2{}^{\circ }$C global warming. The map was generated using CLIMADA V4.0.1 (https://zenodo.org/records/8383171).

Figure 7

Time series of assets damaged and new impacts in Vietnam for the HadGEM2-ES RCP2.6 2006–2018 GCM run based on different recovery assumption for combined TC and RF. Panel (a) presents the percentage of assets value damaged. Panel (b) shows the new asset impacts.