Abstract

Abstract. Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse eight global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, include industry models, pluvial flooding, and flood protection standards in the analysis. In doing so, we provide new insights into how these components change the results of this comparison. We find substantial variability, up to a factor of 4, between the flood hazard maps in the modelled inundated area and exposed gross domestic product (GDP) across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. The inclusion of industry models, which currently model flooding at a higher spatial resolution and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. We find that the addition of pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 percentage points. Our findings strongly highlight the importance of flood defences for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of the area of China) but locally detailed layer of structural defences in high-exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8 %.

Highlights

  • IntroductionBetween 1980 and 2016, floods caused 23 % of overall economic losses and 14 % of fatalities due to natural hazards worldwide (Löw, 2018)

  • Floods are one of the most frequent and most devastating kinds of natural disasters

  • We further investigate the current differences between flood hazard maps of global flood risk models (GFMs), as opposed to a validation study, as the addition of the flood protection and pluvial components provide valuable new insights in their effects on the variability in results

Read more

Summary

Introduction

Between 1980 and 2016, floods caused 23 % of overall economic losses and 14 % of fatalities due to natural hazards worldwide (Löw, 2018). In 2016, economic losses from flooding amounted to USD 56 billion globally. Understanding the risk of natural hazards, including flood risk, has been identified as a priority in recent international risk reduction frameworks, such as the Sendai Framework for Disaster Risk Reduction (UNISDR, 2015). Significant scientific efforts have been carried out to develop global flood risk models (GFMs) (Teng et al, 2017). Attention has especially been paid to developing global flood hazard maps. These maps indicate the severity of the hazard for different exceedance probabilities across the globe

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call