Abstract

AbstractFlood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss‐driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property‐level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time.

Highlights

  • Natural hazards have a large economic impact on human society

  • We address flood loss model transferability across four different flood types, that is, fluvial flooding, pluvial flooding, groundwater flooding, and inundation caused by levee breaches, by investigating the loss‐generating process for these flood types

  • Some differences in warning and preparedness features reflect the ability to better forecast large advective events in comparison to convective rainfall events (Einfalt et al, 2009; Rözer et al, 2016). This is noticeable as a longer warning lead time, more warning information, and a higher share of people receiving an official warning among those affected by levee breaches, followed by those affected by riverine floods, and a worse outcome in this respect for those impacted by surface and groundwater floods

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Summary

Introduction

Natural hazards have a large economic impact on human society. In Europe, for instance, natural hazards caused almost €557 billion in damages between 1980 and 2017 (European Environment Agency, 2019). The quality and consistency of loss reporting is important because these data are used to train and validate loss estimates Despite this importance, the patchy, unstandardized, and heterogeneous nature of current loss documentation and reporting after events results in a degree of uncertainty in loss estimation (Downton & Pielke, 2005; Handmer, 2003; Thieken, Bessel, et al, 2016). If reported data are to be used for deriving or training loss models, it is essential to link the (financial) impact to characteristics of the hydraulic load and the affected structure In this context, it is important to investigate, determine, and order the importance of different variables as a part of the loss‐generating process, which is likely to depend on the flood type (Kelman & Spence, 2004; Kreibich & Dimitrova, 2010). This knowledge and understanding, across flood types, MOHOR ET AL

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