Multi-model ensembles for assessment of flood losses and associated uncertainty

Rui Figueiredo,Kai Schröter,Alexander Weiss-Motz,Heidi Kreibich,Mario L V Martina

doi:10.5194/nhess-18-1297-2018

Abstract

Abstract. Flood loss modelling is a crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a model rating framework to support ensemble construction, based on a probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.

Highlights

Effective management of flood risk requires comprehensive risk assessment studies that consider the hazard component, and the impacts that the phenomena may have on the built environment, economy and society (Messner and Meyer, 2006)
The predictive performance of single loss models and ensemble means is evaluated in terms of accuracy and systematic bias, using respectively the root mean squared error (RMSE) and the mean bias error (MBE)
The uncertain nature of flood loss estimations means that the performance of any single deterministic model may vary considerably from case to case, as large disparities in model outcomes exist even among apparently comparable models. This approach is flawed at two main levels: first, flood risk estimates are highly sensitive to the selection of the flood loss model, and second, deterministic estimates of loss do not lead to optimal decision-making

Summary

Introduction

Effective management of flood risk requires comprehensive risk assessment studies that consider the hazard component, and the impacts that the phenomena may have on the built environment, economy and society (Messner and Meyer, 2006). This integrated approach has gained importance over recent decades, and with it so has the scientific attention given to flood vulnerability models describing the relationships between flood intensity metrics and damage to physical assets, known as flood loss models. The limited predictive ability and high degree of uncertainty associated with such models has been acknowledged (Krzysztofowicz and Davis, 1983; Merz et al, 2004), and more complex models that consider additional explanatory variables have been developed

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Conclusion