Capturing regional differences in flood vulnerability improves flood loss estimation

Sairam, Nivedita, Schröter, Kai, Steinhausen, Max J. ORCID logoORCID: https://orcid.org/0000-0002-8692-8824 and Kreibich, Heidi (2022) Capturing regional differences in flood vulnerability improves flood loss estimation. Frontiers in Water, 4 , 817625. ISSN 2624-9375 [Article] (doi:10.3389/frwa.2022.817625)

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Abstract

Flood vulnerability is quantified by loss models which are developed using either empirical or synthetic approaches. In reality, processes influencing flood risk are stochastic and loss predictions bear significant uncertainty, especially due to differences in vulnerability across exposed objects and regions. However, many state-of-the-art flood loss models are deterministic, i.e., they do not account for data and model uncertainty. The Bayesian Data-Driven Synthetic (BDDS) model was one of the first approaches that used empirical data to reduce the prediction errors at object-level and enhance the reliability of synthetic flood loss models. However, the BDDS model does not account for regional differences in vulnerability which may result in over-/under-estimation of losses in some regions. In order to overcome this limitation, this study introduces a hierarchical parameterization of the BDDS model which enhances synthetic flood loss model predictions by quantifying regional differences in vulnerability. The hierarchical parameterization makes optimal use of the process information contained in the overall data set for the various regional applications, so that it is particularly suitable for cases in which only a small amount of empirical data is available. The implementation and performance of the hierarchical parametrization is demonstrated with the Multi-Colored Manual (MCM) loss functions and empirical damage dataset from the UK consisting of residential buildings from the regions Appleby, Carlisle, Kendal and Cockermouth that suffered losses during the 2015 flood event. The developed model improves prediction accuracy of flood loss compared to MCM by reducing the absolute error and bias by at least 23 and 90%, respectively. The model reliability in terms of hit rate (i.e., the probability that the observed value lies in the 90% high density interval of predictions) is 88% for residential buildings from the same regions used for calibration and 73% for residential buildings from new regions. The approach is of high practical relevance for all regions where only limited amounts of empirical flood loss data is available.

Item Type: Article
Keywords (uncontrolled): Water, Multi-Colored Manual, Bayesian model, hierarchical model, flood loss model, households
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 34832
Notes on copyright: Copyright © 2022 Sairam, Schröter, Steinhausen and Kreibich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms
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Depositing User: Jisc Publications Router
Date Deposited: 04 Mar 2022 11:23
Last Modified: 04 Mar 2022 11:23
URI: https://eprints.mdx.ac.uk/id/eprint/34832

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