A comparative analysis of flood damage models: lessons learnt and future challenges

Arrighi, Chiara; Ballio, Francesco; Carisi, Francesca; Castelli, Fabio; Domeneghetti, Alessio; Gallazzi, Alice; Galliani, Marta; Grelot, Frédéric; Kellermann, Patric; Kreibich, Heidi; Molinari, Daniela; Mohor, Guilherme S.; Mosimann, Markus; Natho, Stephanie; Richert, Claire; Schroeter, Kai; Scorzini, Anna Rita; Thieken, Annegret H.; Zischg, Andreas P. (2020). A comparative analysis of flood damage models: lessons learnt and future challenges. In: Floodrisk 2020 - 4th European Conference on Flood Risk Management (null-null). Online: Budapest University of Technology and Economics 10.3311/FloodRisk2020.9.15

Preview

Text Arrighi_etal2021.pdf - Published Version Available under License Publisher holds Copyright. Download (2MB) | Preview

The estimation of flood losses implies the use of
vulnerability/exposure models for flood damage and risk
assessment (Meyer et al. 2013; Zischg et al. 2018;
Wagenaar et al. 2018; Molinari et al. 2019). According to
Gerl et al. (2016), in central Europe there are 28 models
with 652 functions to assess flood losses, whereas almost
half of the functions refer to residential buildings. The
main differences among damage models are: (i) spatial
scale, (ii) metric (i.e. absolute or relative loss), (iii)
exposure assessment (i.e. whole building or affected
floors, market values or reconstruction-replacement costs),
(iv) number of input variables, (v) calibration/validation
with observed losses. These differences pose the issue of
model transferability to other urban or environmental
contexts.
With a joint effort of eight research groups, the objective
of this study is to test and compare damage models for
residential buildings used or developed by each group, by
applying them in a “blind exercise”, i.e. a common flood
case study characterised by high availability of hazard and
building data, with unknown information on observed
losses in the implementation stage of the models.
As the research groups use approaches representing many
different types and characteristics of models (e.g.
univariable – multivariable; absolute – relative; graduated
– regression – machine learning), being calibrated based on empirical data stemming from different countries
(Austria, France, Germany, Italy, Japan, Netherlands),
with different landscapes, the blind exercise provides an
extensive comparison of models and their transferability.
The analysis of the differences in the ensemble of model
outcomes aims at pointing out common patterns or
divergent behaviours between model outcomes and with
respect to observed losses.

Interest & Impact

Downloads

268 since deposited on 28 Apr 2021
33 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item