Multi-Model Ensembles for Assessment of Flood Losses And
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Nat. Hazards Earth Syst. Sci., 18, 1297–1314, 2018 https://doi.org/10.5194/nhess-18-1297-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Multi-model ensembles for assessment of flood losses and associated uncertainty Rui Figueiredo1,2, Kai Schröter2, Alexander Weiss-Motz2, Mario L. V. Martina1, and Heidi Kreibich2 1Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy 2GFZ German Research Centre for Geosciences, Sect. 5.4: Hydrology, Potsdam, Germany Correspondence: Rui Figueiredo (rui.fi[email protected]) Received: 2 October 2017 – Discussion started: 16 October 2017 Revised: 26 March 2018 – Accepted: 11 April 2018 – Published: 3 May 2018 Abstract. Flood loss modelling is a crucial part of risk as- have on the built environment, economy and society (Mess- sessments. However, it is subject to large uncertainty that is ner and Meyer, 2006). This integrated approach has gained often neglected. Most models available in the literature are importance over recent decades, and with it so has the scien- deterministic, providing only single point estimates of flood tific attention given to flood vulnerability models describing loss, and large disparities tend to exist among them. Adopt- the relationships between flood intensity metrics and damage ing any one such model in a risk assessment context is likely to physical assets, also known as flood loss models. A large to lead to inaccurate loss estimates and sub-optimal decision- number of models have become available in the scientific lit- making. In this paper, we propose the use of multi-model en- erature. However, despite progress in this field, many chal- sembles to address these issues. This approach, which has lenges persist in their development, and flood loss models been applied successfully in other scientific fields, is based tend to be quite heterogeneous. This often results in practi- on the combination of different model outputs with the aim cal difficulties when they are to be applied in risk assessment of improving the skill and usefulness of predictions. We first studies (Gerl et al., 2016; Jongman et al., 2012), as described propose a model rating framework to support ensemble con- below. struction, based on a probability tree of model properties, Flood damage mechanisms are complex, being dependent which establishes relative degrees of belief between candi- on different properties of flood events, such as water depth, date models. Using 20 flood loss models in two test cases, flow velocity and flood duration, as well as on the physical we then construct numerous multi-model ensembles, based characteristics of the exposed assets (Kelman and Spence, both on the rating framework and on a stochastic method, 2004). Precautionary and socio-economic factors can also in- differing in terms of participating members, ensemble size fluence their degree of vulnerability (Thieken et al., 2005). and model weights. We evaluate the performance of ensem- Building accurate and reliable flood loss models that account ble means, as well as their probabilistic skill and reliability. for all these factors is a challenging task. Model development Our results demonstrate that well-designed multi-model en- is hampered by limited knowledge about damage-influencing sembles represent a pragmatic approach to consistently ob- factors, as well as limited data availability (Merz et al., 2010). tain more accurate flood loss estimates and reliable probabil- It is therefore unsurprising that traditional flood loss mod- ity distributions of model uncertainty. els tend to be rather simple, often using water depth as the only explanatory variable to describe damage and loss to coarsely defined groups of assets (Green et al., 2011; Smith, 1994). However, the limited predictive ability and high de- 1 Introduction gree of uncertainty associated with such models has been ac- knowledged (Krzysztofowicz and Davis, 1983; Merz et al., Effective management of flood risk requires comprehensive 2004), and more complex models that consider additional ex- risk assessment studies that consider not only the hazard planatory variables have been developed (Dottori et al., 2016; component, but also the impacts that the phenomena may Published by Copernicus Publications on behalf of the European Geosciences Union. 1298 R. Figueiredo et al.: Multi-model ensembles for assessment of flood losses and associated uncertainty Elmer et al., 2010; Merz et al., 2013). Regardless, uncertainty deterministic (Gerl et al., 2016), providing single point esti- in flood loss modelling is to some extent inevitable (Schröter mates of loss. Such estimates are unable to meet the decision et al., 2014). needs of different stakeholders, who may have differing risk Furthermore, flood loss models are usually developed for attitudes or cost-benefit ratios for risk mitigation measures specific regions, ranging from country to catchment or mu- (Merz and Thieken, 2009). Moreover, the uncertain nature nicipality level, with smaller scales making up the majority of flood loss estimations means that the performance of any of models (Gerl et al., 2016). Lack of available flood loss given deterministic model that appears appropriate for a cer- models in many regions often leads to the transfer of mod- tain application can be limited, as large disparities may exist els in space, resulting in their application to contexts with even among seemingly comparable models (Jongman et al., different built environments and/or socio-economic settings 2012; Merz and Thieken, 2009). This makes flood risk es- than originally intended. However, this is generally done timates highly sensitive to loss model selection (Apel et al., with insufficient justification, and flood loss models have 2009; Wagenaar et al., 2016). It is thus clear that adopting been shown to offer lower predictive ability under such cir- a single deterministic model for the estimation of flood losses cumstances (Cammerer et al., 2013; Jongman et al., 2012; is not recommended, as the information it provides is insuf- Schröter et al., 2014). ficient for optimal decision-making, and the results will po- In addition, flood loss models are most often constructed tentially, and very likely, be inaccurate. Even though research for specific flood types (e.g. fluvial flood, flash flood, coastal on flood loss modelling has recently started to move into the flood) and will usually be poorly suited to estimate loss probabilistic domain (Custer and Nishijima, 2015; Dottori due to flood events with other dominant damaging pro- et al., 2016; Kreibich et al., 2017; Schröter et al., 2014; Vogel cesses (Kreibich and Dimitrova, 2010; Kreibich and Thieken, et al., 2012), probabilistic models are still scarce. 2008). Models also vary in the way loss is expressed, which Multi-model ensembles have been successfully applied in can be either in monetary terms or as a fraction of the value scientific fields such as hydrology or weather forecasting of the element at risk (Messner et al., 2007). These are re- to tackle similar issues to those discussed above. Ensem- ferred to respectively as absolute and relative flood loss mod- ble means have been shown to almost always outperform in- els, the latter being better suited than the former for applica- dividual models (Georgakakos et al., 2004; Gleckler et al., tion across different study cases (Krzysztofowicz and Davis, 2008; Reichler and Kim, 2008), and the combination of the 1983). Further differences may exist in terms of other model output of different models can be a pragmatic approach to es- attributes. timate model uncertainty (Palmer et al., 2004; Weigel et al., Due to this large heterogeneity, it is difficult to identify 2008). However, in the context of vulnerability modelling, flood loss models that, given their attributes, are potentially the concept of combining multiple models is relatively new. the most appropriate for application in specific risk assess- Rossetto et al. (2014) and Spillatura et al. (2014) have pro- ment studies. Ideally, for any given application setting, a per- posed the use of mean model estimates as part of their studies fectly suited model (e.g. similar type of asset, no spatial on respectively fragility and vulnerability curves for seismic transfer required, validated with local evidence) would be risk assessment, but model performance is not evaluated and available and unambiguously identifiable, but unfortunately uncertainty quantification is not discussed. The potential use this is far from the case. The lack of an established proce- of multi-model ensembles in flood vulnerability assessment dure to select suitable flood loss models from the many avail- has not been addressed before. able in the literature means that model selection is often done This study therefore aims to answer the following research rather arbitrarily (Scorzini and Frank, 2015), which can nega- questions: tively impact the quality of flood loss estimations and lead to suboptimal investment decisions based on model outcomes 1. Can multi-model ensembles be used to improve the ac- (Wagenaar et al., 2016). curacy of flood loss estimations? A critical issue in flood loss modelling is uncertainty 2. Are multi-model ensembles able to represent model un- (Merz et al., 2004), which is usually high and can signifi- certainty and provide reliable probabilistic estimates of cantly contribute to overall uncertainty in flood risk analyses flood loss? (de Moel and Aerts, 2011). Model uncertainty is mainly re- lated with parameter representation, whereby fewer param- 3. How should such ensembles be constructed? eters than those theoretically needed to describe physical damage processes are used, and with insufficient data and/or We first propose a framework to rate flood loss models knowledge about damage processes (Wagenaar et al., 2016). according to their potential skill and suitability as partici- Quantifying uncertainty is imperative, as this information is pating members in such ensembles. We then construct var- required to make informed decisions in the context of flood ious multi-model ensembles, based both on the rating frame- risk management (Downton et al., 2005; Peterman and An- work and on a state of simulated non-informativeness, dif- derson, 1999; USACE, 1992).