Testing empirical and synthetic flood damage models: the case of Italy

Abstract

Abstract. Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.

Direct flood risk assessment of the European road network: an object-based approach

“…Continental-scale models typically work with functions relating damage to water depth only (Alfieri et al, 2016a;de Moel et al, 2015;Winsemius et al, 2013), an approach 205 also adopted in this study. Although damage predictions may significantly improve from more complex damage models under some conditions (Schröter et al, 2014), the inclusion of more parameters does not necessarily give better results, especially not when data availability is scarce (Amadio et al, 2019). Therefore, we follow the recommendation of Schröter et al (2014) to quantify the uncertainty around our estimates, which is mainly determined by uncertainty about the flow 210 velocity.…”

“…2.3.4 The Huizinga reference curve A comprehensive set of depth-damage curves has been proposed by Huizinga et al (Huizinga, 2007;Huizinga et al, 2017), which has been applied in many studies (e.g. Albano et al, 2017;Amadio et al, 2019Amadio et al, , 2016Carisi et al, 2018;Dottori et al, 2018b;Jongman et al, 2012;Prahl et al, 2018). We used the 'EU-average curve' for road infrastructures (Huizinga, 2007) to compare to our new curves.…”

Empirical flash flood vulnerability functions for residential buildings

“…Vulnerability is often represented in terms of stage-damage functions, also called damage functions in the literature, which are associated with a flood parameter, commonly water depth, a relative amount of loss or an absolute monetary loss. Vulnerability functions are often developed based on expert judgment [4][5][6], and sometimes they are derived from observed losses [7][8][9]. Recently also some physical laboratory models have been developed to better investigate the key damage mechanisms [10,11].…”

“…Other inundation parameters are also used in the literature to better define vulnerability such as flow velocity, presence of contamination, flood duration, etc., and they are usually based on large dataset and learning algorithms [7,8]. However, several uncertainties affect simulated inundation parameters which might alter the estimated losses [17,18].…”

Beyond the stage-damage function: Estimating the economic damage on residential buildings from storm surges

“…65 These studies are carried out especially within the fields of pluvial and fluvial flooding (e.g. Ootegem et al (2015); Spekkers et al (2014); Merz et al (2004Merz et al ( , 2010; Amadio et al (2019); Pistrika et al (2014); Jongman et al (2012)). ).…”