Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

Carisi, Francesca; Schröter, Kai; Domeneghetti, Alessio; Kreibich, Heidi; Castellarin, Attilio

doi:10.5194/nhess-2017-342

Cited by 4 publications

(9 citation statements)

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“…Various flood loss models have been developed for estimating direct economic loss to buildings (Carisi et al, 2018;Merz, Hall, et al, 2010;Schröter et al, 2014;Smith, 1994). Many models represent the loss in terms of relative loss, which is the ratio between costs of loss and the value of asset at the time of the event.…”

Section: 1029/2018ef000994mentioning

confidence: 99%

Quantifying Flood Vulnerability Reduction via Private Precaution

et al. 2019

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Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.

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Section: 1029/2018ef000994mentioning

confidence: 99%

Quantifying Flood Vulnerability Reduction via Private Precaution

et al. 2019

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“…Since actual compensation often covers only a fraction of the damage costs, claimed damage is preferred in order to measure the economic impact (see Carisi et al 2018). We restricted our analysis on direct monetary damage to the structure of residential buildings, excluding furniture and vehicles.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Empirical, event-specific damage models are developed from observed flood loss data. A major drawback of empirically-based damage models relies on its low transferability to other study areas or regions, as significant errors are often verified when these are used to infer damage in other regions than those for which they were built to (Amadio et al, 2016;Apel et al, 2004;Carisi et al, 2018;Hasanzadeh Nafari et al, 2017;Jongman et al, 2012;Merz et al, 2004;Scorzini and Frank, 2015;Scorzini and Leopardi, 2017;Wagenaar et al, 2016). Synthetic models, on the other hand, are based on ''what-if analyses'', relying on expert-based knowledge in order to generalise the relation between the magnitude of a hazard event and the resulting economic damage.…”

Section: Introductionmentioning

confidence: 99%

“…The vast majority of univariable flood damage models account for water depth as the only explanatory variable to explain the often complex relation between the magnitude of a flood event and the resulting damages; however, a nonexhaustive literature search shows that other parameters may influence the flood damage process, such as flow velocity (Kreibich et al, 2009), flood duration, and water contamination (Molinari et al, 2014;Thieken et al, 2005), to name just a few. In addition, a large number of other non-hazard factors can be significantly different from one place to another, such as type and quality of buildings, presence of basements, density of dwellings, early warning systems and precautionary measures (Cammerer et al, 2013;Carisi et al, 2018;Figueiredo et al, 2018;Kreibich et al, 2005;Merz et al, 2013;Penning-Rowsell et al, 2005;Pistrika and Jonkman, 2010;Schröter et al, 2014;Smith, 1994;Thieken et al, 2008;Wagenaar et al, 2017b). Therefore, multivariable models (MVMs) are potentially better-suited alternatives to describe the complex flooddamage relation (Apel et al, 2009;Elmer et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, multivariable models (MVMs) are potentially better-suited alternatives to describe the complex flooddamage relation (Apel et al, 2009;Elmer et al, 2010). Common techniques applied in a context of MVM are machine learning (e.g., Artificial Neural Networks and Random Forests) (Merz et al 2013;Spekkers et al 2014; Kreibich et al 2017, Carisi et al 2018, Bayesian networks (Vogel et al, 2013), and Tobit estimation (Van Ootegem et al, 2015). Moreover, some MVMs support probabilistic analyis of damage (Dottori et al, 2016;Essenfelder, 2017;Wagenaar et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

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Estimating flood damage in Italy: empirical vs expert-based modelling approach

Amadio

Scorzini

Carisi

et al. 2018

Preprint

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Abstract. Flood risk management generally relies on economic assessments performed using flood loss models of different complexity, ranging from simple univariable to more complex multivariable models. These latter accounts for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. In this paper we collected a comprehensive dataset 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), buildings characteristics (size, type, quality, economic value) as well as 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 performance of four literature flood damage models of different nature and complexity are compared with the performance of univariable, bivariable and multivariable models empirically developed for Italy and tested at the micro scale based upon observed records. The uni- and bivariable models are produced testing linear, logarithmic and square root regression while multivariable models are based on two machine learning techniques, namely Random Forest and Artificial Neural Networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management.

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Flood risk assessments—Exploring maturity and challenges in Sweden

Arvidsson,

Johansson

2024

J Flood Risk Management

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Floods are destructive to society. Hence, to assess and manage flood risk is imperative. Flood risk assessments require several layers of analysis, from flood hazard to societal impact. This paper explores the maturity level and challenges related to three components of a comprehensive risk assessment: flood hazard, direct consequences, and indirect consequences. This is achieved by introducing and applying a maturity framework on flood hazard reports and flood risk management plans in Sweden, as required by the EU Floods Directive (FD). A longitudinal analysis is conducted over two FD cycles (2009–2015 and 2016–2021). Complementary interviews with county administrative boards and responsible authorities provide deeper insights into processes and practical challenges. The results reveal that the maturity level of flood hazard assessment is high, while direct and, in particular, indirect consequences assessments need increased attention and substantial improvements. Further, there is no significant increase in maturity between the FD cycles, indicating fundamental challenges towards achieving this. Critical steps forward include developing applicable methods for analysing both direct and indirect consequences of floods, improving data availability on functionality and interdependency of critical infrastructure and society at large, and creating incentives for a broader range of societal actors to participate in flood risk management.

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Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

Cited by 4 publications

References 5 publications

Quantifying Flood Vulnerability Reduction via Private Precaution

Quantifying Flood Vulnerability Reduction via Private Precaution

Estimating flood damage in Italy: empirical vs expert-based modelling approach

Flood risk assessments—Exploring maturity and challenges in Sweden

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