Improving flood damage assessment models in Italy

Amadio, Mattia; Myšiak, Jaroslav; Carrera, Lorenzo; Koks, Elco

doi:10.1007/s11069-016-2286-0

Cited by 57 publications

(49 citation statements)

References 24 publications

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“…The scientific literature of the last decade shows a large number of innovative damage models that are capable of estimating flood loss starting from one or more predictive variables. Nevertheless, several authors indicate that damage models still provide an important source of uncertainty in flood damage estimates, leading to uncertainties which are comparable to or larger than those associated with any other component de Moel et al, 2012de Moel et al, , 2014Gerl et al, 2016;Merz et al, 2004Merz et al, , 2007Apel et al, 2009).…”

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confidence: 99%

“…One important source of uncertainty is the simplified representation of complex damaging processes in terms of a stage-damage function . Since White (1945) linked the water level to relative (i.e., the loss ratio) or monetary damages, most of the models used today stick to this concept, using only water depth to estimate relative loss (see, e.g., Penning-Rowsell et al, 2005;Smith, 1994;Apel et al, 2009;Kreibich et al, 2009;Merz et al, 2013). Other important influencing factors, such as flood duration and flow velocity, are often not considered (de Moel and Aerts, 2011;Merz et al, 2013).…”

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confidence: 99%

“…A further aspect that contributes to the overall uncertainty in flood risk assessment and modeling is the lack of suffi-cient, comparable and reliable high-quality flood loss data (Meyer et al, 2013;Molinari et al, 2014a;Amadio et al, 2016;Scorzini and Frank, 2015;Green et al, 2011). In the absence of empirical damage data, loss models are either selected from the literature or subjectively and schematically derived by experts using a synthetic approach (see, e.g., Penning-Rowsell et al, 2005;Merz et al, 2004Merz et al, , 2013Thieken et al, 2008;Kreibich et al, 2010;Dottori et al, 2016a).…”

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

Carisi

Schröter

Domeneghetti

et al. 2018

Nat. Hazards Earth Syst. Sci.

108

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Flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data, which is often rooted in a lack of protocols and reference procedures for compiling loss datasets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia River flood event of January 2014, when a sudden levee breach caused the inundation of nearly 52 km 2 in northern Italy. After this event local authorities collected a comprehensive flood loss dataset of affected private households including building footprints and structures and damages to buildings and contents. The dataset was enriched with further information compiled by us, including economic building values, maximum water depths, velocities and flood durations for each building. By analyzing this dataset we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni-and multivariable loss models for residential buildings and contents. The accuracy of the proposed models is compared with that of several flood damage models reported in the literature, providing additional insights into the transferability of the models among different contexts. Our results show that (1) even simple univariable damage models based on local data are significantly more accurate than literature models derived for different contexts;(2) multivariable models that consider several explanatory variables outperform univariable models, which use only water depth. However, multivariable models can only be effectively developed and applied if sufficient and detailed information is available.

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

Carisi

Schröter

Domeneghetti

et al. 2018

Nat. Hazards Earth Syst. Sci.

108

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“…This confirms how challenging it is to identify a regional or large scale model with a general validity (see also Sec. 1 and Cammerer et al, 2013;Amadio et al, 2016;Molinari et al, 2012).…”

Section: Validation Of Locally Derived Damage Modelsmentioning

confidence: 96%

“…To completely exploit the potential of such models and sustain the possibility to export their use in different areas is necessary to pursue a detailed and structured acquisition of explanatory variables. According to Amadio et al (2016), Molinari et al (2012), Molinari et al (2014b), andScorzini andFrank (2015), the most urgent need in Italy, as far as loss estimation is concerned,…”

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confidence: 99%

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

Carisi¹,

Schröter²,

Domeneghetti³

et al. 2017

Preprint

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Abstract.Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the 5 inundation of nearly 52 km 2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni-and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional 10 insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth.However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

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A New Economic Loss Assessment System for Urban Severe Rainfall and Flooding Disasters Based on Big Data Fusion

Wu¹,

Guo²

2021

Economic Impacts and Emergency Management of Disasters in China

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Improving flood damage assessment models in Italy

Cited by 57 publications

References 24 publications

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

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

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

A New Economic Loss Assessment System for Urban Severe Rainfall and Flooding Disasters Based on Big Data Fusion

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