Quantification of flash flood economic risk using ultra-detailed stage–damage functions and 2-D hydraulic models

Garrote, Julio; Alvarenga, F.M.; Díez‐Herrero, Andrés

doi:10.1016/j.jhydrol.2016.02.006

Cited by 59 publications

(44 citation statements)

References 19 publications

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“…Also our findings align with the results of previous studies (Spekkers et al, 2013;Zhou et al, 2013;Wobus et al, 2014;Torgersen et al, 2015) and further indicate that insurance databases are a promising source for flood damage assessment at local (Garrote et al, 2016;Le Bihan et al, 2017;Zischg et al, 2018;Zhou et al, 2013) and at regional scale (Barredo et al, 2012;Kim et al, 2012;Wobus et al, 2014;Zhou et al, 2017).…”

Section: Discussionsupporting

confidence: 91%

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The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

Cortès

Turco

Llasat-Botija

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Floods in the Mediterranean region are often surface water floods, in which intense precipitation is usually the main driver. Determining the link between the causes and impacts of floods can make it easier to calculate the level of flood risk. However, up until now, the limitations in quantitative observations for flood-related damages have been a major obstacle when attempting to analyse flood risk in the Mediterranean. Flood-related insurance damage claims for the last 20 years could provide a proxy for flood impact, and this information is now available in the Mediterranean region of Catalonia, in northeast Spain. This means a comprehensive analysis of the links between flood drivers and impacts is now possible. The objective of this paper is to develop and evaluate a methodology to estimate flood damages from heavy precipitation in a Mediterranean region. Results show that our model is able to simulate the probability of a damaging event as a function of precipitation. The relationship between precipitation and damage provides insights into flood risk in the Mediterranean and is also promising for supporting flood management strategies.

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Section: Discussionsupporting

confidence: 91%

“…slope, soil characteristics, vegetation) could provide additional understanding on flood drivers and impacts. For instance, in Garrote et al (2016) different simulation scenarios were defined considering the modifications to the terrain due to construction of fluvial defence structures in the area.…”

Section: Discussionmentioning

confidence: 99%

The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

Cortès

Turco

Llasat-Botija

et al. 2018

Nat. Hazards Earth Syst. Sci.

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“…Los indicadores del índice abordan las componentes social, económica, ambiental y física o territorial de la vulnerabilidad, aunque, por su propia concepción generalista, resulta poco preciso. Aplicaciones recientes en el ámbito de la cartografía de vulnerabilidad frente al riesgo de inundación pueden encontrarse en trabajos como el de Bescos y Camarasa (2004), Camarasa et al (2008); Ruiz (2011);Garrote et al (2016);Paucar (2016);Castillo (2016);o Aroca-Jiménez et al (2016).…”

Section: Ii2 Antecedentes Metodológicos Aplicados Y Dificultades Asunclassified

Cartografía de la vulnerabilidad del territorio frente al riesgo de inundación. Propuesta adaptada a la directiva europea de inundaciones y normativas derivadas

Roselló¹,

Barrionuevo²,

Prados³

2017

BAGE

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En el artículo se aporta una propuesta de catálogo cartográfico de vulnerabilidad adaptado a los requerimientos de la normativa europea, a los Planes de Gestión del Riesgo y a los Planes de Ámbito Local a aplicar en las ARPS (Áreas de Riesgo Potencial Significativo). Se reflexiona sobre el concepto de vulnerabilidad frente al riesgo, y se puntualizan aspectos cuya definición imprecisa o dispersa dificulta la elaboración de indicadores y cartografías de vulnerabilidad consensuadas. Se adjuntan ejemplos de la cartografía propuesta aplicados al área de inundación del río Guadalhorce (Málaga).

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“…Although some of these works assessed the flood damage prediction, few attempted to predict the potential property damage of the flash flooding events. In addition, the majority of damage prediction studies are conducted at small-scale regional domains, explicitly applicable to the region of interest (Scheuer et al 2011, Garrote et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

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Quantification of flash flood economic risk using ultra-detailed stage–damage functions and 2-D hydraulic models

Cited by 59 publications

References 19 publications

The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

Cartografía de la vulnerabilidad del territorio frente al riesgo de inundación. Propuesta adaptada a la directiva europea de inundaciones y normativas derivadas

Leveraging machine learning for predicting flash flood damage in the Southeast US

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