Modeling Flood Damage: Case of Tokai Flood 2000

Zhai, Guofang; Fukuzono, Teruki; Ikeda, Saburo

doi:10.1111/j.1752-1688.2005.tb03719.x

Cited by 72 publications

(61 citation statements)

References 2 publications

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“…Penning-Rowsell and Chatterton, 1977;Parker et al, 1987;Klaus et al, 1994;IWK, 1999). Over the years a number of multiparameter models have also been developed, such as those by Nicholas et al (2001), Zhai et al (2005), Thieken et al (2008) and Kreibich et al (2010). Studies have also shown that the application of multivariate models improve the reliability of flood damage modeling (Apel et al, 2009;Wünsch et al, 2009;Elmer et al, 2010a;Seifert et al, 2010).…”

Section: Direct Costsmentioning

confidence: 99%

Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps

Meyer

Becker

Markantonis

et al. 2013

Nat. Hazards Earth Syst. Sci.

381

385

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Efficiently reducing natural hazard risks requires a thorough understanding of the costs of natural hazards. Current methods to assess these costs employ a variety of terminologies and approaches for different types of natural hazards and different impacted sectors. This may impede efforts to ascertain comprehensive and comparable cost figures. In order to strengthen the role of cost assessments in the development of integrated natural hazard management, a review of existing cost assessment approaches was undertaken. This review considers droughts, floods, coastal and Alpine hazards, and examines different cost types, namely direct tangible damages, losses due to business interruption, indirect damages, intangible effects, and the costs of risk mitigation. This paper provides an overview of the state-of-the-art cost assessment approaches and discusses key knowledge gaps. It shows that the application of cost assessments in practice is often incomplete and biased, as direct costs receive a relatively large amount of attention, while intangible and indirect effects are rarely considered. Furthermore, all parts of cost assessment entail considerable uncertainties due to insufficient or highly aggregated data sources, along with a lack of knowledge about the processes leading to damage and thus the appropriate models required. Recommendations are provided on how to reduce or handle these uncertainties by improving data sources and cost assessment methods. Further recommendations address how risk dynamics due to climate and socio-economic change can be better considered, how costs are distributed and risks transferred, and in what ways cost assessment can function as part of decision support

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Section: Direct Costsmentioning

confidence: 99%

Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps

Meyer

Becker

Markantonis

et al. 2013

Nat. Hazards Earth Syst. Sci.

381

385

View full text Add to dashboard Cite

show abstract

“…Zhai et al (2005) developed a multi-variate regression model with inundation depth, house ownership, house structure, length of residence and household income to estimate losses in private households in Japan. To our knowledge, this model has not been validated or compared with other models, so its uncertainty is unknown.…”

Section: B Merz Et Al: Multi-variate Flood Damage Assessmentmentioning

confidence: 99%

Multi-variate flood damage assessment: a tree-based data-mining approach

Merz

Kreibich

Lall

2013

Nat. Hazards Earth Syst. Sci.

221

288

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Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damageinfluencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.

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“…Recently, models that consider more parameters besides the water level were introduced. For example, Zhai et al [2005] developed a multivariate regression model with inundation depth, home ownership, house structure, length of residence and household income to estimate losses in private households. Thieken et al [2008] present the new flood loss estimation model FLEMOps.…”

Section: Flood Loss Estimationmentioning

confidence: 99%

Assessment of damage caused by high groundwater inundation

Kreibich

Thieken

2008

Water Resources Research

108

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[1] In several cities around the world, problems with rising groundwater levels were reported, but up to now, losses caused by high groundwater levels have been neglected in loss assessment studies. However reliable loss assessments are necessary to evaluate the cost-effectiveness of mitigating measures like groundwater withdrawal and drainage. To improve the knowledge about flood losses and the loss-influencing factors, 1697 households affected by the Elbe and Danube floods in 2002 in Germany were interviewed. Cases affected by a high groundwater level were identified and analyzed in comparison with cases affected by riverine flooding. Inundations due to high groundwater show significantly different impact and damage characteristics in comparison with riverine floods. Most common loss models tested were as such not suitable to estimate losses due to high groundwater. Therefore, new loss models have to be developed. One step in this direction is the development of loss models just on basis of loss data from groundwater flooding. From all such models tested, the multifactorial model FLEMOps+ performed best, confirming that the uncertainty in loss estimation can be reduced if more predictive variables, besides the water level, are taken into consideration. However further research is necessary to investigate the main factors influencing the losses due to groundwater flooding and to develop specific models for their assessment.

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Modeling Flood Damage: Case of Tokai Flood 2000

Cited by 72 publications

References 2 publications

Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps

Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps

Multi-variate flood damage assessment: a tree-based data-mining approach

Assessment of damage caused by high groundwater inundation

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