A mathematical model for flood loss estimation

Dutta, Dushmanta; Herath, Srikantha; Musiake, Katumi

doi:10.1016/s0022-1694(03)00084-2

Cited by 476 publications

(355 citation statements)

References 10 publications

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“…Besides the interest within the scientific community, the need of flood loss estimations ranges from decisions on loss compensations by disaster funds and financial appraisals of the (re-)insurance sector, to risk maps required by legislation like the Floods Directive 2007/60/EC and evaluation of risk reduction projects (Dutta et al, 2003;Downton and Pielke, 2005;Merz et al, 2010;Jongman et al, 2012;Meyer et al, 2013). The European Floods Directive 2007/60/EC, for instance, requires that all European member states have flood hazard and flood risk maps at the river basin scale in areas of significant flood risk (EC, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, most damage models still use inundation depth as the main impact parameter (see e.g., Merz et al, 2010;Jongman et al, 2012 for an overview), but some models also integrate additional parameters like flow velocity (e.g., Schwarz and Maiwald, 2007;Pistrika and Jonkman, 2010), contamination (e.g., Kreibich and Thieken, 2008;Prettenthaler et al, 2010), the duration of flooding (e.g., Dutta et al, 2003;Penning-Rowsell et al, 2005) or the recurrence interval (e.g., Elmer et al, 2010). With regard to the consideration of different resistance parameters, the majority of damage models differentiates between the use or type of building (e.g., Oliveri and Santoro, 2000;Dutta et al, 2003;Kang et al, 2005;Büchele et al, 2006;Schwarz and Maiwald, 2007;Kreibich and Thieken, 2008;Thieken et al, 2008). Few models also take additional parameters, such as precautionary behavior (e.g., Büchele et al, 2006;Kreibich and Thieken, 2008;Thieken et al, 2008) or the early warning time (e.g., Penning-Rowsell et al, 2005), into account.…”

Section: Introductionmentioning

confidence: 99%

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Adaptability and transferability of flood loss functions in residential areas

Cammerer

Thieken

Lammel

2013

Nat. Hazards Earth Syst. Sci.

130

144

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Abstract. Flood loss modeling is an important component within flood risk assessments. Traditionally, stage-damage functions are used for the estimation of direct monetary damage to buildings. Although it is known that such functions are governed by large uncertainties, they are commonly applied -even in different geographical regions -without further validation, mainly due to the lack of real damage data. Until now, little research has been done to investigate the applicability and transferability of such damage models to other regions. In this study, the last severe flood event in the Austrian Lech Valley in 2005 was simulated to test the performance of various damage functions from different geographical regions in Central Europe for the residential sector. In addition to common stage-damage curves, new functions were derived from empirical flood loss data collected in the aftermath of recent flood events in neighboring Germany. Furthermore, a multi-parameter flood loss model for the residential sector was adapted to the study area and also evaluated with official damage data. The analysis reveals that flood loss functions derived from related and more similar regions perform considerably better than those from more heterogeneous data sets of different regions and flood events. While former loss functions estimate the observed damage well, the latter overestimate the reported loss clearly. To illustrate the effect of model choice on the resulting uncertainty of damage estimates, the current flood risk for residential areas was calculated. In the case of extreme events like the 300 yr flood, for example, the range of losses to residential buildings between the highest and the lowest estimates amounts to a factor of 18, in contrast to properly validated models with a factor of 2.3. Even if the risk analysis is only performed for residential areas, our results reveal evidently that a carefree model transfer in other geographical regions might be critical. Therefore, we conclude that loss models should at least be selected or derived from related regions with similar flood and building characteristics, as far as no model validation is possible. To further increase the general reliability of flood loss assessment in the future, more loss data and more comprehensive loss data for model development and validation are needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptability and transferability of flood loss functions in residential areas

Cammerer

Thieken

Lammel

2013

Nat. Hazards Earth Syst. Sci.

130

144

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“…Many vulnerability curve studies have been reported related to such phenomena as floods (Dutta et al 2003;Merz et al 2004), earthquakes (Colombi et al 2008), windstorms (Lee and Rosowsky 2005), and hail (Hohl et al 2002). Wang et al (2013) quantified wheat drought vulnerability.…”

Section: Introductionmentioning

confidence: 99%

An EPIC model-based vulnerability assessment of wheat subject to drought

Yue

et al. 2015

Nat Hazards

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This paper presents a regionalized vulnerability curve-building approach to vulnerability and risk assessment of wheat subjected to drought that uses the Environmental Policy Integrated Climate (EPIC) model and statistical analysis. We defined wheat vulnerability as the degree to which a wheat production system is likely to experience yield loss due to a perturbation or drought hazard. Wheat vulnerability in a given region is thus the yield loss divided by the drought hazard index (DHI yield losses and associated DHIs, wheat drought vulnerability curves can be developed. We propose that agricultural systems be considered uniform within each wheat-planting region and different in different regions, according to territorial differentiation, when regionalized vulnerability curves are built. Based on this principle, a detailed regional crop calendar was improved, and optimized wheat varieties were refined that can differentiate agricultural systems within wheat-planting regions. The crop calendar was improved based on the assumption that local farmers have perfect knowledge in selecting sowing and harvesting dates. The wheat varieties were optimized by adjusting the genetic parameters of wheat in the EPIC model using the Shuffled Complex Evolution algorithm-University of Arizona (SCE-UA) method. Based on these improvements and innovations, the precision of most vulnerability curves was improved, and the curves were compared favorably to those observed in previous studies related to differences in the genetic character of wheat, the crop calendar, environmental conditions, and other relevant factors. Differences within each region were smaller than differences between regions. More detailed wheat vulnerability curves allow for the assessment of expected wheat yield loss and also allow for a high level of precision in an evaluation, at a variety of scales, of risk of wheat subject to drought. The proposed approach to building regionalized vulnerability curves has the potential to be the basis for crop drought vulnerability curves in different geographical areas at multiple scales.

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“…It is considered that the flood hazard zone is an area to the level of 100-year water. In water management it is assumed that "the main flood hazard zone is the zone described on the basis of the annual peak flow characterized by the probability of exceeding p=1%" 12 .…”

Section: Introductionmentioning

confidence: 99%

Flood Risk Assessment of Łodź Province Communes

Borowska-Stefańska¹

2015

HSS

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A mathematical model for flood loss estimation

Cited by 476 publications

References 10 publications

Adaptability and transferability of flood loss functions in residential areas

Adaptability and transferability of flood loss functions in residential areas

An EPIC model-based vulnerability assessment of wheat subject to drought

Flood Risk Assessment of Łodź Province Communes

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