Flood Depth-Damage Functions for Built Environment

Pistrika, Aimilia K.; Tsakiris, George; Nalbantis, Ioannis

doi:10.1007/s40710-014-0038-2

Cited by 117 publications

(80 citation statements)

References 20 publications

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“…Specifically, for Event 1, the flood peak for the 95% uncertainty band ranges from 0.8 m 3 /s to 1.7 m 3 /s, 3 3 Figure 8. Probability Density Function (PDF) for observed and fitted flood peaks for Event 1 and Event 2.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, there are still open challenges in modelling in this field, due to the great complexity of the urban environments, the number of processes involved in the urban water cycle, as well as the great influence that the dynamics and spatial variability of rainfall events have on a model's results. Urban flood modelling, as part of urban hydrology, is of great importance [1], because of the high risk it imposes on people and properties [2,3] as well as its increasing frequency of occurrence over the last decades. Due to the complexity of the urban environment, an accurate modelling of flooding demands at least a two-dimensional (2D) approach [4].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

Bellos

Kourtis

Moreno-Rodenas

et al. 2017

Water

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Abstract:A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

Bellos

Kourtis

Moreno-Rodenas

et al. 2017

Water

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“…Kelman and Spence [40] mainly analyzed buildings' structural damage, which is determined by flood loads and building resistance. Pistrika, Tsakiris [41] described a detailed relationship between the flooding depth and the expected damage to buildings. In general, different real estate properties have different DDFs.…”

Section: Benefits From Avoided Flooding Damagementioning

confidence: 99%

“…To convert both structural and content damage to the economic estimates of building damage, DDF employs a concept called damage percentage. Damage percentage is "a ratio of the total cost of replacement for damaged components of a property in a flooding event to the pre-disaster market value of the property" [37,41,42]. Damage percentage varies between 0 and 1, and here the cost of repairs and the market value of the building should be considered within the same time period.…”

Section: Benefits From Avoided Flooding Damagementioning

confidence: 99%

A Case Study of Preliminary Cost-Benefit Analysis of Building Levees to Mitigate the Joint Effects of Sea Level Rise and Storm Surge

Peng

Song

2018

Water

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Sea-level rise (SLR) will magnify the impacts of storm surge; the resulting severe flooding and inundation can cause huge damage to coastal communities. Community leaders are considering implementing adaptation strategies, typically hard engineering projects, to protect coastal assets and resources. It is important to understand the costs and benefits of the proposed project before any decision is made. To mitigate the flooding impact of joint effects of storm surge and SLR, building levee segments is chosen to be a corresponding adaptation strategy to protect the real estate assets in the study area-the City of Miami, FL, USA. This paper uses the classic Cost-Benefit Analysis (CBA) to assess the cost efficiency and proposes corresponding improvements in the benefit estimation, by estimating the avoided damages of implementing levee projects. Results show that the city will benefit from implementing levee projects along the Miami River in both a one-time 10 year storm event with SLR and cumulative long-term damage scenarios. This study also suggests that conducting CBA is a critical process before making coastal adaptation planning investment. A more meaningful result of cost effectiveness is estimated by accounting for the appreciation and time value. In addition, a sensitivity analysis is conducted to verify how the choice of discount rate influences the result. Uncertain factors including the rate of SLR, storm intensification, land use changes, and real estate appreciation are further analyzed.

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“…In this scenario, it is assumed that some data manipulation in Stage 2 is required before Stage 3; otherwise, only the Stage 1 and Stage 3 uncertainties are involved. Models developed to estimate monetary losses resulting from disasters can be found, for example, for the case of floods (Pistrika 2010;Pistrika et al 2014;Vozinaki et al 2015), for the case of earthquakes (Wu et al 2012;Jaiswal and Wald 2013), or for the case of hurricanes (Hallegatte 2008;Pan 2015;Smith and Matthews 2015).…”

Section: Credibility: All Stagesmentioning

confidence: 99%

A framework to assess quality and uncertainty in disaster loss data

Romão

Paupério

2016

Nat Hazards

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There is a growing interest in the systematic and consistent collection of disaster loss data for different applications. Therefore, the collected data must follow a set of technical requirements to guarantee its usefulness. One of those requirements is the availability of a measure of the uncertainty in the collected data to express its quality for a given purpose. Many of the existing disaster loss databases do not provide such uncertainty/quality measures due to the lack of a simple and consistent approach to express uncertainty. After reviewing existing literature on the subject, a framework to express the uncertainty in disaster loss data is proposed. This framework builds on an existing uncertainty classification that was updated and combined with an existing method for data characterization. The proposed approach is able to establish a global score that reflects the overall uncertainty in a certain loss indicator and provides a measure of its quality.

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Flood Depth-Damage Functions for Built Environment

Cited by 117 publications

References 20 publications

Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology

A Case Study of Preliminary Cost-Benefit Analysis of Building Levees to Mitigate the Joint Effects of Sea Level Rise and Storm Surge

A framework to assess quality and uncertainty in disaster loss data

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