The Effect of Damage Functions on Urban Flood Damage Appraisal

Notaro, Vincenza; Marchis, Mauro De; Fontanazza, Chiara Maria; Loggia, Goffredo La; Puleo, Valeria; Freni, Gabriele

doi:10.1016/j.proeng.2014.02.138

Cited by 41 publications

(26 citation statements)

References 24 publications

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“…Any function can be a candidate for the damage function as long as it satisfies the above requirements. For example, the application of the flood damage function shown below uses three typical, but different, functions that were found through a literature survey: (1) quadratic function (Merz et al, 2010), (2) exponential function (Notaro et al, 2014) and (3) S-shape function (Totschnig et al, 2011). All are considered appropriate for providing the damage ratio of flood affected crops and are simple enough to match uncertainties in other factors related to damage assessment such as estimated inundation depth and flood disaster statistics that are used to calibrate function parameters.…”

Section: Identifying Flood Damage Functionsmentioning

confidence: 99%

Establishing flood damage functions for agricultural crops using estimated inundation depth and flood disaster statistics in data-scarce regions

Nguyen

Ichikawa

Ishidaira

2017

Hydrological Research Letters

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Abstract:Flood damage functions form the core of flood risk assessment. This study proposes a method for establishing flood damage functions for agricultural crops in data-scarce regions. The method assumes that the flood damage ratio is a function of inundation depth only and utilizes inundation depth estimated from flood extent information and hydro dynamic simulations. The parameters of the damage functions are calibrated through the SCEUA method (Shuffled Complex Evolution method developed at The University of Arizona) so that the calculated flood damages match observations compiled in flood disaster statistics. The established three functions show good agreement with actual agricultural damages caused by a rainfall event in 2010 and are validated against another rainfall event in 2009. The results indicate that the established damage functions are capable of estimating flood damage at the district scale, while damage estimations at finer spatial resolution differ between the functions, suggesting that detailed statistical data need to be incorporated to reduce the estimation uncertainty at fine scales.

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Section: Identifying Flood Damage Functionsmentioning

confidence: 99%

Establishing flood damage functions for agricultural crops using estimated inundation depth and flood disaster statistics in data-scarce regions

Nguyen

Ichikawa

Ishidaira

2017

Hydrological Research Letters

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“…Floods are considered the most devastating and frequently occurring hazards in the world (IPCC, 2014;UNISDR, 2009). That is why it has attracted the attention of many researchers and organizations, trying to appraise and assess the risks involved in order to stem its excesses and guard against future consequences (Dutta et al, 2003(Dutta et al, , 2013Herath and Wang, 2009;Bormudoi et al, 2011;Mohammadi et al, 2014;Notaro et al, 2014). The general shift of focus from flood hazard control to flood risk assessment has propelled the active involvement and interest in flood impact assessment and economic damage estimation (Bubeck et al, 2011;Ke et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Thirdly, the economic analyses of the damages are done through either survey questionnaires after a particular flood events, insurance claim or construction costs. Fourthly, the stage-damage curve (also called vulnerability curve) is established; this is the widely accepted common method for estimating potential flood damage (Notaro et al, 2014). Stagedamage curve can be constructed using either empirical or synthetic methods.…”

Section: Introductionmentioning

confidence: 99%

“…polynomial, linear, square root, etc.) (Freni et al, 2010;Notaro et al, 2014). Lastly, the spatial layers of the hazards (inundation depths, for example), exposure (property, land use) and stage-damage function can be integrated in the Geographic Information Systems (GIS), normalized by the unit cost price of the exposure and the floor area to estimate the potential damages (Herath et al, 1999;Dutta and Herath, 2001) (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of flood damage estimation to spatial resolution

Komolafe

Herath

Avtar

2015

J Flood Risk Management

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Flood damage estimation is an important element in flood risk assessment; it is useful for mobilizing investment and developing policies for flood loss prevention since most flood risk reduction measures are based on cost benefit analysis. From a global or regional view‐point, it is necessary to understand relative impacts on economies of countries to mobilize international support and develop response strategies. This requires a unified approach to flood damage estimation across countries and over large spatial extents. Spatial resolutions used in the assessment have a direct impact on the estimation of potential flood heights as well as representation of property exposure and, consequently, the flood damage estimates. Using Ichinomiya river basin, Japan, as a case study, the sensitivity of spatial scale in estimating potential flood economic damages is investigated and approaches are delineated to reduce errors arising from coarse spatial resolutions. [Correction added on 5 May 2016, after first online publication: in the first sentence of the abstract the word “is” has been inserted between “it” and “useful”.]

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“…Although mathematical models have been much improved in recent years, like all procedures based on a conceptualized representation of the real drainage system behaviour, its predictions are affected by a degree of inherent uncertainty [9][10][11][12]. Moreover, flooding damage functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional laws [6,13]. As results flood damage evaluation is generally affected by a high level of uncertainty due to the accumulation of several uncertainty sources (e.g., intrinsic model uncertainty, uncertainty due to damage appraisal, etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of the best flood retrofitting scenario in an urban watershed by means of a Bayesian Decision Network

Notaro¹,

Fontanazza²,

Loggia³

et al. 2014

WIT Transactions on the Built Environment

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Urban resilience to floods can be defined as a city's capacity to avoid damage through the implementation of structural and non-structural measures, to reduce damage in the case of a flood that exceeds a desired threshold, to recover quickly to the same or an equivalent state, and to adapt to an uncertain future. To build flood resilience, planners need to identify and analyse risk, to understand the impacts of flooding, and how they cope with these impacts by means of innovative and adaptable strategies and measures. The number of possible retrofitting scenarios to cope with flooding problems in an urban watershed could be greatly increased by the combination of several stormwater management practices. Therefore, the present study aims to develop an expert system in the form of a Bayesian Decision Network (BDN) able to evaluate the efficiency of some possible urban flood retrofitting scenario by examining all significant water management variables and their inherent uncertainty. The methodology was applied to an urbanized area of the city of Palermo (Italy).

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The Effect of Damage Functions on Urban Flood Damage Appraisal

Cited by 41 publications

References 24 publications

Establishing flood damage functions for agricultural crops using estimated inundation depth and flood disaster statistics in data-scarce regions

Establishing flood damage functions for agricultural crops using estimated inundation depth and flood disaster statistics in data-scarce regions

Sensitivity of flood damage estimation to spatial resolution

Identification of the best flood retrofitting scenario in an urban watershed by means of a Bayesian Decision Network

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