Regionalisation of asset values for risk analyses

Thieken, Annegret H.; Müller, Matthias; Kleist, L.; Seifert, Isabel; Borst, Dietmar; Werner, Ulrich

doi:10.5194/nhess-6-167-2006

Cited by 60 publications

(69 citation statements)

References 9 publications

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“…To estimate property values, the total asset value of all residential buildings in Cologne was taken from Kleist et al (2006). Since only the total sum was provided, the assets were disaggregated on the basis of the CORINE land cover data 2000 by means of a dasymetric mapping approach that is outlined in Thieken et al (2006). A damage ratio was determined for each inundated grid cell.…”

Section: Damage Estimationmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses: aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the City of Cologne, Germany. This flood risk assessment consists of three modules, (1) flood frequency analysis, (2) inundation estimation, and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.

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Section: Damage Estimationmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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“…The organization of the exposure data in a GIS has been found to significantly improved loss estimation speed and accuracy. Chen et al (2004) and Thieken et al (2006) elaborated the possible improvement in disaster loss analysis that can be obtained by the application of a dasymetric mapping approach in theory (more details on how this approach can be applied are provided in Fig. 8).…”

Section: Related Researchmentioning

confidence: 99%

“…Alahmadi et al (2013) produced a downscale population distribution of Riyadh, Saudi Arabia, using remote sensing data and ward-level census population data. According to Thieken et al (2006), there are four categories of methods for generating population dasymetric map using land cover data: the binary method, three-class method, limiting variable method and regression method. All of these methods consider the spatial distribution of the population to be determined by the land use type.…”

Section: Related Researchmentioning

confidence: 99%

“…-Selecting parameters for the model: land use/land cover (LULC) data are widely used as auxiliary data (parameters) for dasymetric data generation (Thieken et al, 2006;Jia et al, 2014). We also use a similar method to build the dasymetric model, in which the land use data are divided into 60 categories from Landsat TM images.…”

Section: Dasymetric Model For the Data Set Generationmentioning

confidence: 99%

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A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

Xu¹,

An²,

Nie³

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Improving earthquake disaster loss estimation speed and accuracy is one of the key factors in effective earthquake response and rescue. The presentation of exposure data by applying a dasymetric map approach has good potential for addressing this issue. With the support of 30 × 30 areal exposure data (population and building data in China), this paper presents a new earthquake disaster loss estimation method for emergency response situations. This method has two phases: a pre-earthquake phase and a co-earthquake phase. In the pre-earthquake phase, we precalculate the earthquake loss related to different seismic intensities and store them in a 30 × 30 grid format, which has several stages: determining the earthquake loss calculation factor, gridding damage probability matrices, calculating building damage and calculating human losses. Then, in the co-earthquake phase, there are two stages of estimating loss: generating a theoretical isoseismal map to depict the spatial distribution of the seismic intensity field; then, using the seismic intensity field to extract statistics of losses from the pre-calculated estimation data. Thus, the final loss estimation results are obtained. The method is validated by four actual earthquakes that occurred in China. The method not only significantly improves the speed and accuracy of loss estimation but also provides the spatial distribution of the losses, which will be effective in aiding earthquake emergency response and rescue. Additionally, related pre-calculated earthquake loss estimation data in China could serve to provide disaster risk analysis before earthquakes occur. Currently, the precalculated loss estimation data and the two-phase estimation method are used by the China Earthquake Administration.

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“…distributed to the respective land-use units. Thieken et al (2006) and Wünsch et al (2009) applied and tested various disaggregation approaches and analysed the influence of these disaggregation schemes on the uncertainty of flood damage estimations. They commended the application of a binary disaggregation approach when using CLC data.…”

Section: Exposurementioning

confidence: 99%

Drivers of flood risk change in residential areas

Elmer

Hoymann

Düthmann

et al. 2012

Nat. Hazards Earth Syst. Sci.

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Abstract. The observed increase of direct flood damage over the last decades may be caused by changes in the meteorological drivers of floods, or by changing land-use patterns and socio-economic developments. It is still widely unknown to which extent these factors will contribute to future flood risk changes.We survey the change of flood risk in terms of expected annual damage for residential buildings in the lower part of the Mulde River basin (Vereinigte Mulde) between 1990 and 2020 in 10-yr time steps based on measurements and model projections. For this purpose we consider the complete risk chain from climate impact via hydrological and hydraulic modelling to damage and risk estimation. We analyse what drives the changes in flood risk and quantify the contributions of these drivers: flood hazard change due to climate change, land-use change and changes in building values.

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Regionalisation of asset values for risk analyses

Cited by 60 publications

References 9 publications

Flood risk curves and uncertainty bounds

Flood risk curves and uncertainty bounds

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

Drivers of flood risk change in residential areas

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