Quantification of uncertainties in flood risk assessments

Apel, Heiko; Merz, Bruno; Thieken, Annegret H.

doi:10.1080/15715124.2008.9635344

Cited by 167 publications

(188 citation statements)

References 17 publications

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“…stage-damage functions, as well as methodological differences in estimating the exposed asset values linked to these curves (Merz et al, 2004;Apel et al, 2008Apel et al, , 2009Freni et al, 2010;Merz et al, 2010;de Moel and Aerts, 2011). While further efforts are currently being undertaken to reduce the uncertainties of flood loss assessment, it can be assumed that considerable uncertainty will remain in the coming years (Jonkman et al, 2008;Maaskant et al, 2009;Elmer et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

How reliable are projections of future flood damage?

Bubeck

Moel

Bouwer

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood damage modelling is an important component in flood risk management, and several studies have investigated the possible range of flood damage in the coming decades. Generally, flood damage assessments are still characterized by considerable uncertainties in stage-damage functions and methodological differences in estimating exposed asset values. The high variance that is commonly associated with absolute flood damage assessments is the reason for the present study that investigates the reliability of estimates of relative changes in the development of potential flood damage. While studies that estimate (relative) changes in flood damage over time usually address uncertainties resulting from different projections (e.g. land-use characteristics), the influence of different flood damage modelling approaches on estimates of relative changes in the development of flood damage is largely unknown. In this paper, we evaluate the reliability of estimates of relative changes in flood damage along the river Rhine between 1990 and 2030 in terms of different flood-damage modelling approaches. The results show that relative estimates of flood damage developments differ by a factor of 1.4. These variations, which result from the application of different modelling approaches, are considerably smaller than differences between the approaches in terms of absolute damage estimates (by a factor of 3.5 to 3.8), or than differences resulting from land-use projections (by a factor of 3). The differences that exist when estimating relative changes principally depend on the differences in damage functions. In order to improve the reliability of relative estimates of changes in the development of potential flood damage, future research should focus on reducing the uncertainties related to damage functions.

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Section: Introductionmentioning

confidence: 99%

How reliable are projections of future flood damage?

Bubeck

Moel

Bouwer

et al. 2011

Nat. Hazards Earth Syst. Sci.

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“…Other studies discuss uncertainties related to the extreme value analysis (Wahl et al, 2017;Apel et al, 2008). Some coastal studies discuss the effect of DEM corrections for spatial variations in the mean sea level (Muis et al, 2017) and the inundation modelling approach (Vous- doukas et al, 2016;Breilh et al, 2013;Seenath et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

Vousdoukas

Bouziotas

Giardino

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policymaking, and harmonization of climate change adaptation strategies. There is, however, limited insight into the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the coastal flood risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea level (ESL), (ii) the underlying uncertainty in the digital elevation model (DEM), (iii) flood defence information, (iv) the assumptions behind the use of depth-damage functions that express vulnerability, and (v) different climate change projections. The impact of these uncertainties on estimated expected annual damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal, and on the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, and their absolute and relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large datasets with sufficient resolution and accuracy, the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

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“…Such assessments may be associated with significant uncertainty. For example, Merz et al (2004) quantified the uncertainty associated with flood damage estimates at the micro-scale, and Apel et al (2004Apel et al ( , 2008) assessed the uncertainty of flood risk estimates for a river reach. It is widely acknowledged that risk analyses should try to indicate the reliability of the risk quantification.…”

Section: Introductionmentioning

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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Quantification of uncertainties in flood risk assessments

Cited by 167 publications

References 17 publications

How reliable are projections of future flood damage?

How reliable are projections of future flood damage?

Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

Flood risk curves and uncertainty bounds

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