Flood risk assessment and associated uncertainty

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

doi:10.5194/nhess-4-295-2004

Cited by 444 publications

(377 citation statements)

References 12 publications

(13 reference statements)

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“…Detailed information about the existing modules can be found in Apel et al (2004Apel et al ( , 2006Apel et al ( , 2008.…”

Section: Vulnerability 2 Damage Estimationmentioning

confidence: 99%

“…These correlations were used to drawn randomly correlated peak discharges for Ruhr and Lippe for each peak discharge in the Rhine (cf. Apel et al, 2004).…”

Section: Tributary Inflowmentioning

confidence: 99%

“…Apel et al (2004Apel et al ( , 2006 proposed a dynamic-probabilistic modelling system for the calculation of flood risks of a complete river reach. The system is comprised of simple but computational efficient modules each representing a link in the flood process chain.…”

Section: Investigation Areamentioning

confidence: 99%

“…The flood risk assessment approach presented here is based on the works of Apel et al (2004Apel et al ( , 2006, who presented a dynamic probabilistic model system to assess flood risks and associated uncertainties. However, the present work presents some significant changes to the prior model versions.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of uncertainties in flood risk assessments

Apel¹,

Merz²,

Thieken³

2008

International Journal of River Basin Management

167

165

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By a common definition, flood risk assessments are comprised of two parts: a hazard and vulnerability assessment. The hazard assessment investigates the extent and magnitude of usually large flood events, which are associated to a certain exceedance probability, whereas the vulnerability part assesses the impact of the flooding on specified targets, e.g., building, people or infrastructure. Being inherently speculative flood risk assessments should always be accompanied by an uncertainty assessment in order to assist consequent decision properly. In this paper a dynamic-probabilistic method is proposed, which enables a cumulated flood risk assessment of a complete river reach considering dike failures at all dike locations. The model uses simple but computational efficient modules to simulate the complete process chain of flooding. These modules are embedded into a Monte Carlo framework thus enabling a risk assessment which is physically based thus mapping the real flooding process, and which is also probabilistic and not based on scenarios. The model also provides uncertainty estimates by quantifying various epistemic uncertainty sources of the hazard as well as the vulnerability part in a second layer of Monte Carlo simulations. These uncertainty estimates are associated to defined return intervals of the model outputs, i.e., the derived flood frequencies at the end of the reach and the risk curves for the complete reach, thus providing valuable information for the interpretation of the results. By separating single uncertainty sources a comparison of the contribution of different uncertainty sources to the overall predictive uncertainty in terms of derived flood frequencies and monetary risks could be performed. This revealed that the major uncertainties are extreme value statistics, resp. the length of the data series used and the discharge-stage relation used for the transformation of discharge into water levels in the river.

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“…Detailed information about the existing modules can be found in Apel et al (2004Apel et al ( , 2006Apel et al ( , 2008.…”

Section: Vulnerability 2 Damage Estimationmentioning

confidence: 99%

“…These correlations were used to drawn randomly correlated peak discharges for Ruhr and Lippe for each peak discharge in the Rhine (cf. Apel et al, 2004).…”

Section: Tributary Inflowmentioning

confidence: 99%

Section: Investigation Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantification of uncertainties in flood risk assessments

Apel¹,

Merz²,

Thieken³

2008

International Journal of River Basin Management

167

165

View full text Add to dashboard Cite

show abstract

“…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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Charting unknown waters—On the role of surprise in flood risk assessment and management

Merz

Vorogushyn

Lall

et al. 2015

Water Resources Research

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111

119

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Unexpected incidents, failures, and disasters are abundant in the history of flooding events. In this paper, we introduce the metaphors of terra incognita and terra maligna to illustrate unknown and wicked flood situations, respectively. We argue that surprise is a neglected element in flood risk assessment and management. Two sources of surprise are identified: (1) the complexity of flood risk systems, represented by nonlinearities, interdependencies, and nonstationarities and (2) cognitive biases in human perception and decision making. Flood risk assessment and management are particularly prone to cognitive biases due to the rarity and uniqueness of extremes, and the nature of human risk perception. We reflect on possible approaches to better understanding and reducing the potential for surprise and its adverse consequences which may be supported by conceptually charting maps that separate terra incognita from terra cognita, and terra maligna from terra benigna. We conclude that flood risk assessment and management should account for the potential for surprise and devastating consequences which will require a shift in thinking.

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Flood risk assessment and associated uncertainty

Cited by 444 publications

References 12 publications

Quantification of uncertainties in flood risk assessments

Quantification of uncertainties in flood risk assessments

Flood risk curves and uncertainty bounds

Charting unknown waters—On the role of surprise in flood risk assessment and management

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