Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

Pappenberger, Florian; Beven, Keith; Horritt, M. S.; Blažková, Šárka

doi:10.1016/j.jhydrol.2004.06.036

Cited by 406 publications

(353 citation statements)

References 56 publications

(83 reference statements)

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“…For example, the uncertainties caused by the roughness parameterisation for the a priori inundation simulations or the 1D model for the Rhine were not taken into account explicitly. A detailed study as e.g., performed in (Pappenberger et al, 2005a) would possibly lead to a more realistic assessment of the uncertainty caused by the Q-H-relation as presented here. The selection of the uncertainty sources was based on the expert judgement of the authors and thus subjective.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

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“…This problem of being unable to properly constrain the parameter space is closely linked to the equifinality problem, i.e. different 60 parameter sets yield similar model results with respect to the objective function used in the calibration (Beven and Binley, 1992;Aronica et al, 1998Aronica et al, , 2002Horritt and Bates, 2002;Pappenberger et al, 2005;Werner et al, 2005). The parameter sets that lead to the same optimal solution for the calibration problem are often defined as 'behavioural' parameter sets (Beven, 1996).…”

Section: Mapsmentioning

confidence: 99%

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Gobeyn

Wesemael

Neal

et al. 2017

Advances in Water Resources

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“…Although modelers have acknowledged the implications of roughness for a long time, it is not until recent years that any efforts have been made to see how much this type of uncertainty affects the results (see e.g. Pappenberger et al 2005;Werner et al 2005;Casas et al 2006;Schumann et al 2007;Wilson and Atkinson 2007;Brandt 2009;Warmink et al 2013;Wu in press). This is probably due to the type of uncertainty which earlier has been considered the main constraint for successful modeling, viz.…”

Section: Introductionmentioning

confidence: 99%

Modeling and visualizing uncertainties of flood boundary delineation: algorithm for slope and DEM resolution dependencies of 1D hydraulic models

Brandt

2016

Stoch Environ Res Risk Assess

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As flood inundation risk maps have become a central piece of information for both urban and risk management planning, also a need to assess the accuracies and uncertainties of these maps has emerged. Most maps show the inundation boundaries as crisp lines on visually appealing maps, whereby many planners and decision makers, among others, automatically believe the boundaries are both accurate and reliable. However, as this study shows, probably all such maps, even those that are based on high-resolution digital elevation models (DEMs), have immanent uncertainties which can be directly related to both DEM resolution and the steepness of terrain slopes perpendicular to the river flow direction. Based on a number of degenerated DEMs, covering areas along the Eskilstuna River, Sweden, these uncertainties have been quantified into an empirically-derived disparity distance equation, yielding values of distance between true and modeled inundation boundary location. Using the inundation polygon, the DEM, a value representing the DEM resolution, and the desired level of confidence as inputs in a new-developed algorithm that utilizes the disparity distance equation, the slope and DEM dependent uncertainties can be directly visualized on a map. The implications of this strategy should benefit planning and help reduce high costs of floods where infrastructure, etc., have been placed in flood-prone areas without enough consideration of map uncertainties.

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Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations

Cited by 406 publications

References 56 publications

Quantification of uncertainties in flood risk assessments

Quantification of uncertainties in flood risk assessments

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

Modeling and visualizing uncertainties of flood boundary delineation: algorithm for slope and DEM resolution dependencies of 1D hydraulic models

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