Event generation for probabilistic flood risk modelling: Multi-site peak flow dependence model vs. weather-generator-based approach

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Winter, B.; Schneeberger, K.; Förster, K.; Vorogushyn, S.: Event generation for probabilistic flood risk modelling: Multi-site peak flow dependence model vs. weather-generator-based approach. In: Natural Hazards and Earth System Sciences 20 (2020), Nr. 6, S. 1689-1703. DOI: https://doi.org/10.5194/nhess-20-1689-2020

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Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area. © Author(s) 2020.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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1 image of flag of Germany Germany 9 81.82%
2 image of flag of Hungary Hungary 2 18.18%

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