Articles | Volume 20, issue 6
https://doi.org/10.5194/nhess-20-1689-2020
https://doi.org/10.5194/nhess-20-1689-2020
Research article
 | 
08 Jun 2020
Research article |  | 08 Jun 2020

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

Benjamin Winter, Klaus Schneeberger, Kristian Förster, and Sergiy Vorogushyn

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Cited articles

Achleitner, S., Schöber, J., Rinderer, M., Leonhardt, G., Schöberl, F., Kirnbauer, R., and Schönlaub, H.: Analyzing the operational performance of the hydrological models in an alpine flood forecasting system, J. Hydrol., 412–413, 90–100, https://doi.org/10.1016/j.jhydrol.2011.07.047, 2012. a
Achleitner, S., Huttenlau, M., Winter, B., Reiss, J., Plörer, M., and Hofer, M.: Temporal development of flood risk considering settlement dynamics and local flood protection measures on catchment scale: An Austrian case study, Int. J. River Basin Manage., 14, 273–285, https://doi.org/10.1080/15715124.2016.1167061, 2016. a
Andrieu, C., Freitas, N., Doucet, A., and Jordan, M.: An Introduction to MCMC for Machine Learning, Mach. Learn., 50, 5–43, https://doi.org/10.1023/A:1020281327116, 2003. a
Archfield, S. A., Pugliese, A., Castellarin, A., Skøien, J. O., and Kiang, J. E.: Topological and canonical kriging for design flood prediction in ungauged catchments: An improvement over a traditional regional regression approach?, Hydrol. Earth Syst. Sci., 17, 1575–1588, https://doi.org/10.5194/hess-17-1575-2013, 2013. a
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014. a, b
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In this paper two different methods to generate spatially coherent flood events for probabilistic flood risk modelling are compared: on the one hand, a semi-conditional multi-variate dependence model applied to discharge observations and, on the other hand, a continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator. The results of the two approaches are compared in terms of simulated spatial patterns and overall flood risk estimates.
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