Articles | Volume 20, issue 6
Nat. Hazards Earth Syst. Sci., 20, 1689–1703, 2020
Nat. Hazards Earth Syst. Sci., 20, 1689–1703, 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 et al.

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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,, 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,, 2016. a
Andrieu, C., Freitas, N., Doucet, A., and Jordan, M.: An Introduction to MCMC for Machine Learning, Mach. Learn., 50, 5–43,, 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,, 2013. a
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151,, 2014. a, b
Short summary
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.
Final-revised paper