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.

Related authors

An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1)
Kristian Förster, Florian Hanzer, Benjamin Winter, Thomas Marke, and Ulrich Strasser
Geosci. Model Dev., 9, 2315–2333,,, 2016
Short summary

Related subject area

Hydrological Hazards
Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling
Tigstu T. Dullo, George K. Darkwah, Sudershan Gangrade, Mario Morales-Hernández, M. Bulbul Sharif, Alfred J. Kalyanapu, Shih-Chieh Kao, Sheikh Ghafoor, and Moetasim Ashfaq
Nat. Hazards Earth Syst. Sci., 21, 1739–1757,,, 2021
Short summary
Integrated mapping of water-related disasters using the analytical hierarchy process under land use change and climate change issues in Laos
Sengphrachanh Phakonkham, So Kazama, and Daisuke Komori
Nat. Hazards Earth Syst. Sci., 21, 1551–1567,,, 2021
Short summary
Soil moisture and streamflow deficit anomaly index: an approach to quantify drought hazards by combining deficit and anomaly
Eklavyya Popat and Petra Döll
Nat. Hazards Earth Syst. Sci., 21, 1337–1354,,, 2021
Short summary
The uncertainty of flood frequency analyses in hydrodynamic model simulations
Xudong Zhou, Wenchao Ma, Wataru Echizenya, and Dai Yamazaki
Nat. Hazards Earth Syst. Sci., 21, 1071–1085,,, 2021
Short summary
Flood risk assessment of the European road network
Kees C. H. van Ginkel, Francesco Dottori, Lorenzo Alfieri, Luc Feyen, and Elco E. Koks
Nat. Hazards Earth Syst. Sci., 21, 1011–1027,,, 2021
Short summary

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