Articles | Volume 21, issue 11
Technical note
08 Nov 2017
Technical note |  | 08 Nov 2017

Technical note: Combining quantile forecasts and predictive distributions of streamflows

Konrad Bogner, Katharina Liechti, and Massimiliano Zappa

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Cited articles

Abrahart, R. J. and See, L.: Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments, Hydrol. Earth Syst. Sci., 6, 655–670,, 2002.
Addor, N., Jaun, S., Fundel, F., and Zappa, M.: An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios, Hydrol. Earth Syst. Sci., 15, 2327–2347,, 2011.
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403,, 2007.
Baran, S.: Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components, Comput. Stat. Data An., 75, 227–238,, 2014.
Baran, S. and Lerch, S.: Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting, Q. J. Roy. Meteor. Soc., 141, 2289–2299,, 2015.
Short summary
The enhanced availability of many different weather prediction systems nowadays makes it very difficult for flood and water resource managers to choose the most reliable and accurate forecast. In order to circumvent this problem of choice, different approaches for combining this information have been applied at the Sihl River (CH) and the results have been verified. The outcome of this study highlights the importance of forecast combination in order to improve the quality of forecast systems.