Articles | Volume 20, issue 5
Hydrol. Earth Syst. Sci., 20, 1809–1825, 2016
https://doi.org/10.5194/hess-20-1809-2016
Hydrol. Earth Syst. Sci., 20, 1809–1825, 2016
https://doi.org/10.5194/hess-20-1809-2016

Research article 10 May 2016

Research article | 10 May 2016

Accounting for three sources of uncertainty in ensemble hydrological forecasting

Antoine Thiboult et al.

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Cited articles

Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting (vol 141, pg 3462, 2013), Mon. Weather Rev., 142, 2561–2562, https://doi.org/10.1175/mwr-d-14-00018.1, 2014.
Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: Exploration of sequential streamflow assimilation in snow dominated watersheds, Adv. Water Resour., 80, 79–89, https://doi.org/10.1016/j.advwatres.2015.03.011, 2015.
Ajami, N. K., Duan, Q., Gao, X., and Sorooshian, S.: Multimodel combination techniques for analysis of hydrological simulations: Application to Distributed Model Intercomparison Project results, J. Hydrometeorol., 7, 755–768, https://doi.org/10.1175/jhm519.1, 2006.
Ajami, N. K., Duan, Q. Y., 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, https://doi.org/10.1029/2005wr004745, 2007.
Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, https://doi.org/10.5194/hess-13-141-2009, 2009.
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Short summary
Issuing a good hydrological forecast is challenging because of the numerous sources of uncertainty that lay in the description of the hydrometeorological processes. Several modeling techniques are investigated in this paper to assess how they contribute to the forecast quality. It is shown that the best modeling approach uses several dissimilar techniques that each tackle one source of uncertainty.