Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 20, 1809-1825, 2016
https://doi.org/10.5194/hess-20-1809-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
10 May 2016
Accounting for three sources of uncertainty in ensemble hydrological forecasting
Antoine Thiboult1, François Anctil1, and Marie-Amélie Boucher2 1Dept. of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Québec, Canada
2Dept. of Applied Sciences, Université du Québec à Chicoutimi, 555, boulevard de l'Université, Chicoutimi, Canada
Abstract. Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

Citation: Thiboult, A., Anctil, F., and Boucher, M.-A.: Accounting for three sources of uncertainty in ensemble hydrological forecasting, Hydrol. Earth Syst. Sci., 20, 1809-1825, https://doi.org/10.5194/hess-20-1809-2016, 2016.
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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.
Issuing a good hydrological forecast is challenging because of the numerous sources of...
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