Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 1
Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 1
Research article
 | 
14 Jan 2022
Research article |  | 14 Jan 2022

Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems

Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos

Supplement

https://doi.org/10.5194/hess-26-197-2022-supplement

Data sets

The THORPEX interactive grand global ensemble (https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/) Philippe Bougeault, Zoltan Toth, Craig Bishop, Barbara Brown, David Burridge, De Hui Chen, Beth Ebert, Manuel Fuentes, Thomas M. Hamill, Ken Mylne, Jean Nicolau, Tiziana Paccagnella, Young-Youn Park, David Parsons, Baudouin Raoult, Doug Schuster, Pedro Silva Dias, Richard Swinbank, Yoshiaki Takeuchi, Warren Tennant, Laurence Wilson, and Steve Worley https://doi.org/10.1175/2010BAMS2853.1

Model code and software

HOOPLA Antoine Thiboult, Gregory Seiller, and François Anctil https://github.com/AntoineThiboult/HOOPLA

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Short summary
We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
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