Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-197-2022
https://doi.org/10.5194/hess-26-197-2022
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

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Cited articles

Abaza, M., Anctil, F., Fortin, V., and Perreault, L.: On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts, J. Hydrol., 555, 371–384, https://doi.org/10.1016/j.jhydrol.2017.10.038, 2017. a
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, https://doi.org/10.5194/hess-15-2327-2011, 2011. a
Alfieri, L., Pappenberger, F., Wetterhall, F., Haiden, T., Richardson, D., and Salamon, P.: Evaluation of ensemble streamflow predictions in Europe, J. Hydrol., 517, 913–922, https://doi.org/10.1016/j.jhydrol.2014.06.035, 2014. a
Aminyavari, S. and Saghafian, B.: Probabilistic streamflow forecast based on spatial post-processing of TIGGE precipitation forecasts, Stoch. Env. Res. Risk A., 33, 1939–1950, 2019. a
Anctil, F. and Ramos, M.-H.: Verification Metrics for Hydrological Ensemble Forecasts, in: Handbook of Hydrometeorological Ensemble Forecasting, edited by: Duan, Q., Pappenberger, F., Wood, A., and Cloke, H. L., and Schaake, J. C., Springer Berlin Heidelberg, 1–30, https://doi.org/10.1007/978-3-642-39925-1_3, 2019. a, b
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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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