Articles | Volume 26, issue 4
https://doi.org/10.5194/hess-26-1001-2022
https://doi.org/10.5194/hess-26-1001-2022
Research article
 | 
22 Feb 2022
Research article |  | 22 Feb 2022

Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II

Jing Xu, François Anctil, and Marie-Amélie Boucher

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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, Mon. Weather Rev., 141, 3462–3476, https://doi.org/10.1175/MWR-D-12-00206.1, 2013. a
Abaza, M., Anctil, F., Fortin, V., and Perreault, L.: Hydrological Evaluation of the Canadian Meteorological Ensemble Reforecast Product, Atmos. Ocean., 55, 195–211, https://doi.org/10.1080/07055900.2017.1341384, 2017. a
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, 1–19, https://doi.org/10.1029/2005WR004745, 2007. a, b
Bergström, S. and Forsman, A.: Development of a conceptual deterministic rainfall-runoff model, Nord. Hydrol., 4, 147–170, https://doi.org/10.2166/nh.1973.0012, 1973. a
Beven, K. and Binley, A.: GLUE: 20 years on, Hydrol. Process., 28, 5897–5918, https://doi.org/10.1002/hyp.10082, 2014. a
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Short summary
The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
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