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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Jul 2020

17 Jul 2020

Review status
This preprint is currently under review for the journal HESS.

Exploring hydrologic post-processing of ensemble stream flow forecasts based on Affine kernel dressing and Nondominated sorting genetic algorithm II

Jing Xu1, François Anctil1, and Marie-Amélie Boucher2 Jing Xu et al.
  • 1Department of Civil and Water Engineering, Université Laval, 1065 avenue de la Médecine, Québec, Québec, Canada
  • 2Department of Civil and Building Engineering, Université de Sherbrooke, 2500 Boul. de l’Université, Sherbrooke, Québec, Canada

Abstract. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical 5 system and to capture the uncertainties associated with different sources. However, space still exists for improving their predictive skill and credibility through proper hydrologic post-processing. We tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II). Those two methods are theoretically/technically distinct, yet however, share the same feature that both of them relax the parametric assumption of the underlying distribution of the data (i.e., streamflow ensemble forecast). AKD transformed ensemble and the Pareto fronts 10 generated with NSGA-II demonstrated the superiority of post-processed ensemble in efficiently eliminating forecast biases and maintaining a proper dispersion with the increasing forecasting horizon.

Jing Xu et al.

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Jing Xu et al.

Jing Xu et al.


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