Articles | Volume 15, issue 11
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011
Hydrol. Earth Syst. Sci., 15, 3327–3341, 2011

Research article 04 Nov 2011

Research article | 04 Nov 2011

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

D. Brochero1,2, F. Anctil1, and C. Gagné2 D. Brochero et al.
  • 1Chaire de recherche EDS en prévisions et actions hydrologiques, Department of Civil Engineering and Water Engineering, Université Laval, Québec, G1V 0A6, Canada
  • 2Computer Vision and Systems Laboratory (CVSL), Department of Electrical Engineering and Computer Engineering, Université Laval, Québec, G1V 0A6, Canada

Abstract. An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This paper evaluates the generalization ability of a simplification scheme of a 800-member HEPS formed by the combination of 16 lumped rainfall-runoff models with the 50 perturbed members from the European Centre for Medium-range Weather Forecasts (ECMWF) EPS. Tests are made at two levels. At the local level, the transferability of the 9th day hydrological member selection for the other 8 forecast horizons exhibits an 82% success rate. The other evaluation is made at the regional or cluster level, the transferability from one catchment to another from within a cluster of watersheds also leads to a good performance (85% success rate), especially for forecast time horizons above 3 days and when the basins that formed the cluster presented themselves a good performance on an individual basis. Diversity, defined as hydrological model complementarity addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.