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| Titel |
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria |
| VerfasserIn |
D. Brochero, F. Anctil, C. Gagné |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 11 ; Nr. 15, no. 11 (2011-11-04), S.3307-3325 |
| Datensatznummer |
250013015
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| Publikation (Nr.) |
 copernicus.org/hess-15-3307-2011.pdf |
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| Zusammenfassung |
| Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing
rainfall-runoff models with Meteorological Ensemble Prediction Systems
(MEPS), have been recognized as useful approaches to quantify uncertainties
of hydrological forecasting systems. This task is complex both in terms of
the coupling of information and computational time, which may create an
operational barrier. The main objective of the current work is to assess the
degree of simplification (reduction of the number of hydrological members)
that can be achieved with a HEPS configured using 16 lumped hydrological
models driven by the 50 weather ensemble forecasts from the European Centre
for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection
(BGS) is proposed to assess the weight that each model must represent within
a subset that offers similar or better performance than a reference set of
800 hydrological members. These hydrological models' weights represent the
participation of each hydrological model within a simplified HEPS which would
issue real-time forecasts in a relatively short computational time. The
methodology uses a variation of the k-fold cross-validation, allowing an
optimal use of the information, and employs a multi-criterion framework that
represents the combination of resolution, reliability, consistency, and
diversity. Results show that the degree of reduction of members can be
established in terms of maximum number of members required (complexity of the
HEPS) or the maximization of the relationship between the different scores
(performance). |
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