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| Titel |
Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community |
| VerfasserIn |
K. J. Franz, T. S. Hogue |
| 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 (2011-11-15), S.3367-3382 |
| Datensatznummer |
250013019
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| Publikation (Nr.) |
 copernicus.org/hess-15-3367-2011.pdf |
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| Zusammenfassung |
| The hydrologic community is generally moving towards the use of
probabilistic estimates of streamflow, primarily through the implementation
of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation
methods, or multi-modeling platforms. However, evaluation of probabilistic
outputs has not necessarily kept pace with ensemble generation. Much of the
modeling community is still performing model evaluation using standard
deterministic measures, such as error, correlation, or bias, typically
applied to the ensemble mean or median. Probabilistic forecast verification
methods have been well developed, particularly in the atmospheric sciences,
yet few have been adopted for evaluating uncertainty estimates in hydrologic
model simulations. In the current paper, we overview existing probabilistic
forecast verification methods and apply the methods to evaluate and compare
model ensembles produced from two different parameter uncertainty estimation
methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the
Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated
for the National Weather Service SACramento Soil Moisture Accounting
(SAC-SMA) model for 12 forecast basins located in the Southeastern United
States. We evaluate the model ensembles using relevant metrics in the
following categories: distribution, correlation, accuracy, conditional
statistics, and categorical statistics. We show that the presented
probabilistic metrics are easily adapted to model simulation ensembles and
provide a robust analysis of model performance associated with parameter
uncertainty. Application of these methods requires no information in
addition to what is already available as part of traditional model
validation methodology and considers the entire ensemble or uncertainty
range in the approach. |
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