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| Titel |
An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions |
| VerfasserIn |
M. He, T. S. Hogue, S. A. Margulis, K. J. Franz |
| 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 ; 16, no. 3 ; Nr. 16, no. 3 (2012-03-14), S.815-831 |
| Datensatznummer |
250013213
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| Publikation (Nr.) |
 copernicus.org/hess-16-815-2012.pdf |
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| Zusammenfassung |
| The current study proposes an integrated uncertainty and ensemble-based data
assimilation framework (ICEA) and evaluates its viability in providing
operational streamflow predictions via assimilating snow water equivalent
(SWE) data. This step-wise framework applies a parameter uncertainty
analysis algorithm (ISURF) to identify the uncertainty structure of
sensitive model parameters, which is subsequently formulated into an
Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow
prediction. The framework is coupled to the US National Weather Service
(NWS) snow and rainfall-runoff models. Its applicability is demonstrated for
an operational basin of a western River Forecast Center (RFC) of the NWS.
Performance of the framework is evaluated against existing operational
baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF.
Results indicate that the ensemble-mean prediction of ICEA considerably
outperforms predictions from the other three scenarios investigated,
particularly in the context of predicting high flows (top 5th
percentile). The ICEA streamflow ensemble predictions capture the
variability of the observed streamflow well, however the ensemble is not
wide enough to consistently contain the range of streamflow observations in
the study basin. Our findings indicate that the ICEA has the potential to
supplement the current operational (deterministic) forecasting method in
terms of providing improved single-valued (e.g., ensemble mean) streamflow
predictions as well as meaningful ensemble predictions. |
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