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| Titel |
Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation |
| VerfasserIn |
C. M. DeChant, H. Moradkhani |
| 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 (2011-11-16), S.3399-3410 |
| Datensatznummer |
250013021
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| Publikation (Nr.) |
 copernicus.org/hess-15-3399-2011.pdf |
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| Zusammenfassung |
| Within the National Weather Service River Forecast System, water supply
forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP
relies both on the estimation of initial conditions and historically
resampled forcing data to produce seasonal volumetric forecasts. In the
western US, the accuracy of initial condition estimation is particularly
important due to the large quantities of water stored in mountain snowpack.
In order to improve the estimation of snow quantities, this study explores
the use of ensemble data assimilation. Rather than relying entirely on the
model to create single deterministic initial snow water storage, as
currently implemented in operational forecasting, this study incorporates
SNOTEL data along with model predictions to create an ensemble based
probabilistic estimation of snow water storage. This creates a framework to
account for initial condition uncertainty in addition to forcing
uncertainty. The results presented in this study suggest that data
assimilation has the potential to improve ESP for probabilistic volumetric
forecasts but is limited by the available observations. |
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