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| Titel |
Attribution of hydrologic forecast uncertainty within scalable forecast windows |
| VerfasserIn |
L. Yang, F. Tian, Y. Sun, X. Yuan, H. Hu |
| 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 ; 18, no. 2 ; Nr. 18, no. 2 (2014-02-26), S.775-786 |
| Datensatznummer |
250120290
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| Publikation (Nr.) |
 copernicus.org/hess-18-775-2014.pdf |
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| Zusammenfassung |
| Hindcasts based on the extended streamflow prediction (ESP) approach are
carried out in a typical rainfall-dominated basin in China, aiming to
examine the roles of initial conditions (IC), future atmospheric forcing
(FC) and hydrologic model uncertainty (MU) in streamflow forecast skill. The
combined effects of IC and FC are explored within the framework of a forecast
window. By implementing virtual numerical simulations without the
consideration of MU, it is found that the dominance of IC can last up to 90 days
in the dry season, while its impact gives way to FC for lead times exceeding
30 days in the wet season. The combined effects of IC and FC on the forecast
skill are further investigated by proposing a dimensionless parameter
(β) that represents the ratio of the total amount of initial water
storage and the incoming rainfall. The forecast skill increases
exponentially with β, and varies greatly in different forecast
windows. Moreover, the influence of MU on forecast skill is examined by
focusing on the uncertainty of model parameters. Two different hydrologic
model calibration strategies are carried out. The results indicate that the uncertainty
of model parameters exhibits a more significant influence on the forecast
skill in the dry season than in the wet season. The ESP approach is more
skillful in monthly streamflow forecast during the transition period from
wet to dry than otherwise. For the transition period from dry to wet, the
low skill of the forecasts could be attributed to the combined effects of
IC and FC, but less to the biases in the hydrologic model parameters. For the
forecasts in the dry season, the skill of the ESP approach is heavily
dependent on the strategy of the model calibration. |
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