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| Titel |
Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon |
| VerfasserIn |
R. C. D. Paiva, W. Collischonn, M.-P. Bonnet, L. G. G. Gonçalves, S. Calmant, A. Getirana, J. Santos da Silva |
| 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 ; 17, no. 7 ; Nr. 17, no. 7 (2013-07-24), S.2929-2946 |
| Datensatznummer |
250018946
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| Publikation (Nr.) |
 copernicus.org/hess-17-2929-2013.pdf |
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| Zusammenfassung |
| In this work, we introduce and evaluate a data assimilation framework for
gauged and radar altimetry-based discharge and water levels applied to a
large scale hydrologic-hydrodynamic model for stream flow forecasts over the
Amazon River basin. We used the process-based hydrological model called
MGB-IPH coupled with a river hydrodynamic module using a storage model for
floodplains. The Ensemble Kalman Filter technique was used to assimilate
information from hundreds of gauging and altimetry stations based on ENVISAT
satellite data. Model state variables errors were generated by corrupting
precipitation forcing, considering log-normally distributed, time and
spatially correlated errors. The EnKF performed well when assimilating in situ
discharge, by improving model estimates at the assimilation sites (change in
root-mean-squared error Δrms = −49%) and also transferring
information to ungauged rivers reaches (Δrms = −16%). Altimetry data
assimilation improves results, in terms of water levels (Δrms = −44%)
and discharges (Δrms = −15%) to a minor degree, mostly
close to altimetry sites and at a daily basis, even though radar altimetry
data has a low temporal resolution. Sensitivity tests highlighted the
importance of the magnitude of the precipitation errors and that of their
spatial correlation, while temporal correlation showed to be dispensable.
The deterioration of model performance at some unmonitored reaches indicates
the need for proper characterisation of model errors and spatial
localisation techniques for hydrological applications. Finally, we evaluated
stream flow forecasts for the Amazon basin based on initial conditions
produced by the data assimilation scheme and using the ensemble stream flow
prediction approach where the model is forced by past meteorological
forcings. The resulting forecasts agreed well with the observations and
maintained meaningful skill at large rivers even for long lead times, e.g.
>90 days at the Solimões/Amazon main stem. Results encourage
the potential of hydrological forecasts at large rivers and/or poorly
monitored regions by combining models and remote-sensing information. |
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