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| Titel |
Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study |
| VerfasserIn |
C. I. Michailovsky, P. Bauer-Gottwein |
| 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. 3 ; Nr. 18, no. 3 (2014-03-12), S.997-1007 |
| Datensatznummer |
250120303
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| Publikation (Nr.) |
 copernicus.org/hess-18-997-2014.pdf |
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| Zusammenfassung |
River basin management can greatly benefit from short-term river discharge
predictions. In order to improve model produced discharge forecasts, data
assimilation allows for the integration of current observations of the
hydrological system to produce improved forecasts and reduce prediction
uncertainty. Data assimilation is widely used in operational applications to
update hydrological models with in situ discharge or level measurements. In
areas where timely access to in situ data is not possible, remote sensing
data products can be used in assimilation schemes.
While river discharge itself cannot be measured from space, radar altimetry
can track surface water level variations at crossing locations between the
satellite ground track and the river system called virtual stations (VS).
Use of radar altimetry versus traditional monitoring in operational settings
is complicated by the low temporal resolution of the data (between 10 and 35
days revisit time at a VS depending on the satellite) as well as the fact
that the location of the measurements is not necessarily at the point of
interest. However, combining radar altimetry from multiple VS with
hydrological models can help overcome these limitations.
In this study, a rainfall runoff model of the Zambezi River basin is built
using remote sensing data sets and used to drive a routing scheme coupled to
a simple floodplain model. The extended Kalman filter is used to update the
states in the routing model with data from 9 Envisat VS. Model fit was
improved through assimilation with the Nash–Sutcliffe model efficiencies
increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two
distinct watersheds, the initial NSE (Nash–Sutcliffe efficiency) being low at one outlet due to large
errors in the precipitation data set. However, model reliability was poor in
one watershed with only 58 and 44% of observations falling in the
90% confidence bounds, for the open loop and assimilation runs
respectively, pointing to problems with the simple approach used to
represent model error. |
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