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| Titel |
Real-time remote sensing driven river basin modeling using radar altimetry |
| VerfasserIn |
S. J. Pereira-Cardenal, N. D. Riegels, P. A. M. Berry, R. G. Smith, A. Yakovlev, T. U. Siegfried, 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 ; 15, no. 1 ; Nr. 15, no. 1 (2011-01-21), S.241-254 |
| Datensatznummer |
250012599
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| Publikation (Nr.) |
 copernicus.org/hess-15-241-2011.pdf |
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| Zusammenfassung |
Many river basins have a weak in-situ hydrometeorological monitoring
infrastructure. However, water resources practitioners depend on reliable
hydrological models for management purposes. Remote sensing (RS) data have
been recognized as an alternative to in-situ hydrometeorological data in
remote and poorly monitored areas and are increasingly used to force,
calibrate, and update hydrological models.
In this study, we evaluate the potential of informing a river basin model
with real-time radar altimetry measurements over reservoirs. We present a
lumped, conceptual, river basin water balance modeling approach based
entirely on RS and reanalysis data: precipitation was obtained from the
Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation
Analysis (TMPA), temperature from the European Centre for Medium-Range
Weather Forecast's (ECMWF) Operational Surface Analysis dataset and
reference evapotranspiration was derived from temperature data. The Ensemble
Kalman Filter was used to assimilate radar altimetry (ERS2 and Envisat)
measurements of reservoir water levels. The modeling approach was applied
to the Syr Darya River Basin, a snowmelt-dominated basin with large
topographical variability, several large reservoirs and scarce
hydrometeorological data that is located in Central Asia and shared between
4 countries with conflicting water management interests.
The modeling approach was tested over a historical period for which in-situ
reservoir water levels were available. Assimilation of radar altimetry data
significantly improved the performance of the hydrological model. Without
assimilation of radar altimetry data, model performance was limited,
probably because of the size and complexity of the model domain,
simplifications inherent in model design, and the uncertainty of RS and
reanalysis data. Altimetry data assimilation reduced the mean absolute error
of the simulated reservoir water levels from 4.7 to 1.9 m, and overall
model RMSE from 10.3 m to 6.7 m. Model performance was variable for the
different reservoirs in the system. The RMSE ranged from 10% to 76% of
the mean seasonal reservoir level variation.
Because of its easy accessibility and immediate availability, radar
altimetry lends itself to being used in real-time hydrological applications.
As an impartial source of information about the hydrological system that can
be updated in real time, the modeling approach described here can provide
useful medium-term hydrological forecasts to be used in water resources
management. |
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