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| Titel |
Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations. |
| VerfasserIn |
Patricia López López, Niko Wanders, Edwin Sutanudjaja, Luigi Renzullo, Geert Sterk, Jaap Schellekens, Marc Bierkens |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250102840
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| Publikation (Nr.) |
 EGU/EGU2015-8627.pdf |
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| Zusammenfassung |
| The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits
their ability to resolve key water balance processes for many river basins and thus
compromises their suitability for water resources management, especially when compared
to locally-tunes river models. A possible solution to the problem may be to drive
the coarse resolution models with high-resolution meteorological data as well as
to assimilate ground-based and remotely-sensed observations of key water cycle
variables. While this would improve the modelling resolution of the global model,
the impact of prediction accuracy remains largely an open question. In this study
we investigated the impact that assimilating streamflow and satellite soil moisture
observations have on global hydrological model estimation, driven by coarse- and
high-resolution meteorological observations, for the Murrumbidgee river basin in
Australia.
The PCR-GLOBWB global hydrological model is forced with downscaled global
climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH
Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets
(0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite
derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E
and streamflow observations collected from 25 gauging stations are assimilated
using an ensemble Kalman filter. Several scenarios are analysed to explore the
added value of data assimilation considering both local and global climatological
data.
Results show that the assimilation of streamflow observations result in the largest
improvement of the model estimates. The joint assimilation of both streamflow and
downscaled soil moisture observations leads to further improved in streamflow
simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000
km2).
Results also show that the added contribution of data assimilation, for both soil moisture
and streamflow, is more pronounced when the global meteorological data are used to force
the models. This is caused by the higher uncertainty and coarser resolution of the global
forcing.
This study demonstrates that it is possible to improve hydrological simulations forced by
coarse resolution meteorological data with downscaled satellite soil moisture and streamflow
observations and bring them closer to a hydrological model forced with local climatological
data. These findings are important in light of the efforts that are currently done to go to global
hyper-resolution modelling and can significantly help to advance this research. |
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