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| Titel |
Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin |
| VerfasserIn |
N. Tangdamrongsub, S. C. Steele-Dunne, B. C. Gunter, P. G. Ditmar, A. H. Weerts |
| 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 ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-29), S.2079-2100 |
| Datensatznummer |
250120699
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| Publikation (Nr.) |
 copernicus.org/hess-19-2079-2015.pdf |
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| Zusammenfassung |
| The ability to estimate terrestrial water storage (TWS) realistically is
essential for understanding past hydrological events and predicting future
changes in the hydrological cycle. Inadequacies in model physics,
uncertainty in model land parameters, and uncertainties in meteorological
data commonly limit the accuracy of hydrological models in simulating TWS.
In an effort to improve model performance, this study investigated the
benefits of assimilating TWS estimates derived from the Gravity Recovery and
Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model
using an ensemble Kalman filter (EnKF) approach. The study area
chosen was the Rhine River basin, which has both well-calibrated model
parameters and high-quality forcing data that were used for experimentation
and comparison. Four different case studies were examined which were
designed to evaluate different levels of forcing data quality and resolution
including those typical of other less well-monitored river basins. The
results were validated using in situ groundwater (GW) and stream gauge data. The
analysis showed a noticeable improvement in GW estimates when GRACE
data were assimilated, with a best-case improvement of correlation
coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to
the reference (ensemble open-loop) case. For the data-sparse case, the
best-case GW estimates increased the correlation coefficient from
0.46 to 0.61 and decreased the RMSE by 35%. For the average
improvement of GW estimates (for all four cases), the correlation
coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%.
Only a slight overall improvement was observed in streamflow estimates
when GRACE data were assimilated. Further analysis suggested that this is
likely due to sporadic short-term, but sizeable, errors in the forcing data
and the lack of sufficient constraints on the soil moisture component.
Overall, the results highlight the benefit of assimilating GRACE data into
hydrological models, particularly in data-sparse regions, while also
providing insight on future refinements of the methodology. |
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