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| Titel |
The suitability of remotely sensed soil moisture for improving operational flood forecasting |
| VerfasserIn |
N. Wanders, D. Karssenberg, A. Roo, S. M. Jong, M. F. P. Bierkens |
| 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. 6 ; Nr. 18, no. 6 (2014-06-24), S.2343-2357 |
| Datensatznummer |
250120393
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| Publikation (Nr.) |
 copernicus.org/hess-18-2343-2014.pdf |
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| Zusammenfassung |
We evaluate the added value of assimilated remotely sensed soil moisture for
the European Flood Awareness System (EFAS) and its potential to improve the
prediction of the timing and height of the flood peak and low flows. EFAS is
an operational flood forecasting system for Europe and uses a distributed
hydrological model (LISFLOOD) for flood predictions with lead times of up to
10 days. For this study, satellite-derived soil moisture from ASCAT
(Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer -
Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is
assimilated into the LISFLOOD model for the Upper Danube Basin and results
are compared to assimilation of discharge observations only. To assimilate
soil moisture and discharge data into the hydrological model, an ensemble
Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation
of the errors in the satellite products, is included to ensure increased
performance of the EnKF. For the validation, additional discharge
observations not used in the EnKF are used as an independent validation data
set.
Our results show that the accuracy of flood forecasts is increased when more
discharge observations are assimilated; the mean absolute error (MAE) of the
ensemble mean is reduced by 35%. The additional inclusion of satellite
data results in a further increase of the performance: forecasts of baseflows
are better and the uncertainty in the overall discharge is reduced, shown by
a 10% reduction in the MAE. In addition, floods are predicted with a
higher accuracy and the continuous ranked probability score (CRPS) shows a
performance increase of 5–10% on average, compared to assimilation of
discharge only. When soil moisture data is used, the timing errors in the
flood predictions are decreased especially for shorter lead times and
imminent floods can be forecasted with more skill. The number of false flood
alerts is reduced when more observational data is assimilated into the
system. The added values of the satellite data is largest when these
observations are assimilated in combination with distributed discharge
observations. These results show the potential of remotely sensed soil
moisture observations to improve near-real time flood forecasting in large
catchments. |
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