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| Titel |
Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes |
| VerfasserIn |
C. Alvarez-Garreton, D. Ryu, A. W. Western, C.-H. Su, W. T. Crow, D. E. Robertson, C. Leahy |
| 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 (2015-04-09), S.1659-1676 |
| Datensatznummer |
250120676
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| Publikation (Nr.) |
 copernicus.org/hess-19-1659-2015.pdf |
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| Zusammenfassung |
Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil
water stores of rainfall-runoff models has shown skill in improving
streamflow prediction. In the case of large and sparsely monitored
catchments, SM-DA is a particularly attractive tool. Within this context, we
assimilate satellite soil moisture (SM) retrievals from the Advanced
Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT)
and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble
Kalman filter to improve operational flood prediction within a large
(> 40 000 km2) semi-arid catchment in Australia. We assess the importance
of accounting for channel routing and the spatial distribution of forcing
data by applying SM-DA to a lumped and a semi-distributed scheme of the
probability distributed model (PDM). Our scheme also accounts for model error
representation by explicitly correcting bias in soil moisture and streamflow
in the ensemble generation process, and for seasonal biases and errors in the
satellite data.
Before assimilation, the semi-distributed model provided a more accurate
streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped
model (NSE = 0.67) at the catchment outlet. However, this did not ensure good
performance at the "ungauged" inner catchments (two of them with NSE below
0.3). After SM-DA, the streamflow ensemble prediction at the outlet was
improved in both the lumped and the semi-distributed schemes: the root mean
square error of the ensemble was reduced by 22 and 24%, respectively; the
false alarm ratio was reduced by 9% in both cases; the peak volume error was
reduced by 58 and 1%, respectively; the ensemble skill was improved
(evidenced by 12 and 13% reductions in the continuous ranked probability
scores, respectively); and the ensemble reliability was increased in both
cases (expressed by flatter rank histograms). SM-DA did not improve NSE.
Our findings imply that even when rainfall is the main driver of flooding in
semi-arid catchments, adequately processed satellite SM can be used to reduce
errors in the model soil moisture, which in turn provides better streamflow
ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the
spatial distribution in forcing data and routing processes are accounted for.
At ungauged locations, SM-DA is effective at improving some characteristics
of the streamflow ensemble prediction; however, the updated prediction is
still poor since SM-DA does not address the systematic errors found in the
model prior to assimilation. |
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