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| Titel |
Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach |
| VerfasserIn |
J.-G. Liu, Z.-H. Xie |
| 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 ; 17, no. 9 ; Nr. 17, no. 9 (2013-09-03), S.3355-3369 |
| Datensatznummer |
250085920
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| Publikation (Nr.) |
 copernicus.org/hess-17-3355-2013.pdf |
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| Zusammenfassung |
| The quality of soil-moisture simulation using land surface models depends
largely on the accuracy of the meteorological forcing data. We investigated
how to reduce the uncertainty arising from meteorological forcings in a
simulation by adopting a multiple meteorological forcing ensemble approach.
Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland
China were conducted using four different meteorological forcings, and the
four sets of soil-moisture data related to the simulations were then merged
using simple arithmetical averaging and Bayesian model averaging (BMA)
ensemble approaches. BMA is a statistical post-processing procedure for
producing calibrated and sharp predictive probability density functions
(PDFs), which is a weighted average of PDFs centered on the bias-corrected
forecasts from a set of individual ensemble members based on their
probabilistic likelihood measures. Compared to in situ observations, the
four simulations captured the spatial and seasonal variations of soil
moisture in most cases with some mean bias. They performed differently when
simulating the seasonal phases in the annual cycle, the interannual
variation and the magnitude of observed soil moisture over different
subregions of mainland China, but no individual meteorological forcing
performed best for all subregions. The simple arithmetical average ensemble
product outperformed most, but not all, individual members over most of the
subregions. The BMA ensemble product performed better than simple
arithmetical averaging, and performed best for all fields over most of the
subregions. The BMA ensemble approach applied to the ensemble simulation
reproduced anomalies and seasonal variations in observed soil-moisture
values, and simulated the mean soil moisture. It is presented here as a
promising way for reproducing long-term, high-resolution spatial and
temporal soil-moisture data. |
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