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| Titel |
Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations |
| VerfasserIn |
G. Schumann, D. J. Lunt, P. J. Valdes, R. A. M. Jeu, K. Scipal, P. D. Bates |
| 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 ; 13, no. 9 ; Nr. 13, no. 9 (2009-09-01), S.1545-1553 |
| Datensatznummer |
250011987
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| Publikation (Nr.) |
 copernicus.org/hess-13-1545-2009.pdf |
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| Zusammenfassung |
| We demonstrate that global satellite products can be used to evaluate climate
model soil moisture predictions but conclusions should be drawn with care. The
quality of a limited area climate model (LAM) was compared to a general
circulation model (GCM) using soil moisture data from two different Earth
observing satellites within a model validation scheme that copes with the
presence of uncertain data. Results showed that in the face of imperfect
models and data, it is difficult to investigate the quality of current land
surface schemes in simulating hydrology accurately. Nevertheless, a LAM provides,
in general, a better representation of spatial patterns and dynamics of soil
moisture compared to a GCM. However, in months when data uncertainty is higher, particularly in
colder months and in periods when vegetation cover is too dense (e.g. August
in the case of Western Europe), it is not possible to draw firm conclusions
about model acceptability. For periods of higher confidence in observation data, our work indicates
that a higher resolution LAM has more benefits to soil moisture prediction than are
due to the resolution alone and can be attributed to an overall enhanced representation
of precipitation relative to the GCM. Consequently, heterogeneity of rainfall patterns
is better represented in the LAM and thus adequate representation of wet and dry
periods leads to an improved acceptability of soil moisture (with respect to uncertain
satellite observations), particularly in spring and early summer. Our results suggest
that remote sensing, albeit with its inherent uncertainties, can be used to highlight which
model should be preferred and as a diagnostic tool to pinpoint regions where the
hydrological budget needs particular attention. |
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