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| Titel |
Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China |
| VerfasserIn |
X. Han, H.-J. H. Franssen, R. Rosolem, R. Jin, X. Li, H. Vereecken |
| 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. 1 ; Nr. 19, no. 1 (2015-01-30), S.615-629 |
| Datensatznummer |
250120612
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| Publikation (Nr.) |
 copernicus.org/hess-19-615-2015.pdf |
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| Zusammenfassung |
| The recent development of the non-invasive cosmic-ray soil moisture sensing
technique fills the gap between point-scale soil moisture measurements and
regional-scale soil moisture measurements by remote sensing. A cosmic-ray
probe measures soil moisture for a footprint with a diameter of
~ 600 m (at sea level) and with an effective measurement depth
between 12 and 76 cm, depending on the soil humidity. In this study, it
was tested whether neutron counts also allow correcting for a systematic
error in the model forcings. A lack of water management data often causes
systematic input errors to land surface models. Here, the assimilation
procedure was tested for an irrigated corn field (Heihe Watershed Allied
Telemetry Experimental Research – HiWATER, 2012) where no irrigation data
were available as model input although for the area a significant amount of
water was irrigated. In the study, the measured cosmic-ray neutron counts
and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface
temperature (LST) products were jointly assimilated into the Community Land
Model (CLM) with the local ensemble transform Kalman filter. Different data
assimilation scenarios were evaluated, with assimilation of LST and/or
cosmic-ray neutron counts, and possibly parameter estimation of leaf area
index (LAI). The results show that the direct assimilation of cosmic-ray
neutron counts can improve the soil moisture and evapotranspiration (ET)
estimation significantly, correcting for lack of information on irrigation
amounts. The joint assimilation of neutron counts and LST could improve
further the ET estimation, but the information content of neutron counts
exceeded the one of LST. Additional improvement was achieved by calibrating
LAI, which after calibration was also closer to independent field
measurements. It was concluded that assimilation of neutron counts was
useful for ET and soil moisture estimation even if the model has a
systematic bias like neglecting irrigation. However, also the assimilation
of LST helped to correct the systematic model bias introduced by neglecting
irrigation and LST could be used to update soil moisture with state
augmentation. |
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