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| Titel |
Improving soil moisture profile reconstruction from ground-penetrating radar data: a maximum likelihood ensemble filter approach |
| VerfasserIn |
A. P. Tran, M. Vanclooster, S. Lambot |
| 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. 7 ; Nr. 17, no. 7 (2013-07-09), S.2543-2556 |
| Datensatznummer |
250018922
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| Publikation (Nr.) |
 copernicus.org/hess-17-2543-2013.pdf |
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| Zusammenfassung |
| The vertical profile of shallow unsaturated zone soil moisture plays a key
role in many hydro-meteorological and agricultural applications. We propose
a closed-loop data assimilation procedure based on the maximum likelihood
ensemble filter algorithm to update the vertical soil moisture profile from
time-lapse ground-penetrating radar (GPR) data. A hydrodynamic model is used
to propagate the system state in time and a radar electromagnetic model and
petrophysical relationships to link the state variable with the observation
data, which enables us to directly assimilate the GPR data. Instead of using
the surface soil moisture only, the approach allows to use the information of
the whole soil moisture profile for the assimilation. We validated our
approach through a synthetic study. We constructed a synthetic soil column with
a depth of 80 cm and analyzed the effects of the soil type on the
data assimilation by considering 3 soil types, namely, loamy sand, silt and
clay. The assimilation of GPR data was performed to solve the problem of
unknown initial conditions. The numerical soil moisture profiles generated by
the Hydrus-1D model were used by the GPR model to produce the "observed"
GPR data. The results show that the soil moisture profile obtained by
assimilating the GPR data is much better than that of an open-loop forecast.
Compared to the loamy sand and silt, the updated soil moisture profile of the
clay soil converges to the true state much more slowly. Decreasing the update
interval from 60 down to 10 h only slightly improves the effectiveness of
the GPR data assimilation for the loamy sand but significantly for the clay
soil. The proposed approach appears to be promising to improve real-time
prediction of the soil moisture profiles as well as to provide effective
estimates of the unsaturated hydraulic properties at the field scale from
time-lapse GPR measurements. |
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