A major challenge in vadose zone hydrology is to obtain accurate information on the temporal changes of the vertical soil water distribution and its feedback with the atmosphere and groundwater. A state of the art coupled hydrogeophysical inversion scheme is applied to evaluate soil hydraulic properties of a synthetic model and a field soil in southern Ontario based on time-lapse monitoring of soil dynamics with surface ground-penetrating radar (GPR). Film flow was included in the hydrological model to account for noncapillary water flow in a sandy medium during dry conditions. The synthetic study illustrated that GPR data contain sufficient information to accurately constrain soil hydraulic parameters within a coupled inversion framework and led to an accurate estimation of the soil hydraulic properties. When film flow was not accounted for within the inversion, an equally good fit could still be achieved. In this case, errors introduced by neglecting film flow were compensated by different hydraulic parameters. For the field data, the coupled inversion reduced the overall misfit compared to an uncalibrated model using hydraulic parameters obtained from laboratory data. Although the data fit improved significantly for water content in the deeper soil layers, accounting for film flow in the uppermost subsurface layer did not lead to a better fit of the GPR data. Further research is needed to describe the processes controlling water content in the dry range, in particular coupled heat and vapor transport. This study illustrates the suitability of surface GPR measurements combined with coupled inversion for near-surface characterization of soil hydraulic parameters.
机构:
Tongji Univ, State Key Lab Marine Geol, Shanghai, Peoples R China
Penn State Univ, Dept Geosci, University Pk, PA 16801 USATongji Univ, State Key Lab Marine Geol, Shanghai, Peoples R China
Huang, Chao
Zhu, Tieyuan
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Penn State Univ, Dept Geosci, University Pk, PA 16801 USA
Penn State Univ, Energy Inst, Earth & Mineral Sci, University Pk, PA USATongji Univ, State Key Lab Marine Geol, Shanghai, Peoples R China
Zhu, Tieyuan
Xing, Guangchi
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Penn State Univ, Dept Geosci, University Pk, PA 16801 USATongji Univ, State Key Lab Marine Geol, Shanghai, Peoples R China
机构:
Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
KIGAM Korea Inst Geosci & Mineral Resources, Daejeon 34132, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
Kim, Sooyoon
Park, Jiho
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Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
KIGAM Korea Inst Geosci & Mineral Resources, Daejeon 34132, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
Park, Jiho
Seol, Soon Jee
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Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
Seol, Soon Jee
Byun, Joongmoo
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Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea