Geostatistical inverse modeling to characterize the transience of streambed hydraulic conductivity

JOURNAL OF HYDROLOGY(2023)

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摘要
Quantitative investigation of the interaction between surface water and groundwater relies on the determination of transience of streambed hydraulic conductivity (K-sb). The commonly used successive inversion approach (i.e., the inversion is repeated over successive time steps) of interpreting aquifer hydraulic response data belongs to different optimization windows consists of a sizable number of inversion sub-processes and tends to lose sharp changes in K-sb. This study treats the transience of a parameter in the time domain as the heterogeneity in 1D space domain and uses geostatistical inverse modeling to characterize such transience. The methodology is first tested through a group of ten synthetic cases with independently generated random transient patterns of K-sb. Results of these synthetic cases reveal the capability of geostatistical inverse modeling to effectively characterize the general varying pattern as well as sharp changes in K-sb. Subsequently, the methodology is applied to interpret the aquifer hydraulic response data measured at the Rhone River site, Town of Fully, Switzerland. A significant improvement in model calibration is observed when the methodology is compared to the successive inversion approach. The field investigation reveals that the transience of in-situ K-sb is closely related to stream stage variations. Particularly, multiple occurrences of sharp increases and decreases in K-sb appear when the stream stage lowers below and rises above the aquifer head. The driving mechanism for these sharp variation events is that fine-grained sediments within the streambed can be partly washed away when the aquifer water flows toward the river, and vice versa. The geostatistical approach has the potential to help investigate other physical or chemical processes in groundwater hydrology associated with transient changes in K-sb.
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关键词
Streambed,Hydraulic conductivity,Transience,Geostatistical inverse modeling,Synthetic cases,Field application
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