The objective of this study is to demonstrate the ability of riverstage tomography to estimate 2-D spatial distribution of hydraulic diffusivity (D) of Zhuoshui River alluvial fan, Taiwan, using groundwater level data from 65 wells and stream stage data from 5 gauging stations. In order to accomplish this objective, wavelet analysis is first conducted to investigate the temporal characteristics of groundwater level, precipitation, and stream stage. The results of the analysis show that variations of groundwater level and stream stage are highly correlated over seasonal and annual periods while that between precipitation is less significant. Subsequently, spatial cross-correlation between seasonal variations of groundwater level and riverstage data is analyzed. It is found that the correlation contour map reflects the pattern of sediment distribution of the fan. This finding is further substantiated by the cross-correlation analysis using both noisy and noise-free groundwater and riverstage data of a synthetic aquifer, where aquifer heterogeneity is known exactly. The ability of riverstage tomography is then tested with these synthetic data sets to estimate D distribution. Finally, the riverstage tomography is applied to the alluvial fan. The results of the application reveal that the apex and southeast of the alluvial fan are regions with relatively high D and the D values gradually decrease toward the shoreline of the fan. In addition, D at northern alluvial fan is slightly larger than that at southern. These findings are consistent with the geologic evolution of this alluvial fan. (C) 2017 Elsevier B.V. All rights reserved.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/623615 |
| Date | 04 1900 |
| Creators | Wang, Yu-Li, Yeh, Tian-Chyi Jim, Wen, Jet-Chau, Huang, Shao-Yang, Zha, Yuanyuan, Tsai, Jui-Pin, Hao, Yonghong, Liang, Yue |
| Contributors | Univ Arizona, Dept Hydrol & Atmospher Sci |
| Publisher | ELSEVIER SCIENCE BV |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2017 Elsevier B.V. All rights reserved. |
| Relation | http://linkinghub.elsevier.com/retrieve/pii/S0022169417301117 |
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