Author
Wang, Yu-LiYeh, Tian-Chyi Jim
Xu, Dong
Li, Kuangjia
Wen, Jet-Chau
Huang, Shao-Yang
Wang, Wenke
Hao, Yonghong
Affiliation
Department of Hydrology and Atmospheric Sciences, University of ArizonaIssue Date
2021-02-27
Metadata
Show full item recordPublisher
Elsevier B.V.Citation
Wang, Y. L., Yeh, T. C. J., Xu, D., Li, K., Wen, J. C., Huang, S. Y., ... & Hao, Y. (2021). Stochastic Analysis of Oscillatory Hydraulic Tomography. Journal of Hydrology, 126105.Journal
Journal of HydrologyRights
© 2021 Elsevier B.V. All rights reserved.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
This paper investigates the effectiveness of different oscillatory hydraulic tomography (OHT) frequencies for estimating heterogeneous fields. The analysis first formulates the effects of uncharacterized aquifer responses, T, and S fields as the ensemble mean residual flux and residual storage terms in the stochastic unconditional and conditional mean equations. These terms persist unless the T, S, or head fields are specified everywhere. We then conducted OHT to estimate the T and S fields using Monte Carlo experiments. The experiments show that using the heads in response to periodic pumping with different frequencies or multifrequency, the estimates' performance metrics vary from one realization to others. The mean performance metrics over many realizations are, however, indistinguishable, despite the frequency. We attribute the variation in the performance metrics to the lack of parameter or state variable ergodicity. Lastly, we emphasize the importance of dense monitoring networks and a cost-effective data collection strategy to improve the resolution of characterizing aquifer heterogeneity. © 2021 Elsevier B.V.Note
24 month embargo; first published online 27 February 2021ISSN
0022-1694Version
Final accepted manuscriptSponsors
CRDF Globalae974a485f413a2113503eed53cd6c53
10.1016/j.jhydrol.2021.126105
