HR: 11:10h
AN: H32B-04    [Abstracts]
TI: HYDRAULIC TOMOGRAPHY USING TEMPORAL MOMENTS OF DRAWDOWN-RECOVERY DATA: LABORATORY SANDBOX STUDY
AU: * Yin, D
EM: danting-yin@uiowa,edu
AF: The University of Iowa, 423C IIHR-Hydroscience & Engineering, Iowa City, IA 52242, United States
AU: Illman, W A
EM: walter-illman@uiowa.edu
AF: The University of Iowa, 423C IIHR-Hydroscience & Engineering, Iowa City, IA 52242, United States
AU: Liu, X
EM: xiaoyi-liu@uiowa.edu
AF: The University of Iowa, 423C IIHR-Hydroscience & Engineering, Iowa City, IA 52242, United States
AU: Craig, A J
EM: ajcraig@engineering.uiowa.edu
AF: The University of Iowa, 423C IIHR-Hydroscience & Engineering, Iowa City, IA 52242, United States
AB: Hydraulic tomography is a viable technology that images the hydraulic heterogeneity of the subsurface. Unlike steady-state hydraulic tomography (SSHT), which provides estimates of hydraulic conductivity (K), transient hydraulic tomography (THT) can provide estimates of both K and specific storage (Ss) [Liu et al., 2007]. Effective as it may be, THT is a computationally demanding technique. To ease the computational burden, a transient hydraulic tomography which utilizes temporal moments (THT-m) of transient drawdown-recovery data has been developed by Zhu and Yeh [2006]. This procedure simplifies the governing equation from a diffusion equation to a Laplace equation in the corresponding numerical analysis. However, the calculation of the temporal moments, which involves integration in time of the drawdown-recovery data, may cause loss of information on the parameters that are being estimated by the inverse procedure. To test this conjecture, we conduct an investigation of the THT-m by comparing it to the THT through synthetic simulations. We then interpret a previously conducted hydraulic tomography tests in a laboratory sandbox using the THT-m approach and compare its performance to the results from THT previously conducted by Liu et al. [2007]. The laboratory tests were conducted in a synthetic aquifer created with a known heterogeneity pattern with forcing functions controlled. Our results show that the THT-m is able to estimate the K field well, but the estimation of Ss field is more difficult.
UR: http:www.iihr.uiowa.edu/~illman
DE: 1831 Groundwater quality
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: 2007 Joint Assembly