Sequential hydraulic tests for transient and highly permeable unconfined aquifer systems – model development and field-scale implementation

Abstract. The transient hydraulic tomography survey (THTS) is a conceptually improved technique that efficiently estimates detailed variations in aquifer parameters. Based on the concept of the THTS, we developed a geostatistical inverse model to characterize saturated hydraulic conductivity (K) and the specific yield (Sy) in transient and unconfined aquifer systems. In this study, a synthetic example was first used to assess the accuracy of the developed inverse model. Multiple random K and Sy realizations with different variances of natural logarithm of K (lnK) were generated and systematically compared to evaluate the effects of joint inversion on K estimations. The model was implemented in field-scale, cross-hole injection tests in a shallow and highly permeable unconfined aquifer near the middle reaches of the Wu River in central Taiwan. To assess the effect of constant head boundary conditions on the estimation results, two additional modeling domains were evaluated on the basis of the same field data from the injection tests. The results of the synthetic example showed that the proposed inverse model can effectively reproduce the predefined K patterns and magnitudes. However, slightly less detail was obtained for the Sy field based on the sampling data from sequential transient hydraulic stresses. The joint inversion by using transient head observations could slightly decrease the accuracy of K estimations. The model implementation for field-scale injection tests showed that the model can estimate K and Sy fields with detailed spatial variations. Estimation results showed a relatively homogeneous aquifer for the tested well field. Results based on the three modeling domains showed similar patterns and magnitudes of K and Sy near the well locations. These results indicated that the THTS is relatively insensitive to artificially drawn boundary conditions even under transient conditions.