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<jats:title>Abstract</jats:title><jats:p>Significant efforts have been expended for improved characterization of hydraulic conductivity (<jats:italic>K</jats:italic>) and specific storage (<jats:italic>S</jats:italic><jats:sub><jats:italic>s</jats:italic></jats:sub>) to better understand groundwater flow and contaminant transport processes. Conventional methods including grain size analyses (GSA), permeameter, slug and pumping tests have been utilized extensively, while Direct Push‐based Hydraulic Profiling Tool (HPT) surveys have been developed to obtain high‐resolution <jats:italic>K</jats:italic> estimates. Moreover, inverse modeling approaches based on geology‐based zonations, and highly parameterized Hydraulic Tomography (HT) have also been advanced to map spatial variations of <jats:italic>K</jats:italic> and <jats:italic>S</jats:italic><jats:sub><jats:italic>s</jats:italic></jats:sub> between and beyond boreholes. While different methods are available, it is unclear which one yields <jats:italic>K</jats:italic> estimates that are most useful for high resolution predictions of groundwater flow. Therefore, the main objective of this study is to evaluate various <jats:italic>K</jats:italic> estimates at a highly heterogeneous field site obtained with three categories of characterization techniques including: (1) conventional methods (GSA, permeameter and slug tests); (2) HPT surveys; and (3) inverse modeling based on geology‐based zonations and highly parameterized approaches. The performance of each approach is first qualitatively analyzed by comparing <jats:italic>K</jats:italic> estimates to site geology. Then, steady‐state and transient groundwater flow models are employed to quantitatively assess various <jats:italic>K</jats:italic> estimates by simulating pumping tests not used for parameter estimation. Results reveal that inverse modeling approaches yield the best drawdown predictions under both steady and transient conditions. In contrast, conventional methods and HPT surveys yield biased predictions. Based on our research, it appears that inverse modeling and data fusion are necessary steps in predicting accurate groundwater flow behavior.</jats:p><jats:p>This article is protected by copyright. All rights reserved.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Sub‐surface characterization in fractured aquifers is challenging due to the co‐existence of contrasting materials namely matrix and fractures. Transient hydraulic tomography (THT) is proved to be an efficient and robust technique to estimate hydraulic (<jats:italic>K</jats:italic><jats:sub><jats:italic>m</jats:italic></jats:sub>, <jats:italic>K</jats:italic><jats:sub><jats:italic>f</jats:italic></jats:sub>) and storage (<jats:italic>S</jats:italic><jats:sub><jats:italic>m</jats:italic></jats:sub>, <jats:italic>S</jats:italic><jats:sub><jats:italic>f</jats:italic></jats:sub>) properties in such complex hydrogeologic settings. However, performance of THT is governed by data quality and optimization technique used in inversion. We assessed the performance of gradient and gradient‐free optimizers with THT inversion. Laboratory experiments were performed on a two‐dimensional, granite rock (80 cm × 45 cm × 5 cm) with known fracture pattern. Cross‐hole pumping experiments were conducted at 10 ports (located on fractures), and time‐drawdown responses were monitored at 25 ports (located on matrix and fractures). Pumping ports were ranked based on weighted signal‐to‐noise ratio (SNR) computed at each observation port. Noise‐free, good quality (SNR &gt; 100) datasets were inverted using Levenberg–Marquardt: LM (gradient) and Nelder–Mead: NM (gradient‐free) methods. All simulations were performed using a coupled simulation‐optimization model. Performance of the two optimizers is evaluated by comparing model predictions with observations made at two validation ports that were not used in simulation. Both LM and NM algorithms have broadly captured the preferential flow paths (fracture network) via <jats:italic>K</jats:italic> and <jats:italic>S</jats:italic> tomograms, however LM has outperformed NM during validation (). Our results conclude that, while method of optimization has a trivial effect on model predictions, exclusion of low quality (SNR ≤ 100) datasets can significantly improve the model performance.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Assimilating recent observations improves model outcomes for real‐time assessments of groundwater processes. This is demonstrated in estimating time‐varying recharge to a shallow fractured‐rock aquifer in response to precipitation. Results from estimating the time‐varying water‐table altitude (<jats:italic>h</jats:italic>) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed‐lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data‐driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of <jats:italic>h</jats:italic>, filtering and FLS produce estimates of recharge that better represent time‐varying observations of <jats:italic>h</jats:italic> and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real‐time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. Results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.</jats:p><jats:p>This article is protected by copyright. All rights reserved.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The slug test has been commonly used to estimate aquifer parameters. Previous studies on the slug test mainly focused on a single‐layer aquifer. However, understanding the interaction between layers is particularly important when assessing aquifer parameters under certain circumstances. In this study, a new semi‐analytical model on transient flow in a three‐layered aquifer system with a partially penetrating well was developed for the slug test. The proposed model was solved using the Laplace transform method and the Goldstein‐Weber transform method, where the semi‐analytical solution for the model was obtained. The drawdowns of the proposed model were analyzed to understand the impacts of the different parameters on the drawdowns in a three‐layered aquifer system. The results indicated that groundwater interactions between the layers have a significant impact on the slug test. In addition, a shorter and deeper well screen as well as a greater permeability ratio between the layers creates a greater interface flow between them, leading to a higher drawdown in the slug test. Finally, a slug test in a three‐layered aquifer system was conducted in our laboratory to validate the new model, which indicated that the proposed model performed better in the interpretation of the experimental data than a previous model proposed by Hyder et al. (1994). We also proposed an empirical relationship to qualitatively identify the errors in the application of single‐layer model for the analysis of response data in a three‐layered aquifer system.</jats:p><jats:p>This article is protected by copyright. All rights reserved.</jats:p>