Analyses of the PM-2 Aquifer Test Using Multiple Observation Wells

McLin, S.G.

doi:10.2172/842479

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…This may be explained by the early-time negative correlation between head and transmissivity (Wu et al, 2005) and/or the existence of high conductivity inter-well pathways as described by Herweijer (1996). Other possible explanations for time-dependent hydrogeologic parameters are well-bore storage and leakage effects known to exist at the site (McLin, 2005(McLin, , 2006a.…”

Section: Methodsmentioning

confidence: 99%

Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown

Harp,

Vesselinov

2011

Preprint

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It has been previously observed that during a pumping test in heterogeneous media, drawdown data from different time periods collected at a single location produce different estimates of aquifer properties and that Theis type-curve inferences are more variable than late-time Cooper-Jacob inferences. In order to obtain estimates of aquifer properties from highly transient drawdown data using the Theis solution, it is necessary to account for this behavior. We present an approach that utilizes an exponential functional form to represent Theis parameter behavior resulting from the spatial propagation of a cone of depression. This approach allows the use of transient data consisting of early-time drawdown data to obtain late-time convergent Theis parameters consistent with Cooper-Jacob method inferences. We demonstrate the approach on a multi-year dataset consisting of multi-well transient water-level observations due to transient multi-well water-supply pumping. Based on previous research, transmissivities associated with each of the pumping wells are required to converge to a single value, while storativities are allowed to converge to distinct values.

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Section: Methodsmentioning

confidence: 99%

Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown

Harp,

Vesselinov

2011

Preprint

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“…Another intermediate-scale single well pumping test method is referred to as the specific capacity method. The specific capacity method described in McLin (2005), which estimates lower bounds for hydraulic conductivities, is typically used in this dataset.…”

Section: Data Collectionmentioning

confidence: 99%

Developing Hydraulic Conductivity Distributions for Use in Hydrologic Modeling

Jordan,

Anderson,

Gains-Germain

et al. 2024

Preprint

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We present a methodology that uses pilot and anchor points with probability distributions for saturated hydraulic conductivity in a groundwater contaminant transport model. This approach directly links locations with calibration target data (e.g., water levels and drawdown at monitoring wells) to the most relevant physical parameter(s) that drive behavior, in a way that promotes model parsimony. Distributions for hydraulic conductivity are developed for monitoring well locations with pumping tests in order to reflect the state of uncertainty in the local estimates; these locations are called anchor points. Pilot points are placed between monitoring wells, and because they have more uncertainty these are generally assigned wider distributions that reflect plausible hydraulic conductivity values for the geologic material in which they are located. Scaling issues are considered in the development of these distributions. Pilot points are not randomly or uniformly distributed in the domain; rather they are considered connectors between locations with data (anchor points) and placed strategically between them. For a given model realization, hydraulic conductivity values at both pilot and anchor points are sampled from their respective distributions and all remaining locations are derived using an interpolation scheme (e.g., kriging). This approach to hydraulic conductivity assignment honors location-specific data, geologic heterogeneity, and spatial patterns. Given that inverse analysis of high-dimensional models tends to be ill-posed and thus sensitive to initialization of parameters, the distribution development process plays a critical role in driving the outcome of model calibration.

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