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[1] Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new ''value of improved information'' (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.

Field characterization of a trichloroethene (TCE) source area in fractured mudstones produced a detailed understanding of the geology, contaminant distribution in fractures and the rock matrix, and hydraulic and transport properties. Groundwater flow and chemical transport modeling that synthesized the field characterization information proved critical for designing bioremediation of the source area. The planned bioremediation involved injecting emulsified vegetable oil and bacteria to enhance the naturally occurring biodegradation of TCE. The flow and transport modeling showed that injection will spread amendments widely over a zone of lower-permeability fractures, with long residence times expected because of small velocities after injection and sorption of emulsified vegetable oil onto solids. Amendments transported out of this zone will be diluted by groundwater flux from other areas, limiting bioremediation effectiveness downgradient. At nearby pumping wells, further dilution is expected to make bioremediation effects undetectable in the pumped water. The results emphasize that in fracture-dominated flow regimes, the extent of injected amendments cannot be conceptualized using simple homogeneous models of groundwater flow commonly adopted to design injections in unconsolidated porous media (e.g., radial diverging or dipole flow regimes). Instead, it is important to synthesize site characterization information using a groundwater flow model that includes discrete features representing high- and low-permeability fractures. This type of model accounts for the highly heterogeneous hydraulic conductivity and groundwater fluxes in fractured-rock aquifers, and facilitates designing injection strategies that target specific volumes of the aquifer and maximize the distribution of amendments over these volumes.

A three‐dimensional groundwater management model is developed for a shallow, unconfined sandy aquifer at a Superfund site at which a vinyl chloride plume is migrating toward Lake Michigan. We use nonlinear simulation‐regression applied to a transient groundwater flow model to estimate parameter values and their uncertainties and use steady state flow path analyses to confirm the model's consistency with the location of contaminants. Parameter uncertainty is translated into flow model prediction uncertainty using a first‐order Taylor series approximation. Optimal minimum‐pumping strategies for steady state hydraulic containment of the plume are designed, and model prediction uncertainty is accounted for with stochastic programming. It is impossible to achieve a reliability level higher than 60% using only two pumping wells. For the 10‐well case, pumping rates must increase about 40% to extend reliability from 50 to 90%. Monte Carlo analyses indicate that for the 10‐well 90% reliability formulation, the first‐order method of propagating uncertainty results in a solution with accurate performance reliabilities. We find that the coefficient of variation in hydraulic gradient dictates whether the probabilistic constraints are obeyed. Comparison of the probabilistic constraint and “safety factor” approaches to overcoming model uncertainty reveals that the ability of probabilistic constraints to accommodate local variations in model prediction uncertainty is highly important. Postoptimization solute transport studies show that increased reliability levels for hydraulic containment do not necessarily translate into faster plume cleanup times.

A ground water basin is defined as the volume of subsurface through which ground water flows from the water table to a specified discharge location. Delineating the topographically defined surface water basin and extending it vertically downward does not always define the ground water basin. Instead, a ground water basin is more appropriately delineated by tracking ground water flowpaths with a calibrated, three‐dimensional ground water flow model. To determine hydrologic and chemical budgets of the basin, it is also necessary to quantify flow through each hydrogeologic unit in the basin. In particular, partitioning ground water flow through unconsolidated deposits versus bedrock is of significant interest to hillslope hydrologic studies. To address these issues, a model is developed and calibrated to simulate ground water flow through glacial deposits and fractured crystalline bedrock in the vicinity of Mirror Lake, New Hampshire. Tracking of ground water flowpaths suggests that Mirror Lake and its inlet streams drain a ground water recharge area that is about 1.5 times the area of the surface water basin. Calculation of the ground water budget suggests that, of the recharge that enters the Mirror Lake ground water basin, about 40% travels through the basin along flowpaths that stay exclusively in the glacial deposits, and about 60% travels along flowpaths that involve movement in bedrock.

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The bioavailability of total organic carbon (TOC) was examined in ground water from two hydrologically distinct aquifers using biochemical indicators widely employed in chemical oceanography. Concentrations of total hydrolyzable neutral sugars (THNS), total hydrolyzable amino acids (THAA), and carbon-normalized percentages of TOC present as THNS and THAA (referred to as ''yields'') were assessed as indicators of bioavailability. A shallow coastal plain aquifer in Kings Bay, Georgia, was characterized by relatively high concentrations (425 to 1492 lM; 5.1 to 17.9 mg/L) of TOC but relatively low THNS and THAA yields (~0.2%-1.0%). These low yields are consistent with the highly biodegraded nature of TOC mobilized from relatively ancient (Pleistocene) sediments overlying the aquifer. In contrast, a shallow fractured rock aquifer in West Trenton, New Jersey, exhibited lower TOC concentrations (47 to 325 lM; 0.6 to 3.9 mg/L) but higher THNS and THAA yields (~1% to 4%). These higher yields were consistent with the younger, and thus more bioavailable, TOC being mobilized from modern soils overlying the aquifer. Consistent with these apparent differences in TOC bioavailability, no significant correlation between TOC and dissolved inorganic carbon (DIC), a product of organic carbon mineralization, was observed at Kings Bay, whereas a strong correlation was observed at West Trenton. In contrast to TOC, THNS and THAA concentrations were observed to correlate with DIC at the Kings Bay site. These observations suggest that biochemical indicators such as THNS and THAA may provide information concerning the bioavailability of organic carbon present in ground water that is not available from TOC measurements alone.

[1] We develop a new observation-prediction (OPR) statistic for evaluating the importance of system state observations to model predictions. The OPR statistic measures the change in prediction uncertainty produced when an observation is added to or removed from an existing monitoring network, and it can be used to guide refinement and enhancement of the network. Prediction uncertainty is approximated using a first-order second-moment method. We apply the OPR statistic to a model of the Death Valley regional groundwater flow system (DVRFS) to evaluate the importance of existing and potential hydraulic head observations to predicted advective transport paths in the saturated zone underlying Yucca Mountain and underground testing areas on the Nevada Test Site. Important existing observations tend to be far from the predicted paths, and many unimportant observations are in areas of high observation density. These results can be used to select locations at which increased observation accuracy would be beneficial and locations that could be removed from the network. Important potential observations are mostly in areas of high hydraulic gradient far from the paths. Results for both existing and potential observations are related to the flow system dynamics and coarse parameter zonation in the DVRFS model. If system properties in different locations are as similar as the zonation assumes, then the OPR results illustrate a data collection opportunity whereby observations in distant, high-gradient areas can provide information about properties in flatter-gradient areas near the paths. If this similarity is suspect, then the analysis produces a different type of data collection opportunity involving testing of model assumptions critical to the OPR results.