Abstract. We examine the identification of large-scale spatial trends in hydraulic conductivity and the influence of these trends on contaminant transport. Using three different trend identification methods, polynomial regression and Kalman filtering, which fit smooth functions, and hydrofacies delineation, which constructs a geologic model, we try to identify the hydraulic conductivi _+ ty patterns controlling solute transport in a heavily sampled heterogeneous aquifer on Columbus Air Force Base, Mississippi. Even with >2400 hydraulic conductivity measurements, unambiguous determination of largescale trends is not possible. None of the estimated hydraulic conductivity trends gives transport simulations that reproduce the observed non-Gaussian transport behavior. Hydrofacies delineation and Kalman filtering give the best results. While the influence of the identified large-scale trends on advective transport is significant, accurate prediction of contaminant transport requires knowledge of small-scale (<10 m) hydraulic conductivity structures. IntroductionThe spatial heterogeneity of hydrogeologic parameters, particularly hydraulic conductivity, makes prediction of groundwater flow and contaminant transport difficult. Hydraulic conductivity, the primary hydrogeologic parameter controlling transport, is highly variable in most alluvial aquifers, varying at some sites by 6 orders of magnitude over a distance of <10 m. Before groundwater flow and contaminant transport can be modeled in detail a three-dimensional map of hydraulic conductivity and other key hydrologic parameters is needed. Obtaining measurements of the subsurface is an involved, expensive process, and the limited resources of any groundwater investigation allow only an incomplete picture of the subsurface. Investigators therefore must interpolate sparse spatial data to build models of groundwater systems.A common approach for modeling subsurface transport is to assume that hydraulic conductivity K variations on a large spatial scale control advective transport and hydraulic conductivity variations on a small spatial scale control dispersive transport [Scheibe and Cole, 1994]. The exact definitions of "large-scale" and "small-scale" can vary depending on the density of measured data and the amount of natural heterogeneity present. Although some studies assume that hydraulic conductivity varies on two scales, large and small [Rajaram and McLaughlin, 1990; Brannan and Haselow, 1993], others assume that it varies on multiple scales [Dagan, 1986; Cushman, 1990, chap. 1) or on a continuous hierarchy of scales [Neuman, 1990]. In the terminology of geostatistics, hydraulic conductivity variations that have a spatial scale large enough to be described in at least a roughly deterministic manner are known as a "trends" or "drift" and smaller-scale variations, which can only be described statistically because of the sparsity of data, are Separating fluctuations in hydraulic conductivity according to spatial scale is not a straightforward process. Variograms and ...
Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.
This study evaluates commonly used geostatistical methods to assess reproduction of hydraulic conductivity (K) structure and sensitivity under limiting amounts of data. Extensive conductivity measurements from the Cape Cod sand and gravel aquifer are used to evaluate two geostatistical estimation methods, conditional mean as an estimate and ordinary kriging, and two stochastic simulation methods, simulated annealing and sequential Gaussian simulation. Our results indicate that for relatively homogeneous sand and gravel aquifers such as the Cape Cod aquifer, neither estimation methods nor stochastic simulation methods give highly accurate point predictions of hydraulic conductivity despite the high density of collected data. Although the stochastic simulation methods yielded higher errors than the estimation methods, the stochastic simulation methods yielded better reproduction of the measured ln (K) distribution and better reproduction of local contrasts in ln (K). The inability of kriging to reproduce high ln (K) values, as reaffirmed by this study, provides a strong instigation for choosing stochastic simulation methods to generate conductivity fields when performing fine‐scale contaminant transport modeling. Results also indicate that estimation error is relatively insensitive to the number of hydraulic conductivity measurements so long as more than a threshold number of data are used to condition the realizations. This threshold occurs for the Cape Cod site when there are approximately three conductivity measurements per integral volume. The lack of improvement with additional data suggests that although fine‐scale hydraulic conductivity structure is evident in the variogram, it is not accurately reproduced by geostatistical estimation methods. If the Cape Cod aquifer spatial conductivity characteristics are indicative of other sand and gravel deposits, then the results on predictive error versus data collection obtained here have significant practical consequences for site characterization. Heavily sampled sand and gravel aquifers, such as Cape Cod and Borden, may have large amounts of redundant data, while in more common real world settings, our results suggest that denser data collection will likely improve understanding of permeability structure.
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