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Stream water was locally recharged into shallow groundwater flow paths that returned to the stream (hyporheic exchange) in St. Kevin Gulch, a Rocky Mountain stream in Colorado contaminated by acid mine drainage. Two approaches were used to characterize hyporheic exchange: sub-reach-scale measurement of hydraulic heads and hydraulic conductivity to compute streambed fluxes (hydrometric approach) and reachscale modeling of in-stream solute tracer injections to determine characteristic length and timescales of exchange with storage zones (stream tracer approach). Subsurface data were the standard of comparison used to evaluate the reliability of the stream tracer approach to characterize hyporheic exchange. The reach-averaged hyporheic exchange flux (1.5 mL s -• -•_)•, determined by hydrometric methods, was largest when stream base flow was low (10 • s ); hyporheic exchange persisted when base flow was 10-fold higher, decreasing by approximately 30%. Reliability of the stream tracer approach to detect hyporheic exchange was assessed using first-order uncertainty analysis that considered model parameter sensitivity. The stream tracer approach did not reliably characterize hyporheic exchange at high base flow: the model was apparently more sensitive to exchange with surface water storage zones than with the hyporheic zone. At low base flow the stream tracer approach reliably characterized exchange between the stream and gravel streambed (timescale of hours) but was relatively insensitive to slower exchange with deeper alluvium (timescale of tens of hours) that was detected by subsurface measurements. The stream tracer approach was therefore not equally sensitive to all timescales of hyporheic exchange. We conclude that while the stream tracer approach is an efficient means to characterize surface-subsurface exchange, future studies will need to more routinely consider decreasing sensitivities of tracer methods at higher base flow and a potential bias toward characterizing only a fast component of hyporheic exchange. Stream tracer models with multiple rate constants to consider both fast exchange with streambed gravel and slower exchange with deeper alluvium appear to be warranted. This paper is not subject to U.S. copyright. Published in 1996 by the American Geophysical Union. Paper number 96WR01268. face flow systems can be large or small in extent. Individual flow paths of exchange range in scale from hundreds of meters, in which transport occurs on a timescale of years, to centimeter-long flow paths, in which transport occurs on a timescale of minutes. Interactions are driven at small scales by steady flow of surface water over roughness features such as sand waves or pools and riffles. The resulting uneven pressure distributions on the channel bed cause surface water to flow into and out of the bed [Thibodeaux and Boyle, 1987; Harvey and Bencala, 1993]. We refer to small-scale (centimeter to meter) exchanges of water between channels and the subsurface as "hyporheic exchange" (Figure la) in order to emphasize the ...

Abstract. Tracer experiments are valuable tools for analyzing the transport characteristics of streams and their interactions with shallow groundwater. The focus of this work is the design of tracer studies in high-gradient stream systems subject to advection, dispersion, groundwater inflow, and exchange between the active channel and zones in surface or subsurface water where flow is stagnant or slow moving. We present a methodology for (1) evaluating and comparing alternative stream tracer experiment designs and (2) identifying those combinations of stream transport properties that pose limitations to parameter estimation and therefore a challenge to tracer test design. The methodology uses the concept of global parameter uncertainty analysis, which couples solute transport simulation with parameter uncertainty analysis in a Monte Carlo framework. Two general conclusions resulted from this work. First, the solute injection and sampling strategy has an important effect on the reliability of transport parameter estimates. We found that constant injection with sampling through concentration rise, plateau, and fall provided considerably more reliable parameter estimates than a pulse injection across the spectrum of transport scenarios likely encountered in high-gradient streams. Second, for a given tracer test design, the uncertainties in mass transfer and storage-zone parameter estimates are strongly dependent on the experimental Damkohler number, DaI, which is a dimensionless combination of the rates of exchange between the stream and storage zones, the stream-water velocity, and the stream reach length of the experiment.

To date optimization models for groundwater quality management give no assurance that water quality standards will be met. This is in part because they ignore errors in hydraulic heads, flows, and solute concentrations due to flow and transport model parameter uncertainty. Here we explicitly incorporate parameter estimation and estimate uncertainties into a model for the optimal design of an aquifer remediation scheme. Parameter uncertainty is incorporated into the decision‐making process. The objective is to identify the best remediation strategies (well site selection and pumping‐recharge rates) so that water quality standards are met at a specified reliability level. The procedure couples three methods: (1) a finite element flow and transport simulation model combined with nonlinear least squares multiple regression for simultaneous flow and transport parameter estimation; (2) first‐order first‐ and second‐moment analysis to transfer the information about the effects of parameter uncertainty to the management model; and (3) nonlinear chance‐constrained stochastic optimization combined with flow and transport simulation for optimal decision making. This joint approach enables one to estimate unknown aquifer parameters, quantify the uncertainty of the parameter estimates, simulate flow and transport responses, and automatically account for parameter uncertainty in the decision‐making process through the simulation management model. Results show that remediation requirements can increase dramatically due to parameter uncertainty. Risk‐averse design solutions automatically provide insurance by “overdesigning” the strategy relative to the risk‐neutral case. The approach is fairly general and is applicable to a variety of groundwater management problems. The influence on design solutions of the reliability level and verification of the underlying statistical assumptions of the first‐order analysis are explored in a sensitivity study and 2000 Monte Carlo simulations, respectively.

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Most optimization models for groundwater quality management have ignored the effects of uncertainty due to spatial variability of hydraulic conductivity. Here we explicitly incorporate this uncertainty into a procedure for the optimal design of aquifer remediation strategies. Local hydraulic conductivity and head data are used to quantify the uncertainty which is traced through to target a reliable remediation design. The management procedure is based on the stochastic approach to groundwater flow and contaminant transport modeling, in which the log-hydraulic conductivity is represented as a random field. The remediation design procedure has two steps. The first is solution of the stochastic inverse model. Maximum likelihood and Gaussian conditional mean estimation are used to characterize the random conductivity field based on the hydraulic conductivity and hydraulic head measurements. Based on this statistical characterization, conditional simulation is used to generate numerous realizations (maps) of spatially variable hydraulic conductivity that honor the head and conductivity data. The second step is solution of the groundwater quality management model. Two management model formulations are presented. The first, termed the multiple realization management model, simultaneously solves the nonlinear simulation-optimization problem for a sampling of hydraulic conductivity realizations. It is shown that reclamation design based on as few as 30 conductivity realizations can provide reliable (over 90%) remediation strategies. The second model, termed the Monte Carlo management model, solves the nonlinear simulation-optimization problem individually for a sampling of hydraulic conductivity realizations. This provides a relationship between pumping (cost) and reliability. Each of the management models is linked with the stochastic inverse model, and each is demonstrated for two cases: (1) the available data are limited to hydraulic conductivity measurements and (2) both hydraulic conductivity and hydraulic head measurements are used.The contribution of this work is to focus on the effects of uncertainty due to the unknown spatial variability of hydraulic conductivity. We demonstrate a systematic approach, using two different formulations, which traces the effects of uncertainty through to the design of reliable aquifer remediation schemes. LITERATURE REVIEWGroundwater management models have been developed for a variety of applications, such as restoration of contaminated aquifers, isolation of a plume of contaminated groundwater, prevention of saltwater intrusion, aquifer dewatering for excavation, and management of the conjunctive use of surface water and groundwater. To date, most groundwater management studies have ignored uncertainty associated with the groundwater flow and contaminant transport simulation models. A review of simulation-optimization methods for management of groundwater hydraulics, for groundwater policy evaluation and water allocation, and for groundwater quality management can be found in the w...

In recent years, the aquifer simulation model has been combined with techniques of optimization to address important groundwater management problems. The combined simulation and optimization model accounts for the complex behavior of the groundwater system and identifies the best management strategy under consideration of the management objectives and constraints. Simulation‐optimization groundwater management models have been developed for a variety of applications, such as restoration of contaminated groundwater, control of aquifer hydraulics, allocation of ground and surface water resources, and evaluation of groundwater policies [see reviews by Gorelick, 1983, 1990; Yeh, 1992; Ahlfeld and Heidari, 1994; Bredehoeft et al, 1994]. Gorelick [1983] divides groundwater management models into three categories: groundwater hydraulic management, groundwater quality management, and groundwater policy evaluation and allocation. This review focuses in detail on the recent advances made in groundwater hydraulic and groundwater quality management modeling, which encompasses most of the aquifer management work done in the past four years. For a review of recent research in groundwater policy evaluation and allocation, see Bredehoeft et al. [1994].

The bead array approach is a rapid and reliable test for detecting aneuploidies and microdeletions. This assay has the potential to provide the benefit of expanded molecular cytogenetic testing to pregnant women undergoing invasive prenatal diagnosis. This approach may be especially useful in parts of the world where cytogenetic personnel and facilities may be limited.