Geology topics

Randall J. Hunt

Publications and source records attributed to Randall J. Hunt.

At least 91 records · Page 5Linked to original sources

USGS2011

Groundwater recharge in Wisconsin— Annual estimates for 1970–99 using streamflow data

The groundwater component of streamflow is important because it is indicative of the sustained flow of a stream during dry periods, is often of better quality, and has a smaller range of temperatures, than surface contributions to streamflow. All three of these characteristics are important to the health of aquatic life in a stream. If recharge to the aquifers is to be preserved or enhanced, it is important to understand the present partitioning of total streamflow into base flow and stormflow. Additionally, an estimate of groundwater recharge is important for understanding the flows within a groundwater system-information important for water availability/sustainability or other assessments. The U.S. Geological Survey operates numerous continuous-record streamflow-gaging stations (Hirsch and Norris, 2001), which can be used to provide estimates of average annual base flow. In addition to these continuous record sites, Gebert and others (2007) showed that having a few streamflow measurements in a basin can appreciably reduce the error in a base-flow estimate for that basin. Therefore, in addition to the continuous-record gaging stations, a substantial number of low-flow partial-record sites (6 to 15 discharge measurements) and miscellaneous-measurement sites (1 to 3 discharge measurements) that were operated during 1964-90 throughout the State were included in this work to provide additional insight into spatial distribution of annual base flow and, in turn, groundwater recharge.

Wisconsin↗

USGS2011

Using models for the optimization of hydrologic monitoring

Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interest (regardless of how well-suited that model is for the prediction). However, modeling tools are available that can inform which locations and types of data provide the most 'bang for the buck' for a specified prediction. Put another way, the hydrologist can determine which observation data most reduce the model uncertainty around a specified prediction. An advantage of such an approach is the maximization of limited monitoring resources because it focuses on the difference in prediction uncertainty with or without additional collection of field data. Data worth can be calculated either through the addition of new data or subtraction of existing information by reducing monitoring efforts (Beven, 1993). The latter generally is not widely requested as there is explicit recognition that the worth calculated is fundamentally dependent on the prediction specified. If a water manager needs a new prediction, the benefits of reducing the scope of a monitoring effort, based on an old prediction, may be erased by the loss of information important for the new prediction. This fact sheet focuses on the worth or value of new data collection by quantifying the reduction in prediction uncertainty achieved be adding a monitoring observation. This calculation of worth can be performed for multiple potential locations (and types) of observations, which then can be ranked for their effectiveness for reducing uncertainty around the specified prediction. This is implemented using a Bayesian approach with the PREDUNC utility in the parameter estimation software suite PEST (Doherty, 2010). The techniques briefly described earlier are described in detail in a U.S. Geological Survey Scientific Investigations Report available on the Internet (Fienen and others, 2010; http://pubs.usgs.gov/sir/2010/5159/). This fact sheet presents a synopsis of the techniques as applied to a synthetic model based on a model constructed using properties from the Lake Michigan Basin (Hoard, 2010).

Fact Sheet↗

USGS2011

Approaches to highly parameterized inversion: Pilot-point theory, guidelines, and research directions

Pilot points have been used in geophysics and hydrogeology for at least 30 years as a means to bridge the gap between estimating a parameter value in every cell of a model and subdividing models into a small number of homogeneous zones. Pilot points serve as surrogate parameters at which values are estimated in the inverse-modeling process, and their values are interpolated onto the modeling domain in such a way that heterogeneity can be represented at a much lower computational cost than trying to estimate parameters in every cell of a model. Although the use of pilot points is increasingly common, there are few works documenting the mathematical implications of their use and even fewer sources of guidelines for their implementation in hydrogeologic modeling studies. This report describes the mathematics of pilot-point use, provides guidelines for their use in the parameter-estimation software suite (PEST), and outlines several research directions. Two key attributes for pilot-point definitions are highlighted. First, the difference between the information contained in the every-cell parameter field and the surrogate parameter field created using pilot points should be in the realm of parameters which are not informed by the observed data (the null space). Second, the interpolation scheme for projecting pilot-point values onto model cells ideally should be orthogonal. These attributes are informed by the mathematics and have important ramifications for both the guidelines and suggestions for future research.

Scientific Investigations Report↗

USGS2011

Simulation of the shallow groundwater-flow system near Mole Lake, Forest County, Wisconsin

The shallow groundwater system near Mole Lake, Forest County, Wis. was simulated using a previously calibrated regional model. The previous model was updated using newly collected water-level measurements and refinements to surface-water features. The updated model was then used to calculate the area contributing recharge for one existing and two proposed pumping locations on lands of the Sokaogon Chippewa Community. Delineated 1-, 5-, and 10-year areas contributing recharge for existing and proposed wells extend from the areas of pumping to the northeast of the pumping locations. Steady-state pumping was simulated for two scenarios: a base pumping scenario using pumping rates that reflect what the Tribe expects to pump and a high pumping scenario, in which the rate was set to the maximum expected from wells installed in this area. In the base pumping scenario, pumping rates of 32 gallons per minute (gal/min; 46,000 gallons per day (gal/d)) from the existing well and 30 gal/min (43,000 gal/d) at each of the two proposed wells were simulated. The high pumping scenario simulated a rate of 70 gal/min (101,000 gal/d) from each of the three pumping wells to estimate of the largest areas contributing recharge that might be expected given what is currently known about the shallow groundwater system. The areas contributing recharge for both the base and high pumping scenarios did not intersect any modeled surface-water bodies; however, the high pumping scenario had a larger areal extent than the base pumping scenario and intersected a septic separator.

Wisconsin↗

USGS2010

A hybrid finite-difference and analytic element groundwater model

Regional finite-difference models tend to have large cell sizes, often on the order of 1–2 km on a side. Although the regional flow patterns in deeper formations may be adequately represented by such a model, the intricate surface water and groundwater interactions in the shallower layers are not. Several stream reaches and nearby wells may occur in a single cell, precluding any meaningful modeling of the surface water and groundwater interactions between the individual features. We propose to replace the upper MODFLOW layer or layers, in which the surface water and groundwater interactions occur, by an analytic element model (GFLOW) that does not employ a model grid; instead, it represents wells and surface waters directly by the use of point-sinks and line-sinks. For many practical cases it suffices to provide GFLOW with the vertical leakage rates calculated in the original coarse MODFLOW model in order to obtain a good representation of surface water and groundwater interactions. However, when the combined transmissivities in the deeper (MODFLOW) layers dominate, the accuracy of the GFLOW solution diminishes. For those cases, an iterative coupling procedure, whereby the leakages between the GFLOW and MODFLOW model are updated, appreciably improves the overall solution, albeit at considerable computational cost. The coupled GFLOW–MODFLOW model is applicable to relatively large areas, in many cases to the entire model domain, thus forming an attractive alternative to local grid refinement or inset models.

Groundwater↗

USGS2010

Using a cloud to replenish parched groundwater modeling efforts

Groundwater models can be improved by introduction of additional parameter flexibility and simultaneous use of soft-knowledge. However, these sophisticated approaches have high computational requirements. Cloud computing provides unprecedented access to computing power via the Internet to facilitate the use of these techniques. A modeler can create, launch, and terminate “virtual” computers as needed, paying by the hour, and save machine images for future use. Such cost-effective and flexible computing power empowers groundwater modelers to routinely perform model calibration and uncertainty analysis in ways not previously possible.

Ground Water↗

USGS2010

Simulation of the shallow groundwater-flow system near the Hayward Airport, Sawyer County, Wisconsin

There are concerns that removal and trimming of vegetation during expansion of the Hayward Airport in Sawyer County, Wisconsin, could appreciably change the character of a nearby cold-water stream and its adjacent environs. In cooperation with the Wisconsin Department of Transportation, a two-dimensional, steady-state groundwater-flow model of the shallow groundwater-flow system near the Hayward Airport was refined from a regional model of the area. The parameter-estimation code PEST was used to obtain a best fit of the model to additional field data collected in February 2007 as part of this study. The additional data were collected during an extended period of low runoff and consisted of water levels and streamflows near the Hayward Airport. Refinements to the regional model included one additional hydraulic-conductivity zone for the airport area, and three additional parameters for streambed resistance in a northern tributary to the Namekagon River and in the main stem of the Namekagon River. In the refined Hayward Airport area model, the calibrated hydraulic conductivity was 11.2 feet per day, which is within the 58.2 to 7.9 feet per day range reported for the regional glacial and sandstone aquifer, and is consistent with a silty soil texture for the area. The calibrated refined model had a best fit of 8.6 days for the streambed resistance of the Namekagon River and between 0.6 and 1.6 days for the northern tributary stream. The previously reported regional groundwater-recharge rate of 10.1 inches per year was adjusted during calibration of the refined model in order to match streamflows measured during the period of extended low runoff; this resulted in an optimal groundwater-recharge rate of 7.1 inches per year during this period. The refined model was then used to simulate the capture zone of the northern tributary to the Namekagon River.

Scientific Investigations Report↗

USGS2010

Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Scientific Investigations Report↗

USGS2010

Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration

Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly used in other industries, regularized inversion is somewhat imperfectly understood in the groundwater field. There is concern that this unfamiliarity can lead to underuse, and misuse, of the methodology. This document is constructed to facilitate the appropriate use of regularized inversion for calibrating highly parameterized groundwater models. The presentation is directed at an intermediate- to advanced-level modeler, and it focuses on the PEST software suite-a frequently used tool for highly parameterized model calibration and one that is widely supported by commercial graphical user interfaces. A brief overview of the regularized inversion approach is provided, and techniques for mathematical regularization offered by PEST are outlined, including Tikhonov, subspace, and hybrid schemes. Guidelines for applying regularized inversion techniques are presented after a logical progression of steps for building suitable PEST input. The discussion starts with use of pilot points as a parameterization device and processing/grouping observations to form multicomponent objective functions. A description of potential parameter solution methodologies and resources available through the PEST software and its supporting utility programs follows. Directing the parameter-estimation process through PEST control variables is then discussed, including guidance for monitoring and optimizing the performance of PEST. Comprehensive listings of PEST control variables, and of the roles performed by PEST utility support programs, are presented in the appendixes.

Scientific Investigations Report↗

USGS2010

Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in these highly parameterized modeling contexts. Availability of these utilities is particularly important because, in many cases, a significant proportion of the uncertainty associated with model parameters-and the predictions that depend on them-arises from differences between the complex properties of the real world and the simplified representation of those properties that is expressed by the calibrated model. This report is intended to guide intermediate to advanced modelers in the use of capabilities available with the PEST suite of programs for evaluating model predictive error and uncertainty. A brief theoretical background is presented on sources of parameter and predictive uncertainty and on the means for evaluating this uncertainty. Applications of PEST tools are then discussed for overdetermined and underdetermined problems, both linear and nonlinear. PEST tools for calculating contributions to model predictive uncertainty, as well as optimization of data acquisition for reducing parameter and predictive uncertainty, are presented. The appendixes list the relevant PEST variables, files, and utilities required for the analyses described in the document.

Michigan↗

USGS2009

The application of an analytic element model to investigate groundwater-lake interactions at Pretty Lake, Wisconsin

Pretty Lake is a 64 acre, sandy-bottomed groundwater flow-through lake that has a history of hydrologic disturbance. Residents and regulators require a better understanding of lake-groundwater interaction to develop measures to protect the lake's hydrologic system and water quality. A groundwater flow model was constructed as a tool to synthesize field data collected at the site, delineate recharge areas that supply groundwater to the lake, and predict die effect of dredging an adjacent drainage ditch. The one layer, two-dimensional steady-state areal model used analytic element (AE) methods because they are quick to apply and include sophisticated simulation of groundwater-surface water interaction. The model calibrated well to groundwater heads (mean absolute difference = 0.05 m), lake stage (within 0.05 m) and ditch fluxes (mean absolute difference = 0.0023 m 3 ·s −1 ). Model results showed that a single 1000 m wide recharge area supplies all the groundwater inflow to the lake. In addition, the model predicted that dredging an adjacent ditch by 3.0 m would lower the lake level by 0.31 m. The analytic element model was verified using a widely accepted finite-difference (FD) code; differences were less than ±0.015 m near die lake area and reached a maximum of 0.08 m at far corners of the FD grid. These differences are likely a result of die nodal interpolation inherent to FD techniques and error associated with applying a discrete boundary to die AE infinite aquifer. Although developed recently, AE methods have great potential to aid characterizations of groundwater-lake systems.

Wisconsin↗

USGS2009

Two statistics for evaluating parameter identifiability and error reduction

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Journal of Hydrology↗

USGS2009

Petrified Forest National Park Invasive Plant Species Survey and Mapping; 2002-2005

We conducted a survey for invasive nonnative plant species at Petrified Forest National Park from 2002 through 2005. The survey employed a unique sampling design consisting of a grid of consecutive one-hectare cells as the sampling units. Our use of predetermined sampling units allowed all observations to be referenced to a fixed area with geographic coordinates that easily transferred to a geographic information system. Our field team surveyed 2,730 sampling units in three select areas for at least 1 year and 879 sampling units for 4 years. During this period we identified 40 different invasive plant species; more than half the invasive plants (22 species) were annual forbs and grasses. Four invasive plant species occurred in 25 percent or more of all sampling units observed in one or more years: Bromus tectorum, Erodium cicutarium, Salsola tragus, and Sisymbrium altissimum. Salsola tragus was the most abundant species in all years and occurred in more than 55 percent of all sampling units surveyed each year.

Open-File Report↗

USGS2009

Using a coupled groundwater/surface-water model to predict climate-change impacts to lakes in the Trout Lake Watershed, northern Wisconsin

A major focus of the U.S. Geological Survey’s Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. The coupled groundwater/surface-water model GSFLOW was chosen for this purpose because it could easily incorporate an existing groundwater flow model and it provides for simulation of surface-water processes. The Trout Lake watershed in northern Wisconsin is underlain by a highly conductive outwash sand aquifer. In this area, streamflow is dominated by groudwater contributions, however, surface runoff occurs during intense rainfall periods and spring snowmelt. Surface runoff also occurs locally near stream/lake areas where the unsaturated zone is thin. A diverse data set, collected from 1992 to 2007 for the Trout Lake WEBB project and the co-located and NSF-funded North Temperate Lake LTER project, includes snowpack, solar radiation, potential evapotranspiration, lake levels, groundwater levels, and streamflow. The time-series processing software TSPROC (Doherty 2001)was used to distill the large time series data set to a smaller set of observations and summary statistics that captured the salient hydrologic information. The time-series processing reduced hundreds of thousands of observations to less than 5,000. Model calibration included specific predictions for several lakes in the study area using the PEST parameter estimation suit of software (Doherty 2007). The calibrated model was used to simulate the hydrologic response in the study lakes to a variety of climate change scenarios culled from the IPCC Fourth Assessment Report of the Intergovernmental Panel of Climate Change (Solomon et al. 2007). Results from the simulations indicate climate change could result in substantial changes to the lake levels and components of the hydrologic budget of a seepage lake in the flow system. For a drainage lake lower in the flow system, the impacts of climate change are diminished.

Wisconsin↗

USGS2009

Selected achievements, science directions, and new opportunities for the WEBB small watershed research program

Over nearly two decades, the Water, Energy, and Biogeochemical Budgets (WEBB) small watershed research program of the U.S. Geological Survey (USGS) has documented how water and solute fluxes, nutrient, carbon, and mercury dynamics, and weathering and sediment transport respond to natural and humancaused drivers, including climate, climate change, and atmospheric deposition. Together with a continued and increasing focus on the effects of climate change, more investigations are needed that examine ecological effects (e.g., evapotranspiration, nutrient uptake) and responses (e.g., species abundances, biodiversity) that are coupled with the physical and chemical processes historically observed in the WEBB program. Greater use of remote sensing, geographic modeling, and habitat/watershed modeling tools is needed, as is closer integration with the USGS-led National Phenology Network. Better understanding of process and system response times is needed. The analysis and observation of land-use and climate change effects over time should be improved by pooling data obtained by the WEBB program during the last two decades with data obtained earlier and (or) concurrently from other research and monitoring studies conducted at or near the five WEBB watershed sites. These data can be supplemented with historical and paleo-environmental information, such as could be obtained from tree rings and lake cores. Because of the relatively pristine nature and small size of its watersheds, the WEBB program could provide process understanding and basic data to better characterize and quantify ecosystem services and to develop and apply indicators of ecosystem health. In collaboration with other Federal and State watershed research programs, the WEBB program has an opportunity to contribute to tracking the short-term dynamics and long-term evolution of ecosystem services and health indicators at a multiplicity of scales across the landscape.

Conference Paper↗

USGS2009

Using a coupled groundwater/surfacewater model to predict climate-change impacts to lakes in the Trout Lake watershed, Northern Wisconsin

A major focus of the U.S. Geological Survey’s Trout Lake Water, Energy, and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. The coupled groundwater/surface-water model GSFLOW was chosen for this purpose because it could easily incorporate an existing groundwater flow model and it provides for simulation of surface-water processes. The Trout Lake watershed in northern Wisconsin is underlain by a highly conductive outwash sand aquifer. In this area, streamflow is dominated by groundwater contributions; however, surface runoff occurs during intense rainfall periods and spring snowmelt. Surface runoff also occurs locally near stream/lake areas where the unsaturated zone is thin. A diverse data set, collected from 1992 to 2007 for the Trout Lake WEBB project and the co-located and NSF-funded North Temperate Lakes LTER project, includes snowpack, solar radiation, potential evapotranspiration, lake levels, groundwater levels, and streamflow. The timeseries processing software TSPROC (Doherty 2003) was used to distill the large time series data set to a smaller set of observations and summary statistics that captured the salient hydrologic information. The timeseries processing reduced hundreds of thousands of observations to less than 5,000. Model calibration included specific predictions for several lakes in the study area using the PEST parameter estimation suite of software (Doherty 2007). The calibrated model was used to simulate the hydrologic response in the study lakes to a variety of climate change scenarios culled from the IPCC Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Solomon et al. 2007). Results from the simulations indicate climate change could result in substantial changes to the lake levels and components of the hydrologic budget of a seepage lake in the flow system. For a drainage lake lower in the flow system, the impacts of climate change are diminished.

Conference Paper↗

USGS2008

Simulation of contributing areas and surface-water leakage to potential replacement wells near the community of New Post, Sawyer County, Wisconsin, by means of a two-dimensional ground-water flow model

A two-dimensional, steady-state ground-water-flow model of the shallow ground-water-flow system near the community of New Post, Sawyer County, Wis., was refined from an existing model of the area. Hydraulic-conductivity and recharge values were not changed from the existing model for the scenario simulations described in this report. Rather, the model was refined by adding detail along the Chippewa Flowage and then was used to simulate contributing areas for three potential replacement wells pumping 30,000 gallons per day. The model also was used to simulate potential surface-water leakage out of the Chippewa Flowage captured by replacement-well pumping. A range in resistance to vertical ground-water flow was simulated along the Chippewa Flowage for each potential replacement-well location to bound the potential effects of representing three-dimensional flow with a two-dimensional model. Results indicate that pumping from a replacement well sited about 130 feet from the Chippewa Flowage could capture as much as 39 percent of the total pumping from the flowage. Pumping from either of two potential replacement wells sited at least 400 feet from the Chippewa Flowage did not induce surface-water leakage out of the flowage regardless of the resistance applied along the flowage for simulations described in this report.

Wisconsin↗

USGS2007

Identifying spatial variability of groundwater discharge in a wetland stream using a distributed temperature sensor

Discrete zones of groundwater discharge in a stream within a peat‐dominated wetland were identified on the basis of variations in streambed temperature using a distributed temperature sensor (DTS). The DTS gives measurements of the spatial (±1 m) and temporal (15 min) variation of streambed temperature over a much larger reach of stream (>800 m) than previous methods. Isolated temperature anomalies observed along the stream correspond to focused groundwater discharge zones likely caused by soil pipes within the peat. The DTS also recorded variations in the number of temperature anomalies, where higher numbers correlated well with a gaining reach identified by stream gauging. Focused zones of groundwater discharge showed essentially no change in position over successive measurement periods. Results suggest DTS measurements will complement other techniques (e.g., seepage meters and stream gauging) and help further improve our understanding of groundwater–surface water dynamics in wetland streams.

Water Resources Research↗