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Randall J. Hunt

Publications and source records attributed to Randall J. Hunt.

At least 37 records · Page 2Linked to original sources
USGS2021

Evaluating lower computational burden approaches for calibration of large environmental models

Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.

Mississippi Embayment regional aquifer system↗
USGS2021

Assessment of NMR logging for estimating hydraulic conductivity in glacial aquifers

Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K . Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1 H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐ K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐ K ( K < 10 −4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K , suggesting the SDR model does not incorporate all the properties of the pore space that control K . Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐ K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.

Groundwater↗
USGS2021

Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin

Background: Groundwater quality in the Silurian dolomite aquifer in northeastern Wisconsin, USA, has become contentious as dairy farms and exurban development expand. Objectives: We investigated private household wells in the region, determining the extent, sources, and risk factors of nitrate and microbial contamination. Methods: Total coliforms, Escherichia coli , and nitrate were evaluated by synoptic sampling during groundwater recharge and no-recharge periods. Additional seasonal sampling measured genetic markers of human and bovine fecal-associated microbes and enteric zoonotic pathogens. We constructed multivariable regression models of detection probability (log-binomial) and concentration (gamma) for each contaminant to identify risk factors related to land use, precipitation, hydrogeology, and well construction. Results: Total coliforms and nitrate were strongly associated with depth-to-bedrock at well sites and nearby agricultural land use, but not septic systems. Both human wastewater and cattle manure contributed to well contamination. Rotavirus group A, Cryptosporidium , and Salmonella were the most frequently detected pathogens. Wells positive for human fecal markers were associated with depth-to-groundwater and number of septic system drainfield within 229 m "> 229 m 229m . Manure-contaminated wells were associated with groundwater recharge and the area size of nearby agricultural land. Wells positive for any fecal-associated microbe, regardless of source, were associated with septic system density and manure storage proximity modified by bedrock depth. Well construction was generally not related to contamination, indicating land use, groundwater recharge, and bedrock depth were the most important risk factors. Discussion: These findings may inform policies to minimize contamination of the Silurian dolomite aquifer, a major water supply for the U.S. and Canadian Great Lakes region.

Wisconsin↗
USGS2020

Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis

PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first-order, second-moment parameter and forecast uncertainty estimation (PESTPP-GLM); an iterative, localized ensemble smoother (PESTPP-IES); and a tool for management optimization under uncertainty (PESTPP-OPT). Additionally, all PEST++ Version 5 tools have a built-in fault-tolerant, multithreaded parallel run manager and are model independent, using the same protocol as the widely used PEST software suite. PEST++ Version 5 is consistent with PEST++ Version 3 conventions and design philosophy. The software’s emphasis continues to target efficient and optimized algorithms that have proven beneficial in decision-support settings and can accommodate large, highly parameterized problems. Expanded and new capabilities are now available to express uncertainty using Monte Carlo and analytical uncertainty approaches and allow evaluation of thousands to millions of parameters. New management optimization capabilities in Version 5 also allow environmental models to be used to answer management questions using multiple societal constraints in a risk-based framework. The PEST++ Version 5 software suite can be compiled for Microsoft Windows® and Unix-based operating systems such as Apple and Linux®; the source code is available with a Microsoft Visual Studio® 2019 solution; and CMake support for all three operating system is also provided. PEST++ Version 5 continues to build a foundation for an open-source framework capable of producing model-independent, robust, and efficient decision-support tools for large environmental models. The functionality of each of the PEST++ tools are demonstrated on a simple example problem. Implications of decisions used when using the PEST++ suite tools are also discussed.

Techniques and Methods↗
USGS2020

Modeling water quality in watersheds: From here to the next generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Water Resources Research↗
USGS2020

NGWA Groundwater Modeling Advisory Panel Survey on the Use of Uncertainty Analysis in Groundwater Modeling

This white paper provides the results of a survey by members of the NGWA Groundwater Modeling Advisory Panel to assess the use of uncertainty analysis in groundwater modeling. The objective of the survey was to improve understanding of the use of uncertainty analysis in practical groundwater modeling projects subject to real-world constraints, such as limited budgets and tight deadlines.

Report↗
USGS2019

A simple method for simulating groundwater interactions with fens to forecast development effects

Protection of fens–wetlands dependent on groundwater discharge–requires characterization of groundwater sources and stresses. Because instrumentation and numerical modeling of fens is labor intensive, easy-to-apply methods that model fen distribution and their vulnerability to development are desirable. Here we demonstrate that fen areas can be simulated using existing steady-state MODFLOW models when the unsaturated zone flow (UZF) package is included. In cells where the water table is near land surface, the UZF package calculates a head difference and scaled conductance at these “seepage drain” cells to generate average rates of vertical seepage to the land. This formulation, which represents an alternative to blanketing the MODFLOW domain with drains, requires very little input from the user because unsaturated flow-routing is inactive and results are primarily driven by easily obtained topographic information. Like the drain approach, it has the advantage that the distribution of seepage areas is not predetermined by the modeler, but rather emerges from simulated heads. Beyond the drain approach, it takes account of intracell land surface variation to explicitly quantify multiple surficial flows corresponding to infiltration, rejected recharge, recharge and land-surface seepage. Application of the method to a basin in southeastern Wisconsin demonstrates how it can be used as a decision-support tool to first, reproduce fen distribution and, second, forecast drawdown and reduced seepage at fens in response to shallow pumping.

Wisconsin↗
USGS2019

Revisiting “An Exercise in Groundwater Model Calibration and Prediction” after 30 years: Insights and New Directions

In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods.

Groundwater↗
USGS2019

Simulation of the regional groundwater-flow system in the St. Louis River basin, Minnesota

The St. Louis River Basin (SLRB) covers 3,600 square miles in northeastern Minnesota, with headwaters in the Mesabi Range and extensive wetlands and lakes throughout the basin. To better understand the regional groundwater system in the SLRB, a two-dimensional, steady-state groundwater-flow model of the SLRB was developed by the U.S. Geological Survey, in cooperation with the Minnesota Ojibwe Bands, using the analytic-element computer code GFLOW. The parameter-estimation software suite PEST was used to obtain a best fit of the modeled to measured groundwater levels and streamflows. The calibrated regional model was locally refined to create a smaller version of the model, the central SLRB model, that was used to evaluate hydrologic effects from extensive ditching in wetlands of the central SLRB. The refinements included adding ditches that were not represented in the regional model and modifying the aquifer base elevation to be more representative of the localized area. The central SLRB model was recalibrated to better match the distribution of mapped wetlands. Two scenarios were run of the central SLRB model: one with ditches and one without ditches. The model results were compared between the two scenarios to assess the effect of ditching on the groundwater system and potential changes to hydrologic conditions that support wetlands. Calibration of the regional SRLB model resulted in average horizontal hydraulic conductivity values of 6–39 feet per day for the glacial deposits and 3–4 feet per day for the uppermost fractured bedrock in the Biwabik Iron-Formation on the Mesabi Range. Average recharge across the calibrated model was 5.9 inches per year. Linesink resistance for the routed stream network was calibrated by using resistance categories based on the mapped soil hydrologic groups. The modeled regional groundwater-flow direction was generally to the south near the Mesabi Range topographic high and south or southwest across the rest of the basin. The updated calibration of the central SLRB model resulted in average horizontal hydraulic conductivity values of 5–36 feet per day for the glacial deposits and 3 feet per day for the uppermost fractured Biwabik Iron-Formation of the Mesabi Range. Average recharge across the ditch scenarios was 4.1 inches per year. Comparison of the preditch and postditch model scenarios showed that ditching reduced the area where the modeled water table was within 1 foot of the land surface (a wetland hydrology indicator) in as much as 40,000 acres, or 37 percent, of mapped permanent wetlands in the SLRB. An increase in the depth to the water table in wetland areas has the potential to degrade wetland persistence or function.

Minnesota↗
USGS2019

Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow , and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management.

Environmental Modelling & Software↗
USGS2019

A framework for characterising and evaluating the effectiveness of environmental modelling

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures.

Environmental Modelling and Software↗
USGS2018

Book review: Analytical groundwater mechanics

Encapsulating almost 50 years of experience applying mathematics to groundwater flow problems, this latest textbook from Otto Strack ( 2017 ) is a tour de force for analytical groundwater approaches. It is comprised of 10 chapters, spanning topics from the basics of groundwater mechanics, to steady state, three‐dimensional, and transient flow, as well as particle tracking and solute transport. The book concludes with a chapter on finite‐difference and finite‐element methods, but the treatment is cursory, focusing on basic principles. The book is high quality, especially with respect to the presentation, due perhaps in no small part to the keen eye of the author's longtime publication resource (and clearly supportive wife) Andrine!

Groundwater↗
USGS2018

Automated time-series measurement of microbial concentrations in groundwater-derived water supplies

Fecal contamination by human and animal pathogens, including viruses, bacteria, and protozoa, is a potential human health hazard, especially with regards to drinking water. Pathogen occurrence in groundwater varies considerably in space and time, which can be difficult to characterize as sampling typically requires hundreds of liters of water to be passed through a filter. Here we describe the design and deployment of an automated sampler suited for hydrogeologically and chemically dynamic groundwater systems. Our design focused on a compact form to facilitate transport and quick deployment to municipal and domestic water supplies. We deployed a sampler to characterize water quality from a household well tapping a shallow fractured dolomite aquifer in northeast Wisconsin. The sampler was deployed from January to April 2017, and monitored temperature, nitrate, chloride, specific conductance, and fluorescent dissolved organic matter on a minute time step; water was directed to sequential microbial filters during three recharge periods that ranged from 5 to 20 days. Results from the automated sampler demonstrate the dynamic nature of the household water quality, especially with regard to microbial targets, which were shown to vary 1 to 2 orders of magnitude during a single sampling event. We believe assessments of pathogen occurrence and concentration, and related assessments of drinking well vulnerability, would be improved by the time‐integrated characterization provided by this sampler.

Groundwater↗
USGS2018

SWB Version 2.0—A soil-water-balance code for estimating net infiltration and other water-budget components

The U.S. Geological Survey’s Soil-Water-Balance (SWB) code was developed as a tool to estimate distribution and timing of net infiltration out of the root zone by means of an approach that uses readily available data and minimizes user effort required to begin a SWB application. SWB calculates other components of the water balance, including soil moisture, reference and actual evapotranspiration, snowfall, snowmelt, canopy interception, and crop-water demand. SWB is based on a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated at a daily time step. Net-infiltration calculations are computed by means of a rectangular grid of computational elements, which allows the calculated infiltration rates to be imported into grid-based regional groundwater-flow models. SWB makes use of gridded datasets, including datasets describing hydrologic soil groups, moisture-retaining capacity, flow direction, and land use. Climate data may be supplied in gridded or tabular form. The SWB 2.0 code described in this report extends capabilities of the original SWB version 1.0 model by adding new options for representing physical processes and additional data input and output capabilities. New methods included in SWB 2.0 allow for direct gridded input of externally calculated water-budget components (fog, septic, and storm-sewer leakage), simulation of canopy interception by several alternative processes, and a crop-water demand method for estimating irrigation amounts. New input and output capabilities allow for grids with differing spatial extents and projections to be combined without requiring the user to resample and resize the grids before use.

Techniques and Methods↗
USGS2018

Groundwater modeling

The state of the science and practice in groundwater modeling brings to mind highly sophisticated computer models that are running in parallel on many multi-processor machines. These models are expected to incorporate many different processes of both saturated and unsaturated groundwater flow and transport and possibly the media to which it connects, like surface waters and the atmosphere. We are increasingly aware we cannot study groundwater flow in isolation if we are to make useful predictions of, for instance, the impacts of climate change on the groundwater regime. We have come a long way. Today we are no longer limited to equations for flow toward a well, perhaps near an infinitely long straight canal (method of images), to sandbox models in the laboratory, or to simple steady state models of flow in a single aquifer. We now have computer models that solve groundwater flow and transport in multi-aquifer settings under transient conditions and with a user-friendly graphical user interface that allows widespread use. Additionally, multi-media models are now leaving the research environment and becoming available to mainstream consultants. So in that sense the science of groundwater modeling has matured. The practice of groundwater modeling, however, has also matured. We have come to realize that model output, being a necessary simplification of an unknowably complex natural world, has inherent limitations. That is, a model of reality is not reality itself. There is uncertainty associated with all facets of our model—parameterization, aquifer geometry and discretization, boundary conditions, and future hydrologic drivers such as future pumping regimes and climates. Today a model is now more appropriately seen as a tool that provides a quantitative framework to make supportable forecasts rather than an oracle that gives us all the answers. In this chapter we set out to briefly review the state of the science and practice in modeling. In doing so, we augment existing assessments from the journal Groundwater (e.g., Hunt and Zheng 2012; Langevin and Panday 2012; Molz 2017a,b; White 2017), specifically in terms of modeling approach. An effective modeling approach is critical. If a modeler does not decompose the societal problem correctly, the model will not be fit-for-purpose, no matter how sophisticated the code’s capabilities. Moreover, capabilities of codes will be ever improving; good modeling practices have a timelessness that is more robust. How best to decompose the problem and provide models that are accepted? We lay out here some approaches for today’s applied groundwater modeling. Specifically, we suggest: (1) a step-wise modeling process; (2) including a two-dimensional areal model within this process; (3) keeping abreast of industry standards; and (4) ways to increase acceptance of the models we produce.

Book chapter↗
USGS2017

Estimation of the groundwater resources of the bedrock aquifers at the Kettle Moraine Springs State Fish Hatchery, Sheboygan County, Wisconsin

Groundwater resources information was needed to understand regional aquifer systems and water available to wells and springs for rearing important Lake Michigan fish species at the Kettle Moraine Springs State Fish Hatchery in Sheboygan County, Wisconsin. As a basis for estimating the groundwater resources available, an existing groundwater-flow model was refined, and new groundwater-flow models were developed for the Kettle Moraine Springs State Fish Hatchery area using the U.S. Geological Survey (USGS) finite-difference code MODFLOW. This report describes the origin and construction of these groundwater-flow models and their use in testing conceptual models and simulating the hydrogeologic system. The study area is in the Eastern Ridges and Lowlands geographical province of Wisconsin, and the hatchery property is situated on the southeastern edge of the Kettle Moraine, a north-south trending topographic high of glacial origin. The bedrock units underlying the study area consist of Cambrian, Ordovician, and Silurian units of carbonate and siliciclastic lithology. In the Sheboygan County area, the sedimentary bedrock sequence reaches a thickness of as much as about 1,600 feet (ft). Two aquifer systems are present at the Kettle Moraine Springs State Fish Hatchery. A shallow system is made up of Silurian bedrock, consisting chiefly of dolomite, overlain by unconsolidated Quaternary-age glacial deposits. The glacial deposits of this aquifer system are the typical source of water to local springs, including the springs that have historically supplied the hatchery. The shallow aquifer system, therefore, consists of the unconsolidated glacial aquifer and the underlying bedrock Silurian aquifer. Most residential wells in the area draw from the Silurian aquifer. A deeper confined aquifer system is made up of Cambrian- and Ordovician-age bedrock units including sandstone formations. Because of its depth, very few wells are completed in the Cambrian-Ordovician aquifer system (COAS) near the Kettle Moraine Springs State Fish Hatchery. Three groundwater-flow models were used to estimate the water resources available to the hatchery from bedrock aquifers under selected scenarios of well placement and seasonal water requirements and subject to constraints on the effects of pumping on neighboring wells, local springs, and creeks. Model input data (recharge, water withdrawal, and boundary conditions) for these models were compiled from a number of data and information sources. The first model, named the “KMS model,” (KMS stands for Kettle Moraine Springs) is an inset model derived from a published USGS regional Lake Michigan Basin model and was constructed to simulate groundwater pumping from the semiconfined Silurian aquifer. The second model, named the “Pumping Test model,” was constructed to evaluate an aquifer pumping test conducted in the COAS as part of this project. The Pumping Test model was also used to simulate the local effects of 20 years of groundwater pumping from this deep bedrock aquifer for future hatchery operations. The third model, named the “LMB modified model,” is a version of the published Lake Michigan Basin (LMB) model that was modified with aquifer parameters refined in an area around the hatchery (approximately a 5-mile radius circle, corresponding to the area stressed by the aquifer pumping test). This LMB modified model was applied to evaluate regional effects of pumping from the confined COAS. The available Silurian aquifer groundwater resource was estimated using the KMS model with three scenarios—named “AllConstraints,” “Constraints2,” and “Constraints3”—that specified local water-level and flow constraints such as drawdown at nearby household wells, water levels inside pumping well boreholes, and flow in local streams and springs. Each scenario utilized the MODFLOW Groundwater Management Process (GWM) to select three locations from six candidate locations that provided the greatest combined flow while satisfying the constraints. The three constraint scenarios provided estimates of 430 gallons per minute (gal/min), 480 gal/min, and 520 gal/min pumping from three wells—AllConstraints, Constraints2, and Constraints3, respectively. The same three wells were selected for the scenarios that estimated 480 gal/min and 520 gal/min; the scenario that estimated 430 gal/min shared two of these same wells, but the third selected well was different. The available COAS groundwater resource was estimated by two scenarios with each conducted over a period of 20 years with the Pumping Test model and the LMB modified model . The Pumping Test model was used to simulate local effects of pumping, and the LMB modified model was used to simulate regional effects of pumping. The scenarios simulate a range of total and seasonal pumping rates potentially linked to site activities. Scenario 1 simulates two wells completed in the Cambrian-Ordovician aquifer system, each pumping for 8 months at 300 gal/min, followed by pumping for 4 months at 600 gal/min. The average yearly pumping rate of Scenario 1 is 800 gal/min. Scenario 2 simulates three wells completed in the Cambrian-Ordovician aquifer system pumping for 8 months at 200 gal/min, followed by pumping for 4 months at 500 gal/min. The average yearly pumping rate of Scenario 2 is 900 gal/min. The Pumping Test model simulations confirmed that drawdown in the boreholes of the pumping wells at the selected 2-well or 3-well rates will meet the desired condition that the pumping water level remains at least 100 ft above the highest Cambrian-Ordovician unit open to the well. The LMB modified model was used to evaluate the regional drawdown of the pumping from the confined COAS under the same 2-well and 3-well scenarios. At the nearest known existing COAS well, Campbellsport production well #4, the simulated drawdown for Scenario 1 after 20 years of cyclical pumping with two pumping wells averaging a total of 800 gal/min is 16.9 ft, whereas the simulated drawdown for Scenario 2 after 20 years of pumping with three pumping wells averaging a total of 900 gal/min is 19.0 ft. The total deep aquifer thickness at the Campbellsport location is on the order of 620 ft, meaning that the simulated drawdown for either scenario is about 3 percent of the confined aquifer thickness. The models developed as part of this project are archived in the project data release. The archive includes the model input and output files as well as MODFLOW source code and executables. ( Haserodt and others, 2017 ).

Wisconsin↗
USGS2017

Ice duration drives winter nitrate accumulation in north temperate lakes

The duration of winter ice cover on lakes varies substantially with climate variability, and has decreased over the last several decades in many temperate lakes. However, little is known of how changes in seasonal ice cover may affect biogeochemical processes under ice. We examined winter nitrogen (N) dynamics under ice using a 30+ yr dataset from five oligotrophic/mesotrophic north temperate lakes to determine how changes in inorganic N species varied with ice duration. Nitrate accumulated during winter and was strongly related to the number of days since ice-on. Exogenous inputs accounted for less than 3% of nitrate accumulation in four of the five lakes, suggesting a paramount role of nitrification in regulating N transformation and the timing of chemical conditions under ice. Winter nitrate accumulation rates ranged from 0.15 μ g N L −1 d −1 to 2.7 μ g N L −1 d −1 (0.011–0.19 μ M d −1 ), and the mean for intermediate depths was 0.94 μ g N L −1 d −1 (0.067 μ M d −1 ). Given that winters with shorter ice duration (< 120 d) have become more frequent in these lakes since the late 1990s, peak winter nitrate concentrations and cumulative nitrate production under ice may be declining. As ice extent and duration change, the physical and chemical conditions supporting life will shift. This research suggests we may expect changes in the form and amount of inorganic N, and altered dissolved nitrogen : phosphorus ratios, in lakes during winters with shorter ice duration.

Limnology and Oceanography Letters↗