TC Chamberlin Modeling Center Active
Examples of where we work
The TC Chamberlin Modeling Center works nationally and internationally. These are footprints of some of the national models run at the Center.
Both Windows® and Linux® operating systems are increasingly brought to bear as shown by increases in the CPU hours for jobs run at the Center.
The Center specializes in combining commodity hardware such as desktop PCs and blade servers to solve problems that a single computer cannot.
The TC Chamberlin Modeling Center provides one-stop access to advanced computing so no project is limited by a lack of computer power. The Center can provide hardware access, assistance with migration and implementation, and training. We also develop, test, and disseminate state-of-the-art computational and analytical techniques and tools so models can be more effectively used in decision-making.
The TC Chamberlin Modeling Center is a founding partner of the USGS Advanced Computing Cooperative (ACC). The ACC provides one-stop access to advanced computing so that no work is limited by a lack of computer power. The TC Chamberlin Modeling Center is funded in part by the Core Science Systems Mission Area of the USGS to provide access to computer hardware and assistance for moving problems from local desktops to larger systems. Depending on the needs of the modeler, assistance can include implementing powerful new computational and analytical techniques, and training others in the skills needed for future modeling projects. Within the larger ACC, the TC Chamberlin Modeling Center also develops, tests, and disseminates state-of-the-art techniques and tools so that models are more effectively applied to today’s decision-making. Use the contact information on the overview tab to start the process to move your problem to advanced computing.
Problem
Modeling has become the language of quantitative scientific problem solving. Yet increases in computing power, parallel computing, and the availability of diverse sources of data have increased the complexity of models, and training on these new capabilities has not kept pace. As a result, it has become impractical for practitioners to develop expertise in the wide-ranging aspects of parallel computing, software customization, modeling, calibration, and uncertainty analysis.
Objectives
- Provide advanced computing assistance to modelers to implement powerful new computational and analytical techniques
- Provide training to others so they can gain the skills necessary to apply to their projects in the future
- Develop, implement, and disseminate state-of-the-art techniques and tools so that models are more effectively applied to today’s decision-making
Examples of Advanced Computing
A complex natural world means that there can never be certain that a simple representation such as a model captures its important characteristics. One way to handle uncertainty is to run a model many times where each run has slightly different model input. When finished, the runs can be summarized to identify which outcomes are likely and which are not. Likewise, visualization of model results can be importing for efficiently conveying what the many hours of model runs produced. Examples of these types of advanced computing outputs are shown here.
The TC Chamberlin Modeling Center has assisted projects throughout the world, including: Alabama, Arkansas, Arizona, California, Colorado, Connecticut, Florida, Hawaii, Illinois, Iowa, Maine, Massachusetts, Michigan, Minnesota, Mississippi, Montana, Nebraska, Nevada, North Dakota, Oregon, Oklahoma, Pennsylvania, South Carolina, South Dakota, Texas, Virginia, Washington, Wisconsin, Wyoming, the Great Lakes Basin, Native American tribes, Australia, Denmark, and Canada. Past work focuses primarily on water issues but also includes a range of models - from temperature to pathogens to populations. Examples of some of this work are shown in the figures on the right, along with some of our key publications.
BOOKS:
- Anderson, M.P., Woessner, W.W. and Hunt, R.J., 2015, Applied Groundwater Modeling: Simulation of Flow and Advective Transport (2nd Edition). Academic Press, Inc. 564 p. ISBN 9780120581030.
- Jakeman, A.J., Barreteau, O., Hunt, R.J., Rinaudo, J-D., and Ross, A., (editors) 2016, Integrated Groundwater Management: Concepts, Approaches, Challenges. Springer International Publishing, Switzerland. 953 p. ISBN 978-3-319-23575-2.
GUIDELINES AND SUGGESTED PRACTICES:
- Hunt, R.J., Anderson, M.P., and Kelson, V.A., 1998, Improving a complex finite difference groundwater-flow model through the use of an analytic element screening model. Groundwater 36(6), p.1011-1017.
- Anderson, M.P., Hunt, R.J., Krohelski, J.T., and Chung, K., 2002, Using high hydraulic conductivity nodes to simulate seepage lakes. Groundwater 40(2): 119-124.
- Kelson, V.A., Hunt, R.J., and Haitjema, H.M., 2002, Improving a regional model using reduced complexity and parameter estimation. Groundwater 40(2), p. 138-149.
- Hunt, R.J., Haitjema, H.M., Krohelski, J.T., and Feinstein, D.T., 2003, Simulating ground water-lake interactions: Approaches and insights, Groundwater 41(2): 227-237.
- Feinstein, D.T., Hart, D.J., and Krohelski, J.T., 2004, The value of long-term monitoring in the development of ground-water-flow models: USGS Fact Sheet 116-03, 4 p.
- Hunt, R.J., Doherty, J., and Tonkin, M.J., 2007, Are models too simple? Arguments for increased parameterization. Groundwater 45(3): 254-263.
- Hunt, R.J., Prudic, D.E., Walker, J.F., and Anderson, M.P., 2008, Importance of unsaturated zone flow for simulating recharge in a humid climate. Groundwater 46(4):551-560.
- Fienen, M.N., Muffels, C.T., and Hunt, R.J., 2009, On constraining pilot point calibration with regularization in PEST. Groundwater 47(6): 835-844.
- Hunt, R.J., Luchette, J., Schreüder, W.A., Rumbaugh, J.O., Doherty, J., Tonkin, M.J., and Rumbaugh, D.B., 2010a, Using a Cloud to replenish parched groundwater modeling efforts. Groundwater 48(3): 360-365.
- Fienen, M.N., Doherty, J.E., Hunt, R.J., and Reeves, H.W., 2010, Using prediction uncertainty analysis to design hydrologic monitoring networks—Example applications from the Great Lakes Water Availability Pilot Project: U.S. Geological Survey Scientific Investigations Report 2010–5159, 44 p.
- Doherty, J., Fienen, M.N., and Hunt, R.J., 2010, Approaches to Highly Parameterized Inversion: Pilot-point theory, guidelines, and research directions: U.S. Geological Survey Scientific Investigations Report 2010–5168, 36 p.
- Doherty, J., and Hunt, R.J., 2010, Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Groundwater-Model Calibration. U.S. Geological Survey Scientific Investigations Report 2010–5169, 59 p.
- Doherty, J., Hunt, R.J., and Tonkin, M.J., 2010, Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis. U.S. Geological Survey Scientific Investigations Report 2010–5211, 71 p.
- Barnett, B., Townley, L.R., Post, V., Evans, R.E., Hunt, R.J., Peeters, L., Richardson, S., Werner, A.D., Knapton, A. and Boronkay, A., 2012, Australian Groundwater Modelling Guidelines. Waterlines Report Series No. 82, National Water Commission, Canberra, Australia. 191 p. ISBN: 978-1-921853-91-3.
- Hunt, R.J., 2012, Uncertainty, pp. 92-105 in Australian Groundwater Modelling Guidelines. Waterlines Report Series No. 82, National Water Commission, Canberra, Australia. 191 p. ISBN: 978-1-921853-91-3.
- Fienen, M.N., and Hunt, R.J., 2015, High-Throughput Computing vs. High-Performance Computing for groundwater applications. Groundwater 53(2), p. 180-184. http://dx.doi.org/10.1111/gwat.12320.
- Hunt, R.J., 2017, Applied uncertainty. Groundwater 55(6), p. 771-772. http://dx.doi.org/10.1111/gwat.12604
- Erickson, R.A., Fienen, M.N., McCalla, S.G., Weiser, E.L., Bower, M.L., Knudson, J.M., and Thain, G., 2018, Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor. PLOS Computational Biology 14(10): e1006468. https://doi.org/10.1371/journal.pcbi.1006468
SOFTWARE DEVELOPED:
- Doherty, J., and Hunt, R.J., 2009, Two statistics for evaluating parameter identifiability and error reduction. Journal of Hydrology 366: 119-127.
- Westenbroek, S.M., Kelson, V.A., Dripps, W.R., Hunt, R.J., and Bradbury, K.R., 2010, SWB—A modified Thornthwaite-Mather Soil-Water-Balance code for estimating groundwater recharge: U.S. Geological Survey Techniques and Methods 6–A31, 60 p.
- Fienen, M.N., Kunicki, T.C., and Kester, D.E., 2011, cloudPEST – A python module for cloud-computing deployment of PEST, a program for parameter estimation. U.S. Geological Survey Open-File Report 2011-1062, 22 p.
- Westenbroek, S.M., Doherty, J.E., Walker, J.F., Kelson, V.A., Hunt, R.J., and Cera, T.B., 2012, Approaches in Highly Parameterized Inversion: TSPROC, A General Time-Series Processor to Assist in Model Calibration and Result Summarization. U.S. Geological Survey Techniques and Methods, Book 7, Section C7, 73 p.
- Welter, D.E., Doherty, J.E., Hunt, R.J., Muffels, C.T., Tonkin, M.J., and Schreüder, W.A., 2012, Approaches in Highly Parameterized Inversion: PEST++, A Parameter ESTimation Code Optimized For Large Environmental Models. U.S. Geological Survey Techniques and Methods, Book 7, Section C5, 47 p.
- Muffels, C.T., Schreüder, W.A., Doherty, J.E., Karanovic, M., Tonkin, M.J., Hunt, R.J., and Welter, D.E., 2012, Approaches in Highly Parameterized Inversion: GENIE, A General Model-Independent TCP/IP Run Manager. U.S. Geological Survey Techniques and Methods, Book 7, Section C6, 26 p.
- Karanovic, M., Muffels, C.T., Tonkin, M.J., and Hunt, R.J., 2012, Approaches in Highly Parameterized Inversion: PESTCommander, A Graphical User Interface for File and Run Management Across Networks. U.S. Geological Survey Techniques and Methods, Book 7, Section C8, 9 p.
- Fienen, M.N., D’Oria, Marco, Doherty, J.E., and Hunt, R.J., 2013, Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST—Documentation and instructions: U.S. Geological Survey Techniques and Methods, Book 7, Section C9, 86 p.
- Welter, D.E., White, J.T., Hunt, R.J., and Doherty, J.E. , 2015, Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models: U.S. Geological Survey Techniques and Methods, Book 7, Section C12, 54 p.
- Muffels, C.T., Hayes, D.A., Tonkin, M.J., and Hunt, R.J., 2015, GENIE Version 2 – A General Model-Independent TCP/IP Run: pp. 24-37 in Welter, D.E., White, J.T., Hunt, R.J., and Doherty, J.E., 2015, Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models: U.S. Geological Survey Techniques and Methods, Book 7, Section C12, 54 p.
- White, J.T., Fienen, M.N., and Doherty, J.E., 2016, A python framework for environmental model uncertainty analysis. Environmental Modelling and Software 85, p. 217-228. http://dx.doi.org/10.1016/j.envsoft.2016.08.017
- White, J. T., Fienen, M. N., Barlow, P. M., and Welter, D.E., 2017, A tool for efficient, model-independent management optimization under uncertainty. Environmental Modeling and Software. http://dx.doi.org/10.1016/j.envsoft.2017.11.019
- Westenbroek, S.M., Engott, J.A., Kelson, V.A., and Hunt, R.J., 2018, SWB Version 2.0—A soil-water-balance code for estimating net infiltration and other water-budget components: U.S. Geological Survey Techniques and Methods, book 6, chap. A59, 118 p., https://doi.org/10.3133/tm6A59
Below are publications associated with the TC Chamberlin Modeling Center.
Hydrology, nutrient concentrations, and nutrient yields in nearshore areas of four lakes in northern Wisconsin, 1999-2001
Flowpath delineation and ground water age, Allequash Basin, Wisconsin
Improving a regional model using reduced complexity and parameter estimation
Using high hydraulic conductivity nodes to simulate seepage lakes
Using groundwater temperature data to constrain parameter estimation in a groundwater flow model of a wetland system
Simulation of Fish, Mud, and Crystal Lakes and the shallow ground-water system, Dane County, Wisconsin
Hydrologic investigation of Powell Marsh and its relation to Dead Pike Lake, Vilas County, Wisconsin
Evaluating the effects of urbanization and land-use planning using ground-water and surface-water models
Delineating a recharge area for a spring using numerical modeling, Monte Carlo techniques, and geochemical investigation
Use of a watershed-modeling approach to assess hydrologic effects of urbanization, North Fork Pheasant Branch basin near Middleton, Wisconsin
The effects of large-scale pumping and diversion on the water resources of Dane County, Wisconsin
Simulation of the shallow hydrologic system in the vicinity of Middle Genesee Lake, Wisconsin, using analytic elements and parameter estimation
Below are data or web applications associated with the TC Chamberlin Modeling Center.
Below are partners associated with the TC Chamberlin Modeling Center.
- Overview
The TC Chamberlin Modeling Center provides one-stop access to advanced computing so no project is limited by a lack of computer power. The Center can provide hardware access, assistance with migration and implementation, and training. We also develop, test, and disseminate state-of-the-art computational and analytical techniques and tools so models can be more effectively used in decision-making.
The TC Chamberlin Modeling Center is a founding partner of the USGS Advanced Computing Cooperative (ACC). The ACC provides one-stop access to advanced computing so that no work is limited by a lack of computer power. The TC Chamberlin Modeling Center is funded in part by the Core Science Systems Mission Area of the USGS to provide access to computer hardware and assistance for moving problems from local desktops to larger systems. Depending on the needs of the modeler, assistance can include implementing powerful new computational and analytical techniques, and training others in the skills needed for future modeling projects. Within the larger ACC, the TC Chamberlin Modeling Center also develops, tests, and disseminates state-of-the-art techniques and tools so that models are more effectively applied to today’s decision-making. Use the contact information on the overview tab to start the process to move your problem to advanced computing.
Problem
Modeling has become the language of quantitative scientific problem solving. Yet increases in computing power, parallel computing, and the availability of diverse sources of data have increased the complexity of models, and training on these new capabilities has not kept pace. As a result, it has become impractical for practitioners to develop expertise in the wide-ranging aspects of parallel computing, software customization, modeling, calibration, and uncertainty analysis.Objectives
- Provide advanced computing assistance to modelers to implement powerful new computational and analytical techniques
- Provide training to others so they can gain the skills necessary to apply to their projects in the future
- Develop, implement, and disseminate state-of-the-art techniques and tools so that models are more effectively applied to today’s decision-making
Examples of Advanced Computing
A complex natural world means that there can never be certain that a simple representation such as a model captures its important characteristics. One way to handle uncertainty is to run a model many times where each run has slightly different model input. When finished, the runs can be summarized to identify which outcomes are likely and which are not. Likewise, visualization of model results can be importing for efficiently conveying what the many hours of model runs produced. Examples of these types of advanced computing outputs are shown here.
The TC Chamberlin Modeling Center has assisted projects throughout the world, including: Alabama, Arkansas, Arizona, California, Colorado, Connecticut, Florida, Hawaii, Illinois, Iowa, Maine, Massachusetts, Michigan, Minnesota, Mississippi, Montana, Nebraska, Nevada, North Dakota, Oregon, Oklahoma, Pennsylvania, South Carolina, South Dakota, Texas, Virginia, Washington, Wisconsin, Wyoming, the Great Lakes Basin, Native American tribes, Australia, Denmark, and Canada. Past work focuses primarily on water issues but also includes a range of models - from temperature to pathogens to populations. Examples of some of this work are shown in the figures on the right, along with some of our key publications.
BOOKS:
- Anderson, M.P., Woessner, W.W. and Hunt, R.J., 2015, Applied Groundwater Modeling: Simulation of Flow and Advective Transport (2nd Edition). Academic Press, Inc. 564 p. ISBN 9780120581030.
- Jakeman, A.J., Barreteau, O., Hunt, R.J., Rinaudo, J-D., and Ross, A., (editors) 2016, Integrated Groundwater Management: Concepts, Approaches, Challenges. Springer International Publishing, Switzerland. 953 p. ISBN 978-3-319-23575-2.
GUIDELINES AND SUGGESTED PRACTICES:
- Hunt, R.J., Anderson, M.P., and Kelson, V.A., 1998, Improving a complex finite difference groundwater-flow model through the use of an analytic element screening model. Groundwater 36(6), p.1011-1017.
- Anderson, M.P., Hunt, R.J., Krohelski, J.T., and Chung, K., 2002, Using high hydraulic conductivity nodes to simulate seepage lakes. Groundwater 40(2): 119-124.
- Kelson, V.A., Hunt, R.J., and Haitjema, H.M., 2002, Improving a regional model using reduced complexity and parameter estimation. Groundwater 40(2), p. 138-149.
- Hunt, R.J., Haitjema, H.M., Krohelski, J.T., and Feinstein, D.T., 2003, Simulating ground water-lake interactions: Approaches and insights, Groundwater 41(2): 227-237.
- Feinstein, D.T., Hart, D.J., and Krohelski, J.T., 2004, The value of long-term monitoring in the development of ground-water-flow models: USGS Fact Sheet 116-03, 4 p.
- Hunt, R.J., Doherty, J., and Tonkin, M.J., 2007, Are models too simple? Arguments for increased parameterization. Groundwater 45(3): 254-263.
- Hunt, R.J., Prudic, D.E., Walker, J.F., and Anderson, M.P., 2008, Importance of unsaturated zone flow for simulating recharge in a humid climate. Groundwater 46(4):551-560.
- Fienen, M.N., Muffels, C.T., and Hunt, R.J., 2009, On constraining pilot point calibration with regularization in PEST. Groundwater 47(6): 835-844.
- Hunt, R.J., Luchette, J., Schreüder, W.A., Rumbaugh, J.O., Doherty, J., Tonkin, M.J., and Rumbaugh, D.B., 2010a, Using a Cloud to replenish parched groundwater modeling efforts. Groundwater 48(3): 360-365.
- Fienen, M.N., Doherty, J.E., Hunt, R.J., and Reeves, H.W., 2010, Using prediction uncertainty analysis to design hydrologic monitoring networks—Example applications from the Great Lakes Water Availability Pilot Project: U.S. Geological Survey Scientific Investigations Report 2010–5159, 44 p.
- Doherty, J., Fienen, M.N., and Hunt, R.J., 2010, Approaches to Highly Parameterized Inversion: Pilot-point theory, guidelines, and research directions: U.S. Geological Survey Scientific Investigations Report 2010–5168, 36 p.
- Doherty, J., and Hunt, R.J., 2010, Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Groundwater-Model Calibration. U.S. Geological Survey Scientific Investigations Report 2010–5169, 59 p.
- Doherty, J., Hunt, R.J., and Tonkin, M.J., 2010, Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis. U.S. Geological Survey Scientific Investigations Report 2010–5211, 71 p.
- Barnett, B., Townley, L.R., Post, V., Evans, R.E., Hunt, R.J., Peeters, L., Richardson, S., Werner, A.D., Knapton, A. and Boronkay, A., 2012, Australian Groundwater Modelling Guidelines. Waterlines Report Series No. 82, National Water Commission, Canberra, Australia. 191 p. ISBN: 978-1-921853-91-3.
- Hunt, R.J., 2012, Uncertainty, pp. 92-105 in Australian Groundwater Modelling Guidelines. Waterlines Report Series No. 82, National Water Commission, Canberra, Australia. 191 p. ISBN: 978-1-921853-91-3.
- Fienen, M.N., and Hunt, R.J., 2015, High-Throughput Computing vs. High-Performance Computing for groundwater applications. Groundwater 53(2), p. 180-184. http://dx.doi.org/10.1111/gwat.12320.
- Hunt, R.J., 2017, Applied uncertainty. Groundwater 55(6), p. 771-772. http://dx.doi.org/10.1111/gwat.12604
- Erickson, R.A., Fienen, M.N., McCalla, S.G., Weiser, E.L., Bower, M.L., Knudson, J.M., and Thain, G., 2018, Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor. PLOS Computational Biology 14(10): e1006468. https://doi.org/10.1371/journal.pcbi.1006468
SOFTWARE DEVELOPED:
- Doherty, J., and Hunt, R.J., 2009, Two statistics for evaluating parameter identifiability and error reduction. Journal of Hydrology 366: 119-127.
- Westenbroek, S.M., Kelson, V.A., Dripps, W.R., Hunt, R.J., and Bradbury, K.R., 2010, SWB—A modified Thornthwaite-Mather Soil-Water-Balance code for estimating groundwater recharge: U.S. Geological Survey Techniques and Methods 6–A31, 60 p.
- Fienen, M.N., Kunicki, T.C., and Kester, D.E., 2011, cloudPEST – A python module for cloud-computing deployment of PEST, a program for parameter estimation. U.S. Geological Survey Open-File Report 2011-1062, 22 p.
- Westenbroek, S.M., Doherty, J.E., Walker, J.F., Kelson, V.A., Hunt, R.J., and Cera, T.B., 2012, Approaches in Highly Parameterized Inversion: TSPROC, A General Time-Series Processor to Assist in Model Calibration and Result Summarization. U.S. Geological Survey Techniques and Methods, Book 7, Section C7, 73 p.
- Welter, D.E., Doherty, J.E., Hunt, R.J., Muffels, C.T., Tonkin, M.J., and Schreüder, W.A., 2012, Approaches in Highly Parameterized Inversion: PEST++, A Parameter ESTimation Code Optimized For Large Environmental Models. U.S. Geological Survey Techniques and Methods, Book 7, Section C5, 47 p.
- Muffels, C.T., Schreüder, W.A., Doherty, J.E., Karanovic, M., Tonkin, M.J., Hunt, R.J., and Welter, D.E., 2012, Approaches in Highly Parameterized Inversion: GENIE, A General Model-Independent TCP/IP Run Manager. U.S. Geological Survey Techniques and Methods, Book 7, Section C6, 26 p.
- Karanovic, M., Muffels, C.T., Tonkin, M.J., and Hunt, R.J., 2012, Approaches in Highly Parameterized Inversion: PESTCommander, A Graphical User Interface for File and Run Management Across Networks. U.S. Geological Survey Techniques and Methods, Book 7, Section C8, 9 p.
- Fienen, M.N., D’Oria, Marco, Doherty, J.E., and Hunt, R.J., 2013, Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST—Documentation and instructions: U.S. Geological Survey Techniques and Methods, Book 7, Section C9, 86 p.
- Welter, D.E., White, J.T., Hunt, R.J., and Doherty, J.E. , 2015, Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models: U.S. Geological Survey Techniques and Methods, Book 7, Section C12, 54 p.
- Muffels, C.T., Hayes, D.A., Tonkin, M.J., and Hunt, R.J., 2015, GENIE Version 2 – A General Model-Independent TCP/IP Run: pp. 24-37 in Welter, D.E., White, J.T., Hunt, R.J., and Doherty, J.E., 2015, Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models: U.S. Geological Survey Techniques and Methods, Book 7, Section C12, 54 p.
- White, J.T., Fienen, M.N., and Doherty, J.E., 2016, A python framework for environmental model uncertainty analysis. Environmental Modelling and Software 85, p. 217-228. http://dx.doi.org/10.1016/j.envsoft.2016.08.017
- White, J. T., Fienen, M. N., Barlow, P. M., and Welter, D.E., 2017, A tool for efficient, model-independent management optimization under uncertainty. Environmental Modeling and Software. http://dx.doi.org/10.1016/j.envsoft.2017.11.019
- Westenbroek, S.M., Engott, J.A., Kelson, V.A., and Hunt, R.J., 2018, SWB Version 2.0—A soil-water-balance code for estimating net infiltration and other water-budget components: U.S. Geological Survey Techniques and Methods, book 6, chap. A59, 118 p., https://doi.org/10.3133/tm6A59
- Publications
Below are publications associated with the TC Chamberlin Modeling Center.
Filter Total Items: 115Hydrology, nutrient concentrations, and nutrient yields in nearshore areas of four lakes in northern Wisconsin, 1999-2001
The effects of shoreline development on water quality and nutrient yields in nearshore areas of four lakes in northern Wisconsin were investigated from October 1999 through September 2001. The study measured surface runoff and ground-water flows from paired developed (sites containing lawn, rooftops, sidewalks, and driveways) and undeveloped (mature and immature woods) catchments adjacent to fourAuthorsDavid J. Graczyk, Randall J. Hunt, Steven R. Greb, Cheryl A. Buchwald, James T. KrohelskiFlowpath delineation and ground water age, Allequash Basin, Wisconsin
An analysis of ground water flowpaths to a lake and creek in northern Wisconsin shows the flow system in a geologically simple basin dominated by lakes can be surprisingly complex. Differences in source area, i.e., lakes or terrestrial, combined with the presence of intervening lakes, which may or may not capture underflowing ground water as water moves downgradient from recharge areas, contributeAuthorsChristine D. Pint, Randall J. Hunt, Mary P. AndersonImproving a regional model using reduced complexity and parameter estimation
The availability of powerful desktop computers and graphical user interfaces for ground water flow models makes possible the construction of ever more complex models. A proposed copper-zinc sulfide mine in northern Wisconsin offers a unique case in which the same hydrologic system has been modeled using a variety of techniques covering a wide range of sophistication and complexity. Early in the pAuthorsVictor A. Kelson, Randall J. Hunt, Henk M. HaitjemaUsing high hydraulic conductivity nodes to simulate seepage lakes
In a typical ground water flow model, lakes are represented by specified head nodes requiring that lake levels be known a priori. To remove this limitation, previous researchers assigned high hydraulic conductivity (K) values to nodes that represent a lake, under the assumption that the simulated head at the nodes in the high-K zone accurately reflects lake level. The solution should also produceAuthorsMary P. Anderson, Randall J. Hunt, James T. Krohelski, Kuopo ChungUsing groundwater temperature data to constrain parameter estimation in a groundwater flow model of a wetland system
Parameter estimation is a powerful way to calibrate models. While head data alone are often insufficient to estimate unique parameters due to model nonuniqueness, flow‐and‐heat‐transport modeling can constrain estimation and allow simultaneous estimation of boundary fluxes and hydraulic conductivity. In this work, synthetic and field models that did not converge when head data were used did converAuthorsHector R. Bravo, Feng Jiang, Randall J. HuntSimulation of Fish, Mud, and Crystal Lakes and the shallow ground-water system, Dane County, Wisconsin
A new MODFLOW lake package (LAK3) that simulates ground-water/lake interaction was used in simulation of Fish, Mud and Crystal Lakes?three shallow seepage lakes located in northwestern Dane County, Wis. The simulations were done to help determine the cause of increasing lake stages and provide a tool to estimate the effect of pumping water from Fish lake on future lake stages. The ground-water-floAuthorsJames T. Krohelski, Yu-Feng Lin, William J. Rose, Randall J. HuntHydrologic investigation of Powell Marsh and its relation to Dead Pike Lake, Vilas County, Wisconsin
An analytic element ground-water-flow model was constructed to help understand the ground- and surface-water hydrology in the vicinity of Dead Pike Lake and Powell Marsh, Vilas County, Wisconsin. The model was used to simulate the effect of removing Powell Marsh control structures (ditches and Vista Pond) on the hydrology of Dead Pike Lake. Measurements and model simulation results show that grounAuthorsJames T. Krohelski, William J. Rose, Randall J. HuntEvaluating the effects of urbanization and land-use planning using ground-water and surface-water models
Why are the effects of urbanization a concern? As the city of Middleton, Wisconsin, and its surroundings continue to develop, the Pheasant Branch watershed (fig.l) is expected to undergo urbanization. For the downstream city of Middleton, urbanization in the watershed can mean increased flood peaks, water volume and pollutant loads. More subtly, it may also reduce water that sustains the ground-waAuthorsR. J. Hunt, J. J. SteuerDelineating a recharge area for a spring using numerical modeling, Monte Carlo techniques, and geochemical investigation
Recharge areas of spring systems can be hard to identify, but they can be critically important for protection of a spring resource. A recharge area for a spring complex in southern Wisconsin was delineated using a variety of complementary techniques. A telescopic mesh refinement (TMR) model was constructed from an existing regional-scale ground water flow model. This TMR model was formally optimizAuthorsR. J. Hunt, J. J. Steuer, M.T.C. Mansor, T.D. BullenUse of a watershed-modeling approach to assess hydrologic effects of urbanization, North Fork Pheasant Branch basin near Middleton, Wisconsin
The North Fork Pheasant Branch Basin in Dane County, Wisconsin is expected to undergo development. There are concerns that development will adversely affect water resources with increased flood peaks, increased runoff volumes, and increased pollutant loads. To provide a scientific basis for evaluating the hydrologic system response to development the Precipitation Runoff Modeling System (PRMS) wasAuthorsJeffrey J. Steuer, R. J. HuntThe effects of large-scale pumping and diversion on the water resources of Dane County, Wisconsin
Throughout many parts of the U.S., there is growing concern over the effects of rapid urban growth and development on water resources. Ground- water and surface-water systems (which comprise the hydrologic system) are linked in much of Wisconsin, and ground water can be utilized both for drinking water and as a source of water for sustaining lakes, streams, springs, and wetlands. Ground water is iAuthorsRandall J. Hunt, Kenneth R. Bradbury, James T. KrohelskiSimulation of the shallow hydrologic system in the vicinity of Middle Genesee Lake, Wisconsin, using analytic elements and parameter estimation
Middle Genesee Lake is a ground-water flow-through lake located in a developing area in southeastern Wisconsin. Because the lake is in good connection with the shallow ground-water system, hydrologic stresses to the shallow ground-water system could adversely affect the lake system. In order to assess the effects of potential stresses on the lake, a study was completed by the U.S. Geological SurveAuthorsR. J. Hunt, Y. Lin, J. T. Krohelski, P. F. Juckem - Web Tools
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