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.
Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling
High-throughput computing vs. high-performance computing for groundwater applications
Simulation of the regional groundwater-flow system of the Menominee Indian Reservation, Wisconsin
A model for evaluating stream temperature response to climate change in Wisconsin
Basin-scale simulation of current and potential climate changed hydrologic conditions in the Lake Michigan Basin, United States
A cross-validation package driving Netica with python
Numerical simulation of groundwater flow in the Columbia Plateau Regional Aquifer System, Idaho, Oregon, and Washington
Simulation of groundwater flow and interaction of groundwater and surface water on the Lac du Flambeau Reservation, Wisconsin
Simulated and observed 2010 flood-water elevations in selected river reaches in the Moshassuck and Woonasquatucket River Basins, Rhode Island
Hydrology and water quality of Shell Lake, Washburn County, Wisconsin, with special emphasis on the effects of diversion and changes in water level on the water quality of a shallow terminal lake
Effects of sea-level rise on barrier island groundwater system dynamics: ecohydrological implications
Temporal and spatial variability of groundwater recharge on Jeju Island, Korea
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: 115Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling
The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improvedAuthorsMagdalena Necpálová, Robert P. Anex, Michael N. Fienen, Stephen J. Del Grosso, Michael J. Castellano, John E. Sawyer, Javed Iqbal, Jose L. Pantoja, Daniel W. BarkerHigh-throughput computing vs. high-performance computing for groundwater applications
No abstract available.AuthorsMichael N. Fienen, Randall J. HuntSimulation of the regional groundwater-flow system of the Menominee Indian Reservation, Wisconsin
A regional, two-dimensional, steady-state groundwater-flow model was developed to simulate the groundwater-flow system and groundwater/surface-water interactions within the Menominee Indian Reservation. The model was developed by the U.S. Geological Survey (USGS), in cooperation with the Menominee Indian Tribe of Wisconsin, to contribute to the fundamental understanding of the region’s hydrogeologAuthorsPaul F. Juckem, Charles P. DunningA model for evaluating stream temperature response to climate change in Wisconsin
Expected climatic changes in air temperature and precipitation patterns across the State of Wisconsin may alter future stream temperature and flow regimes. As a consequence of flow and temperature changes, the composition and distribution of fish species assemblages are expected to change. In an effort to gain a better understanding of how climatic changes may affect stream temperature, an approacAuthorsJana S. Stewart, Stephen M. Westenbroek, Matthew G. Mitro, John D. Lyons, Leah E. Kammel, Cheryl A. BuchwaldBasin-scale simulation of current and potential climate changed hydrologic conditions in the Lake Michigan Basin, United States
The Great Lakes Restoration Initiative (GLRI) is the largest public investment in the Great Lakes in two decades. A task force of 11 Federal agencies developed an action plan to implement the initiative. The U.S. Department of the Interior was one of the 11 agencies that entered into an interagency agreement with the U.S. Environmental Protection Agency as part of the GLRI to complete scientific pAuthorsDaniel E. Christiansen, John F. Walker, Randall J. HuntA cross-validation package driving Netica with python
Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica isAuthorsMichael N. Fienen, Nathaniel G. PlantNumerical simulation of groundwater flow in the Columbia Plateau Regional Aquifer System, Idaho, Oregon, and Washington
A three-dimensional numerical model of groundwater flow was constructed for the Columbia Plateau Regional Aquifer System (CPRAS), Idaho, Oregon, and Washington, to evaluate and test the conceptual model of the system and to evaluate groundwater availability. The model described in this report can be used as a tool by water-resource managers and other stakeholders to quantitatively evaluate proposeAuthorsD. Matthew Ely, Erick R. Burns, David S. Morgan, John J. VaccaroSimulation of groundwater flow and interaction of groundwater and surface water on the Lac du Flambeau Reservation, Wisconsin
The Lac du Flambeau Band of Lake Superior Chippewa and Indian Health Service are interested in improving the understanding of groundwater flow and groundwater/surface-water interaction on the Lac du Flambeau Reservation (Reservation) in southwest Vilas County and southeast Iron County, Wisconsin, with particular interest in an understanding of the potential for contamination of groundwater supplyAuthorsPaul F. Juckem, Michael N. Fienen, Randall J. HuntSimulated and observed 2010 flood-water elevations in selected river reaches in the Moshassuck and Woonasquatucket River Basins, Rhode Island
Heavy persistent rains from late February through March 2010 caused severe flooding and set, or nearly set, peaks of record for streamflows and water levels at many long-term U.S. Geological Survey streamgages in Rhode Island. In response to this flood, hydraulic models were updated for selected reaches covering about 33 river miles in Moshassuck and Woonasquatucket River Basins from the most receAuthorsPhillip J. Zarriello, David E. Straub, Stephen M. WestenbroekHydrology and water quality of Shell Lake, Washburn County, Wisconsin, with special emphasis on the effects of diversion and changes in water level on the water quality of a shallow terminal lake
Shell Lake is a relatively shallow terminal lake (tributaries but no outlets) in northwestern Wisconsin that has experienced approximately 10 feet (ft) of water-level fluctuation over more than 70 years of record and extensive flooding of nearshore areas starting in the early 2000s. The City of Shell Lake (City) received a permit from the Wisconsin Department of Natural Resources in 2002 to divertAuthorsPaul F. Juckem, Dale M. RobertsonEffects of sea-level rise on barrier island groundwater system dynamics: ecohydrological implications
We used a numerical model to investigate how a barrier island groundwater system responds to increases of up to 60 cm in sea level. We found that a sea-level rise of 20 cm leads to substantial changes in the depth of the water table and the extent and depth of saltwater intrusion, which are key determinants in the establishment, distribution and succession of vegetation assemblages and habitat suiAuthorsJohn P. Masterson, Michael N. Fienen, E. Robert Thieler, Dean B. Gesch, Benjamin T. Gutierrez, Nathaniel G. PlantTemporal and spatial variability of groundwater recharge on Jeju Island, Korea
Estimates of groundwater recharge spatial and temporal variability are essential inputs to groundwater flow models that are used to test groundwater availability under different management and climate conditions. In this study, a soil water balance analysis was conducted to estimate groundwater recharge on the island of Jeju, Korea, for baseline, drought, and climate-land use change scenarios. TheAuthorsAlan Mair, Benjamin Hagedorn, Suzanne Tillery, Aly I. El-Kadi, Stephen M. Westenbroek, Kyoochul Ha, Gi-Won Koh - Web Tools
Below are data or web applications associated with the TC Chamberlin Modeling Center.
- Partners
Below are partners associated with the TC Chamberlin Modeling Center.