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.
Bridging groundwater models and decision support with a Bayesian network
Development of a numerical model to simulate groundwater flow in the shallow aquifer system of Assateague Island, Maryland and Virginia
Simulation of the shallow groundwater-flow system in the Forest County Potawatomi Community, Forest County, Wisconsin
Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions
Potential effects of climate change on inland glacial lakes and implications for lake-dependent biota in Wisconsin: final report April 2013
Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization
Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks
Development and application of a groundwater/surface-water flow model using MODFLOW-NWT for the Upper Fox River Basin, southeastern Wisconsin
Approaches in highly parameterized inversion - GENIE, a general model-independent TCP/IP run manager
Watershed scale response to climate change--Black Earth Creek Basin, Wisconsin
Integrated watershed-scale response to climate change for selected basins across the United States
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: 115Bridging groundwater models and decision support with a Bayesian network
Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability makeAuthorsMichael N. Fienen, John P. Masterson, Nathaniel G. Plant, Benjamin T. Gutierrez, E. Robert ThielerDevelopment of a numerical model to simulate groundwater flow in the shallow aquifer system of Assateague Island, Maryland and Virginia
A three-dimensional groundwater-flow model was developed for Assateague Island in eastern Maryland and Virginia to simulate both groundwater flow and solute (salt) transport to evaluate the groundwater system response to sea-level rise. The model was constructed using geologic and spatial information to represent the island geometry, boundaries, and physical properties and was calibrated using anAuthorsJohn P. Masterson, Michael N. Fienen, Dean B. Gesch, Carl S. CarlsonSimulation of the shallow groundwater-flow system in the Forest County Potawatomi Community, Forest County, Wisconsin
The shallow groundwater system in the Forest County Potawatomi Comminity, Forest County, Wisconsin, was simulated by expanding and recalibrating a previously calibrated regional model. The existing model was updated using newly collected water-level measurements, inclusion of surface-water features beyond the previous near-field boundary, and refinements to surface-water features. The updated modeAuthorsMichael N. Fienen, David A. Saad, Paul F. JuckemApproaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions
The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability thAuthorsMichael N. Fienen, Marco D'Oria, John E. Doherty, Randall J. HuntPotential effects of climate change on inland glacial lakes and implications for lake-dependent biota in Wisconsin: final report April 2013
The economic vitality and quality of life of many northern Wisconsin communities is closely associated with the ecological condition of the abundant water resources in the region. Climate change models predict warmer temperatures, changes to precipitation patterns, and increased evapotranspiration in the Great Lakes region. Recently (1950-2006), many regions of Wisconsin have experienced warminAuthorsMichael W. Meyer, John F. Walker, Kevin P. Kenow, Paul W. Rasmussen, Paul J. Garrison, Paul C. Hanson, Randall J. HuntApproaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization
The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetiAuthorsStephen M. Westenbroek, John Doherty, John F. Walker, Victor A. Kelson, Randall J. Hunt, Timothy B. CeraApproaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks
Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensiveAuthorsMarinko Karanovic, Christopher T. Muffels, Matthew J. Tonkin, Randall J. HuntDevelopment and application of a groundwater/surface-water flow model using MODFLOW-NWT for the Upper Fox River Basin, southeastern Wisconsin
The Fox River is a 199-mile-long tributary to the Illinois River within the Mississippi River Basin in the states of Wisconsin and Illinois. For the purposes of this study the Upper Fox River Basin is defined as the topographic basin that extends from the upstream boundary of the Fox River Basin to a large wetland complex in south-central Waukesha County called the Vernon Marsh. The objectives forAuthorsD. T. Feinstein, M.N. Fienen, J.L. Kennedy, C.A. Buchwald, M.M. GreenwoodApproaches in highly parameterized inversion - GENIE, a general model-independent TCP/IP run manager
GENIE is a model-independent suite of programs that can be used to generally distribute, manage, and execute multiple model runs via the TCP/IP infrastructure. The suite consists of a file distribution interface, a run manage, a run executer, and a routine that can be compiled as part of a program and used to exchange model runs with the run manager. Because communication is via a standard protocoAuthorsChristopher T. Muffels, Willem A. Schreuder, John E. Doherty, Marinko Karanovic, Matthew J. Tonkin, Randall J. Hunt, David E. WelterWatershed scale response to climate change--Black Earth Creek Basin, Wisconsin
General Circulation Model simulations of future climate through 2099 project a wide range of possible scenarios. To determine the sensitivity and potential effect of long-term climate change on the freshwater resources of the United States, the U.S. Geological Survey Global Change study, "An integrated watershed scale response to global change in selected basins across the United States" was startAuthorsRandall J. Hunt, John F. Walker, Steven M. Westenbroek, Lauren E. Hay, Steven L. MarkstromIntegrated watershed-scale response to climate change for selected basins across the United States
A study by the U.S. Geological Survey (USGS) evaluated the hydrologic response to different projected carbon emission scenarios of the 21st century using a hydrologic simulation model. This study involved five major steps: (1) setup, calibrate and evaluated the Precipitation Runoff Modeling System (PRMS) model in 14 basins across the United States by local USGS personnel; (2) acquire selected simuAuthorsSteven L. Markstrom, Lauren E. Hay, D. Christian Ward-Garrison, John C. Risley, William A. Battaglin, David M. Bjerklie, Katherine J. Chase, Daniel E. Christiansen, Robert W. Dudley, Randall J. Hunt, Kathryn M. Koczot, Mark C. Mastin, R. Steven Regan, Roland J. Viger, Kevin C. Vining, John F. Walker - 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.