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.
MODFLOW-NWT – Robust handling of dry cells using a Newton Formulation of MODFLOW-2005
MODFLOW-style parameters in underdetermined parameter estimation
Selected approaches to estimate water-budget components of the High Plains, 1940 through 1949 and 2000 through 2009
Groundwater availability of the Mississippi embayment
Application of the Local Grid Refinement package to an inset model simulating the interaction of lakes, wells, and shallow groundwater, northwestern Waukesha County, Wisconsin
Simulation of the shallow groundwater-flow system near Mole Lake, Forest County, Wisconsin
Using models for the optimization of hydrologic monitoring
cloudPEST - A python module for cloud-computing deployment of PEST, a program for parameter estimation
Water availability and use pilot: A multiscale assessment in the U.S. Great Lakes Basin
Approaches to highly parameterized inversion: Pilot-point theory, guidelines, and research directions
Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration
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: 115MODFLOW-NWT – Robust handling of dry cells using a Newton Formulation of MODFLOW-2005
The first versions of the widely used groundwater flow model MODFLOW (McDonald and Harbaugh 1988) had a sure but inflexible way of handling unconfined finite-difference aquifer cells where the water table dropped below the bottom of the cell—these "dry cells" were turned inactive for the remainder of the simulation. Problems with this formulation were easily seen, including the potential for inadvAuthorsRandal J. Hunt, Daniel T. FeinsteinMODFLOW-style parameters in underdetermined parameter estimation
In this article, we discuss the use of MODFLOW-Style parameters in the numerical codes MODFLOW_2005 and MODFLOW_2005-Adjoint for the definition of variables in the Layer Property Flow package. Parameters are a useful tool to represent aquifer properties in both codes and are the only option available in the adjoint version. Moreover, for overdetermined parameter estimation problems, the parameterAuthorsMarco D. D'Oria, Michael N. FienenSelected approaches to estimate water-budget components of the High Plains, 1940 through 1949 and 2000 through 2009
The High Plains aquifer, underlying almost 112 million acres in the central United States, is one of the largest aquifers in the Nation. It is the primary water supply for drinking water, irrigation, animal production, and industry in the region. Expansion of irrigated agriculture throughout the past 60 years has helped make the High Plains one of the most productive agricultural regions in the NaAuthorsJennifer S. Stanton, Sharon L. Qi, Derek W. Ryter, Sarah E. Falk, Natalie A. Houston, Steven M. Peterson, Stephen M. Westenbroek, Scott C. ChristensonGroundwater availability of the Mississippi embayment
Groundwater is an important resource for agricultural and municipal uses in the Mississippi embayment. Arkansas ranks first in the Nation for rice and third for cotton production, with both crops dependent on groundwater as a major source of irrigation requirements. Multiple municipalities rely on the groundwater resources to provide water for industrial and public use, which includes the city ofAuthorsBrian R. Clark, Rheannon M. Hart, Jason J. GurdakApplication of the Local Grid Refinement package to an inset model simulating the interaction of lakes, wells, and shallow groundwater, northwestern Waukesha County, Wisconsin
Groundwater use from shallow, high-capacity wells is expected to increase across southeastern Wisconsin in the next decade (2010-2020), owing to residential and business growth and the need for shallow water to be blended with deeper water of lesser quality, containing, for example, excessive levels of radium. However, this increased pumping has the potential to affect surface-water features. A prAuthorsD. T. Feinstein, C. P. Dunning, P. F. Juckem, R. J. HuntSimulation of the shallow groundwater-flow system near Mole Lake, Forest County, Wisconsin
The shallow groundwater system near Mole Lake, Forest County, Wis. was simulated using a previously calibrated regional model. The previous model was updated using newly collected water-level measurements and refinements to surface-water features. The updated model was then used to calculate the area contributing recharge for one existing and two proposed pumping locations on lands of the SokaogonAuthorsMichael N. Fienen, Paul F. Juckem, Randall J. HuntUsing models for the optimization of hydrologic monitoring
Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interestAuthorsMichael N. Fienen, Randall J. Hunt, John E. Doherty, Howard W. ReevescloudPEST - A python module for cloud-computing deployment of PEST, a program for parameter estimation
This report documents cloudPEST-a Python module with functions to facilitate deployment of the model-independent parameter estimation code PEST on a cloud-computing environment. cloudPEST makes use of low-level, freely available command-line tools that interface with the Amazon Elastic Compute Cloud (EC2(TradeMark)) that are unlikely to change dramatically. This report describes the preliminary seAuthorsMichael N. Fienen, Thomas C. Kunicki, Daniel E. KesterWater availability and use pilot: A multiscale assessment in the U.S. Great Lakes Basin
Beginning in 2005, water availability and use were assessed for the U.S. part of the Great Lakes Basin through the Great Lakes Basin Pilot of a U.S. Geological Survey (USGS) national assessment of water availability and use. The goals of a national assessment of water availability and use are to clarify our understanding of water-availability status and trends and improve our ability to forecast tAuthorsHoward W. ReevesApproaches to highly parameterized inversion: Pilot-point theory, guidelines, and research directions
Pilot points have been used in geophysics and hydrogeology for at least 30 years as a means to bridge the gap between estimating a parameter value in every cell of a model and subdividing models into a small number of homogeneous zones. Pilot points serve as surrogate parameters at which values are estimated in the inverse-modeling process, and their values are interpolated onto the modeling domaiAuthorsJohn E. Doherty, Michael N. Fienen, Randall J. HuntApproaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration
Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly useAuthorsJohn E. Doherty, Randall J. Hunt - 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.