Wisconsin Modeling Center

Science Center Objects

The Wisconsin 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 Wisconsin 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 Wisconsin Modeling Center is funded 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 Wisconsin 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.

Figure showing the probability that an area on the land surface contributes to a spring
Visualizing uncertainty can help decision makers evaluate appropriate tradeoffs for protection of a resource. In this figure, the results of many model runs are summarized to show where there is low (blue) to high (red) probability that an area on the land surface contributes to a spring (USGS Water Resources Investigations Report 2000-4172).

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

  1. Provide advanced computing assistance to modelers to implement powerful new computational and analytical techniques
  2. Provide training to others so they can gain the skills necessary to apply to their projects in the future
  3. 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.

Animation showing simulated streamflows from an integrated groundwater/surface-water model
Simulated streamflows and groundwater seepage from an integrated groundwater/surface-water model in southcentral Wisconsin. The model takes inputs of daily precipitation and temperature, and simulates all components of the water cycle including evapotranspiration, groundwater flow, groundwater/surface water interactions, and streamflows. Pulses of streamflow and then recession can be seen following the storm events in mid-June 2001, indicated by the daily precipitation totals. A delayed response in groundwater seepage can be seen in the wetlands east of the watershed boundary. (USGS Scientific Investigations Report 2016-5091)

Graphs of climate-change scenario results for potential and actual evapotranspiration and soil moisture
Uncertainty example #1. Results from a groundwater-surface water model in a humid, temperate climate in northcentral Wisconsin.  An ensemble of global climate models and emission scenarios was run involving thousands of CPU hours.  This figure shows the range of the simulated evapotranspiration and soil moisture under potential changes to climate through the year 2100; the average of each emission scenario is shown with a colored line (USGS Scientific Investigations Report 2013-5159)

The Wisconsin 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 below, 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.
Graphs showing climate-change results for streamflow and groundwater recharge
Uncertainty example #2. Similarly, when a groundwater-surface water model from southcentral Wisconsin is run using advanced computing it can portray uncertainty in other outputs of interest such as groundwater recharge and streamflows. The red lines in Panels B and C represent current conditions. (USGS Scientific Investigations Report 2016-5091)

 

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.
Illustration showing a data-worth map, which, in this example, shows areas where groundwater modeling could reduce uncertainty.
Uncertainty example #3. Data worth analysis can help focus limited resources on the most effective field data collection, by identifying the data that would most model forecasts. In this example, several thousand potential monitoring well locations were evaluated for their ability to improve predictions of base flow in the Tyler Forks River, under the condition of dewatering from a proposed mine. Areas colored in orange indicate locations where groundwater level monitoring could reduce model prediction uncertainty by more than 50%. Two clusters of high data worth are evident. The largest cluster shows that knowledge of water levels between the proposed mine pit and the upstream reaches of the river is most valuable to predicting base flows near the proposed mine. A second cluster shows that there is also value in monitoring water levels between the Tyler Forks and Potato River, which acts as a competing sink. (USGS Scientific Investigations Report 2015-5162)