Dr. Ming Ye

Associate Professor

Department of Scientific Computing
Florida State University

PhD, University of Arizona

Office: 489 Dirac Science Library
Phone: (850) 644-4587
Fax: (850) 644-0098
Email: mye@fsu.edu

Project Title:
Impact of Calibration Data on Evaluating Plausibility of Alternative Groundwater models

Sponsored by the NSF Hydrologic Sciences Program

Collaborators:

Graduate Student: Dan Lu

Post-doc: Liying Wang

Project Abstract:




















Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions regardless of the quantity and quality of available data. This recognition has led to a growing tendency among hydrologists to postulate several alternative hydrologic models for a site. Models here are not limited to governing equations and associated boundary/initial conditions, but refer to conceptualmathematical representations of hydrologic systems (e.g., their processes and interactions). Facing the alternative models, the scientific question to be answered is how to evaluate plausibility of the models so that the models can be properly used to yield optimum predictions. Evaluating model plausibility considers the entire modeling process (including model formulation, calibration, and validation), and calibration/validation data play a key role in the evaluation process. Although calibrating a single model has been studied for decades, impact of calibration data on evaluating plausibility of multiple models has not been well understood. Open questions are as follows: What kinds of calibration data can be used to most effectively discriminate between models? How many data are needed to reliably evaluate model plausibility? How does data correlation (spatial and temporal) affect the evaluation? How does biased evaluation of model plausibility influence predictive performance? These fundamental questions will be addressed using an interdisciplinary approach combining Bayesian statistical and computational methods. Model plausibility will be quantified using model probability, which is estimated, in a Bayesian framework, based on conformity of model simulations to calibration data, complexity of models, and expert judgment. Based on the model probability, one can choose a single model (i.e., Bayesian model selection) or use multiple models (i.e., Bayesian model averaging) to make predictions. Predictive performance of the Bayesian model selection or averaging will be investigated. Hypothesis will be tested using a two-pronged strategy based on both synthetic and realworld modeling. For the synthetic case, alternative groundwater models will be developed based on different representations of site heterogeneity and boundary conditions. The real-world modeling will be conducted at the Naturita site, Colorado, where a risk exists that uranium may reach the Colorado River. Alternative models will be developed based on different ways of formulations of uranium reactive transport models, such as surface complexation models with different numbers of functional groups. For the real-world modeling, model predictive performance will be evaluated using cross-validation methods such as leave-one-out and K-fold. The synthetic and realworld modeling will be conducted together with USGS scientists at Boulder and Menlo Park. Scientific insights gained in this project will be valuable to any environmental modeling through cost-effective data collection for refining existing models and developing new models for environmental restoration and protection.

Project Activities:

  • September 21 - 23, 2011: Dan attended SAMSI 2011-12 Workshop of Uncertainty Quantification Program: Geosciences Applications Opening Workshop.
  • June 7, 2011: Ming Ye gave an oral presentation entitled "Improving Estimation of Inter-basin Groundwater Flow into Northern Yucca Flat, Nevada National Security Site, Using Multi-models and Multi-kinds of Observations" at the MODFLOW and More 2011 Conferencethe. Download Presentation Slides
  • June 6, 2011: Dan Lu gave an oral presentation entitled "Analysis of Regression and Bayesian Predictive Uncertainty Measure" at the MODFLOW and More 2011 Conferencethe based on her work with Dr. Mary Hill at USGS Boulder.
  • December 16, 2010: Dan Lu gave an oral presentation entitled "A Controlled Experiment for Investigating Uncertainty Measures in Groundwater Flow Modeling" at the AGU annual meeting based on her work with Dr. Mary Hill at USGS Boulder. Download Presentation Slides
  • November 20, 2010: Together with Karl Pohlmann at the Desert Research Institute (DRI), we published a report entitled "Numerical Simulation of Inter-basin Groundwater Flow into Northern Yucca Flat, Nevada National Security Site.
  • August 10, 2010: Dan left for Boulder, Colorado to work with Dr. Mary Hill on development of the synthetic model.
  • April 5, 2010: Dr. Liying Wang joined our group as a post-doc working on this project. Liying's Profile

Conference Abstracts:

  • Ye, M., D. Lu, G.P. Curtis, P.D. Meyer, and S. Yabusaki (2010), Effect of Temporal Residual Correlation on Estimation of Model Averaging Weights, AGU Fall Meeting, December 13-17, San Francisco, California.
  • Lu, D. (student), M.C. Hill, and M. Ye (2010), A Controlled Experiment for Investigating Uncertainty Measures in Groundwater Flow Modeling, AGU Fall Meeting, December 13-17, San Francisco, California.
  • Neuman, S.P., M. Ye, L. Xue, and D. Lu (2010), Multimodel Bayesian Analysis of the Worth of Data, AGU Fall Meeting, December 13-17, San Francisco, California.
  • Ye, M. and D. Lu (2009), On model selection criteria and model complexity, Annual Meeting of the American Geophysical Unior, Dec. 14-18, San Francisco, CA.

Conference Proceedings:

  • Ye, M., D. Lu, S.P. Neuman, L. Xue (2011), Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs, International Conference on Groundwater: Our Source of Security in an Uncertain Future, September 19 - 21, 2011, Pretoria, South Africa.
  • Ye, M., L. Wang, and K.F. Pohlmann (2011), Evaluation of plausibility of alternative groundwater models using different kinds of observations, MODFLOW and More 2011Conference, June 5 - 8, 2011, Golden, CO.
  • Lu, D. (student), M.C. Hill, and M. Ye (2011), Analysis of regression and Bayesian predictive uncertainty measure, MODFLOW and More 2011Conference, June 5 - 8, 2011, Golden, CO.
  • Neuman, S.P., L. Xue, M. Ye, and D. Lu (2011), Multimodel Assessment of the Worth of Data Under Uncertainty, Waste Management Symposium, February 28 - March 3, 2011, Phoenix, Arizona.

Peer-Reviewed Journal Articles:

  • Gupta, H. V., M. P. Clark, J. A. Vrugt, G. Abramowitz, and M. Ye (2012), Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., doi:10.1029/2011WR011044, in press.
  • Lu, D., M. Ye, and M. C. Hill (2012), Analysis of regression confidence intervals and Bayesian credible in\| tervals for uncertainty quantification, Water Resour. Res., doi:10.1029/2011WR011289, in press.
  • Lu, D. (student), M. Ye, S.P. Neuman, and L. Xue (2012), Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs, Advances in Water Resources, 35, 69-82, DOI: 10.1016/j.advwatres.2011.10.007.
  • Lu, D. (student), M. Ye, S.P. Neuman, and L. Xue (2012), Multimodel Bayesian analysis of data-worth applied to unsaturated fractured tuffs, Advances in Water Resources, 35, 69-82, DOI: 10.1016/j.advwatres.2011.10.007.
  • Lu, D. (student), M. Ye, and S.P. Neuman (2011), Dependence of Bayesian model selection criteria and Fisher information ma trix on sample size, Mathematical Geosciences, 43(8), 971-993, DOI 10.1007/s11004-011-9359-0.
  • Neuman, S.P., L. Xue, M. Ye, and D. Lu (2011), Bayesian analysis of data-worth considering model and parameter uncertainties, Advances in Water Resources, doi:10.1016/j.advwatres.2011.02.007.
  • Ye, M., K.F. Pohlmann, J.B. Chapman, G.M. Pohll, and D.M. Reeves (2011), A model-averaging method for assessing groundwater conceptual model uncertainty, Ground Water, doi:10.1111/j.1745-6584.2009.00633.x.
  • Ye, M. (2010), MMA: A computer code for multi-model analysis, Ground Water, doi: 10.1111/i.1745-6584.2009.00647.x.
  • Ye, M., D. Lu, S.P. Neuman, and P.D. Meyer (2010), Comment on "Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window" by Frank T.-C. Tsai and Xiaobao Li, Water Resour. Res., 46, W02801, doi:10.1029/2009WR008501.