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2016 - Model Calibration and Predictive Uncertainty Analysis using PEST

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Five-Day Course

Model Calibration and Predictive Uncertainty Analysis using PEST

1-5 February 2016

John Doherty

ECTS: 5

 

 

Topics covered on each day are listed below. It is anticipated that the days will be divided between workshops and practical sessions. However this can be varied according to participants’ desires.

Practical exercises will be provided so that attendees can gain experience in the use of PEST. However if course participants would prefer to work on their own models, and discuss these models with the presenter, that will be fine. In fact half of the last day will be set aside for just this purpose.

The course will be as informal as possible, with plenty of time provided for discussions on topics of interest, and for repeating parts of the course material that may not have been well understood on the first time through.


DAY 1: Introduction and Parameter Estimation Basics
• introductions
• outline for next five days
• basic statistics
• what is “calibration”?
• well-posed problems and ill-posed problems
• linear theory of well-posed inverse problems
• inferring parameter uncertainty in the well-posed context
• extension of theory to nonlinear models
• observation weighting
• prior information
• parameter nonuniqueness
• use of parameter bounds
• the Marquardt lambda
• analysis of residuals
• PEST and model-independence
• template and instruction files
• the PEST control file
• tuning PEST performance to the current inversion problem
• PEST, Parallel PEST and BEOPEST

DAY 2: Ill-Posed Problems and Highly Parameterized Inversion
• the nature of expert knowledge
• the need for regularization
• metrics for uniqueness
• brief discussion of geostatistics
• kriging as regularized inversion
• Tikhonov regularization
• use of pilot points as a device for spatial parameterization
• combining pilot points and regularization
• utility software to implement regularized inversion
• truncated singular value decomposition as a regularization device
• information transfer expressed through singular value decomposition
• model simplification as a regularization device
• PEST’s “SVD-assist” methodology
• the resolution matrix
• examples

DAY 3: A: Practical Groundwater Model Calibration
• use of PEST with MODFLOW, MT3D and SEAWAT
• coping with cell drying and re-wetting in MODFLOW
• pilot point emplacement guidelines
• strategies for steady state model calibration
• strategies for transient model calibration
• strategies for multi-layer model calibration
• handling uncertain boundary conditions
• calibration and hypothesis testing
• groundwater modeling utility support software available with PEST
• PEST and MODFLOW-USG
• the PLPROC utility

DAY 3: B: Practical Surface Water and Land Use Model Calibration
• lumped-parameter and distributed-parameter models
• decomposition of a flow time series into its components
• formulation of a multi-component objective function
• high-pass, low-pass and baseflow filtering
• regionalization of model parameters
• the role of expert knowledge
• regularization strategies
• simultaneous calibration of multiple models
• multiple optima and parameter nonuniqueness
• the TSPROC model postprocessor and pest preprocessor


DAY 4: Uncertainty and Sensitivity Analysis
• sensitivity analysis
• loss of detail incurred through model calibration
• the difference between “uncertainty” and “potential for error”
• linear propagation of uncertainty and error
• nonlinear predictive uncertainty and error variance analysis
• stochastic field generation
• calibration-constrained stochastic field generation
• calibration constrained Monte Carlo analysis
• Markov chain Monte Carlo
• null space Monte Carlo
• data worth analysis
• parameter contributions to predictive uncertainty
• examples


DAY 5: Working with Defective Models – and Conclusions
• expressing model defects mathematically
• the nature of structural noise
• ramifications for predictive uncertainty analysis
• surrogate roles taken by parameters and repercussions for model calibration
• prediction-specific calibration
• uncertainty quantification through hypothesis-testing
• model-based decision-making

Practical Sessions
Many sessions during the course will be devoted to workshops through which participants can gain experience in using PEST. Files and printed notes for one of these workshops will be installed on participants’ laptops during the first day of the course. Participants can then chose what workshop they would like to do next; workshop files and notes can then be transferred from the memory sticks provided at the beginning of the course.

Alternatively, participants may wish to download the following workshop from the PEST web pages at:

http://www.pesthomepage.org/

If so, it is suggested that documentation for this workshop be printed after downloading and brought to the course.

Another alternative for those who use Groundwater Vistas is to do the workshops that are provided with this software. If so, workshops notes should be printed out ahead of time and brought to the course.

Yet another alternative is to bring along your own model calibration dataset and work with that during the practical sessions, asking questions as required.

Memory Sticks
Memory sticks provided to course participants also include the following.
• latest versions of PEST and BEOPEST
• literature on PEST
• copies of all slideshows used in the course
• over 14 PEST workshops (with all files and associated workshop documentation)

 

Download programme above.


Participation
Enrolled Ph.D. students have first priority. Master students and other students will be considered if the course is not filled with Ph.D. students. Registrations from non-PhD-students will be considered after Jan 25th.

If you come from outside the Copenhagen area:
Please postpone your purchase of air tickets and booking of hotel room until you have received a confirmation from us that the course will actually be held. We will inform applicants from abroad around Jan 4th. We will have to cancel the course if very few people sign up.

Travel and accommodation
The course is free to attend for Ph.D. students and other students.

Participants are expected to cover their own travel, food, and accommodation expenses. Coffee and tea will be served during the course.

Hotel suggestions:

Budget: Hotel CabInn Scandinavia.

Mid-range: Hotel Østerport.
(Tip: ask for superior room "away" from the train tracks)


Participants (January 26th):

  1. Julian Koch, University of Copenhagen
  2. Beatriz Revilla-Romero, University of Utrecht
  3. Weishi Wang, Potsdam Universivity
  4. Mads Steiness, University of Copenhagen
  5. Rena Meyer, University of Copenhagen
  6. Mehrdis Danapour, University of Copenhagen
  7. Lennart Ehlers, University of Copenhagen
  8. Sandip Chaudhary, University of Potsdam
  9. Sascha Müller, University of Copenhagen
  10. Salik Anders Rosing, University of Copenhagen
  11. Pernille Forsberg, University of Copenhagen
  12. Guohan Zhao, University of Copenhagen
  13. Mark Randall, University of Copenhagen
  14. Catharina Madsen, University of Copenhagen
  15. Majken Looms Zibar, University of Copenhagen
  16. Silvia Leirião, DHI
  17. Birgitte von Christierson, DHI
  18. Xiulan He, University of Copenhagen
  19. Maria Skjerbæk, University of Copenhagen
  20. Sachin Karan, COWI
  21. Sebastian Zastruzny, University of Copenhagen
  22. Kim Madsen, University of Copenhagen

 

 

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