An efficient calibration-constrained Monte Carlo technique for evaluating model predictive error

Tonkin, M. and Doherty, J. (2008). An efficient calibration-constrained Monte Carlo technique for evaluating model predictive error. In: Jens Christian Refsgaard, Calibration and reliability in groundwater modelling : credibility of modelling ; proceedings of an International Conference on Calibration and Reliability in Groundwater Modelling : Credibility of Modelling (ModelCARE 2007). International Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007, Copenhagen, (76-82). 9 - 13 September 2007.

Author Tonkin, M.
Doherty, J.
Title of paper An efficient calibration-constrained Monte Carlo technique for evaluating model predictive error
Conference name International Conference on Calibration and Reliability in Groundwater Modelling: Credibility of Modelling, ModelCARE2007
Conference location Copenhagen
Conference dates 9 - 13 September 2007
Proceedings title Calibration and reliability in groundwater modelling : credibility of modelling ; proceedings of an International Conference on Calibration and Reliability in Groundwater Modelling : Credibility of Modelling (ModelCARE 2007)   Check publisher's open access policy
Journal name IAHS Proceedings and Reports   Check publisher's open access policy
Place of Publication Wallingford, Oxon, United Kingdom
Publisher I A H S Press
Publication Year 2008
Year available 2008
Sub-type Fully published paper
Open Access Status
ISBN 9781901502497
ISSN 0144-7815
Editor Jens Christian Refsgaard
Issue 320
Start page 76
End page 82
Total pages 7
Collection year 2009
Language eng
Abstract/Summary We describe a new Monte Carlo (MC) technique that reduces the computational burden of calibration-constrained MC using the concept of the calibration null space. In the new MC approach, the model is calibrated using a subspace regularization method such as Truncated Singular Value Decomposition (TSVD) or the hybrid Tikhonov-TSVD approach described by Tonkin & Doherty (2005). Next, a stochastic parameter field generator is used to produce many realizations of the parameter field. For each realization, a difference is formed between the stochastic field and the calibration field. This difference is projected onto the calibration null space determined through the calibration process, and added to the calibration field. If the model is no longer calibrated, the underlying field is re-estimated with the null-space-difference field "riding on its back". If this can be undertaken using pre-calculated sensitivities, conditioning may require only a very small number of model runs. The new MC approach can rapidly produce a large number of conditioned stochastic fields, for use in assessing the potential error in a wide range of predictions. Copyright
Subjects 1900 Earth and Planetary Sciences
Keyword Calibration
Monte Carlo
Null space
Predictive error
Stochastic
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: Advanced Water Management Centre Publications
 
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