Identification of Aquifer Parameters from Pumping Test Data with Regard for Uncertainty
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 7
Abstract
When fitting hydraulic models of groundwater flow to pumping test data, Bayesian inference provides a framework for quantifying the posterior uncertainty of aquifer parameters estimated from data and the most likely range of parameters that are consistent with the data. In this study, noise-perturbed drawdown data is measured. For clarity, groundwater models with few parameters are considered and Markov chain Monte Carlo is used to quantify uncertainty of transmissivity, storativity, and leakage parameters. These models exhibit many of the features typically encountered in much higher dimensional computational groundwater models like multimodality, failure of least squares algorithms, and poorly determined parameters. For comparison, Bayesian inference is contrasted with least squares model fitting.
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Acknowledgments
In conclusion, the authors would like to extend their gratitude to Hilary Lough for sharing pumping test data from her thesis and to Tiangang Cui for some very helpful discussions. Matlab code for the examples considered here may be downloaded from www.ocmo.co.nz.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 7 • July 2012
Pages: 769 - 781
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Jul 11, 2011
Accepted: Sep 30, 2011
Published online: Oct 6, 2011
Published in print: Jul 1, 2012
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