Influence of numerical precision on the calibration of AEM-based groundwater flow models

Groundwater modelers have embraced the use of automated calibration tools based on classical nonlinear regression techniques. While clearly an improvement over trial-and-error calibration, it is not clear to what extent these popular inverse modeling tools yield accurate parameter sets for groundwater flow models. The impact of model configuration and precision upon automated parameter estimation is also unclear. An extensive set of numerical experiments was performed to explore the influence of model configuration on the calibration of a regional groundwater flow model developed using the analytic element method. The results provided insight into the manner in which the specified level of model precision and the location of observation points influence the results of inverse modeling based on nonlinear regression. While the importance of these issues is application-specific, obtaining an accurate model calibration for the case study required both a careful placement of test observations and a greater-than-anticipated level of model precision. The required level of model precision for calibration was more than necessary to produce an acceptable flow solution.

[1]  John Doherty,et al.  Ground Water Model Calibration Using Pilot Points and Regularization , 2003, Ground water.

[2]  Victor A Kelson,et al.  Improving a Regional Model Using Reduced Complexity and Parameter Estimation , 2002, Ground water.

[3]  C. Bolster,et al.  Influence of Calibration Methodology on Ground Water Flow Predictions , 2004, Ground water.

[4]  Mary C. Hill,et al.  Methods and Guidelines for Effective Model Calibration , 2000 .

[5]  Warit Silavisesrith,et al.  Enhancement of aquifer vulnerability indexing using the analytic-element method , 2003 .

[6]  Richard L. Cooley,et al.  Confidence Intervals for Ground‐Water Models Using Linearization, Likelihood, and Bootstrap Methods , 1997 .

[7]  E. Poeter,et al.  Documentation of UCODE; a computer code for universal inverse modeling , 1998 .

[8]  Randal J. Barnes,et al.  Model Calibration Techniques for Use with the Analytic Element Method , 1993 .

[9]  Richard M. Yager,et al.  Detecting influential observations in nonlinear regression modeling of groundwater flow , 1998 .

[10]  R. Hunt,et al.  Using groundwater temperature data to constrain parameter estimation in a groundwater flow model of a wetland system , 2002 .

[11]  Randal J. Barnes,et al.  High-order line elements in modeling two-dimensional groundwater flow , 1999 .

[12]  Steen Christensen,et al.  Prediction of Regional Ground Water Flow to Streams , 1998 .

[13]  C. Tiedeman,et al.  Methods for using groundwater model predictions to guide hydrogeologic data collection, with application to the Death Valley regional groundwater flow system , 2003 .

[14]  E. Poeter,et al.  Inverse Models: A Necessary Next Step in Ground‐Water Modeling , 1997 .