The Practical Use of Simplicity in Developing Ground Water Models

The advantages of starting with simple models and building complexity slowly can be significant in the development of ground water models. In many circumstances, simpler models are characterized by fewer defined parameters and shorter execution times. In this work, the number of parameters is used as the primary measure of simplicity and complexity; the advantages of shorter execution times also are considered. The ideas are presented in the context of constructing ground water models but are applicable to many fields. Simplicity first is put in perspective as part of the entire modeling process using 14 guidelines for effective model calibration. It is noted that neither very simple nor very complex models generally produce the most accurate predictions and that determining the appropriate level of complexity is an ill-defined process. It is suggested that a thorough evaluation of observation errors is essential to model development. Finally, specific ways are discussed to design useful ground water models that have fewer parameters and shorter execution times.

[1]  K. Popper.  The Open Universe , 1982 .

[2]  Frank Morrison,et al.  Sharp boundary inversion of 2D magnetotelluric data , 1999 .

[3]  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 .

[4]  Mary C. Hill,et al.  Predictive modeling of flow and transport in a two‐dimensional intermediate‐scale, heterogeneous porous medium , 2001 .

[5]  Arlen W. Harbaugh,et al.  MODFLOW-2000, the U.S. Geological Survey modular ground-water model; user guide to the observation, sensitivity, and parameter-estimation processes and three post-processing programs , 2000 .

[6]  M. C. Hill,et al.  MODFLOW-2000, the U.S. Geological Survey Modular Ground-Water Model -Documentation of the Hydrogeologic-Unit Flow (HUF) Package , 2000 .

[7]  Mary C. Hill,et al.  Hydrogeologic evaluation and numerical simulation of the Death Valley regional ground-water flow system, Nevada and California , 1997 .

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

[9]  B. Harrison.  Las Vegas, Nevada , 2002 .

[10]  R. Parker.  Geophysical Inverse Theory , 1994 .

[11]  R. Bras,et al.  Six myths about mathematical modeling in geomorphology , 2003 .

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

[13]  C. Tiedeman,et al.  Effective Groundwater Model Calibration: With Analysis of Data, Sensitivities, Predictions, and Uncertainty , 2007 .

[14]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[15]  Mary C. Hill,et al.  Death valley regional ground-water flow model calibration using optimal parameter estimation methods and geoscientific information systems , 1999 .

[16]  Heidi Christiansen Barlebo,et al.  Investigating the Macrodispersion Experiment (MADE) site in Columbus, Mississippi, using a three‐dimensional inverse flow and transport model , 2004 .

[17]  G. Fogg,et al.  Modeling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains , 1997 .

[18]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

[19]  William W.-G. Yeh,et al.  Aquifer parameter identification with optimum dimension in parameterization , 1981 .

[20]  Scott L. Painter,et al.  Stochastic simulation of radionuclide migration in discretely fractured rock near the Äspö Hard Rock Laboratory , 2004 .

[21]  D. M. Ely,et al.  Preliminary evaluation of the importance of existing hydraulic-head observation locations to advective-transport predictions, Death Valley regional flow system, California and Nevada , 2001 .

[22]  J. Doherty,et al.  A hybrid regularized inversion methodology for highly parameterized environmental models , 2005 .

[23]  Arlen W. Harbaugh,et al.  MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model - User Guide to Modularization Concepts and the Ground-Water Flow Process , 2000 .

[24]  Steven F. Carle,et al.  CONDITIONAL SIMULATION OF HYDROFACIES ARCHITECTURE: A TRANSITION PROBABILITY/MARKOV APPROACH1 , 1998 .

[25]  J. Jaime Gómez-Hernández,et al.  ISIM3D: and ANSI-C three-dimensional multiple indicator conditional simulation program , 1990 .

[26]  Timothy Scheibe,et al.  An Evaluation of Conditioning Data for Solute Transport Prediction , 2003, Ground water.