Practical Use of Computationally Frugal Model Analysis Methods |
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| Authors: | Mary C Hill Dmitri Kavetski Martyn Clark Ming Ye Mazdak Arabi Dan Lu Laura Foglia Steffen Mehl |
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| Affiliation: | 1. U.S. Geological Survey, Boulder, CO;2. University of Adelaide, Adelaide, Australia;3. National Center for Atmospheric Research, Boulder, CO;4. Florida State University, Tallahassee, FL;5. Colorado State University, Fort Collins, CO;6. Oak Ridge National Laboratory, Oak Ridge, TN;7. University of Darmstadt, Darmstadt, Germany;8. California State University, Chico, CA |
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| Abstract: | Three challenges compromise the utility of mathematical models of groundwater and other environmental systems: (1) a dizzying array of model analysis methods and metrics make it difficult to compare evaluations of model adequacy, sensitivity, and uncertainty; (2) the high computational demands of many popular model analysis methods (requiring 1000's, 10,000 s, or more model runs) make them difficult to apply to complex models; and (3) many models are plagued by unrealistic nonlinearities arising from the numerical model formulation and implementation. This study proposes a strategy to address these challenges through a careful combination of model analysis and implementation methods. In this strategy, computationally frugal model analysis methods (often requiring a few dozen parallelizable model runs) play a major role, and computationally demanding methods are used for problems where (relatively) inexpensive diagnostics suggest the frugal methods are unreliable. We also argue in favor of detecting and, where possible, eliminating unrealistic model nonlinearities—this increases the realism of the model itself and facilitates the application of frugal methods. Literature examples are used to demonstrate the use of frugal methods and associated diagnostics. We suggest that the strategy proposed in this paper would allow the environmental sciences community to achieve greater transparency and falsifiability of environmental models, and obtain greater scientific insight from ongoing and future modeling efforts. |
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