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Towards uncertainty quantification and parameter estimation for Earth system models in a component-based modeling framework
- Peckham, Scott D., Kelbert, Anna, Hill, Mary C., Hutton, Eric W.H.
- Computers & geosciences 2016 v.90 pp. 152-161
- Earth system science, algorithms, computers, mathematical models, observational studies, rivers, uncertainty
- Component-based modeling frameworks make it easier for users to access, configure, couple, run and test numerical models. However, they do not typically provide tools for uncertainty quantification or data-based model verification and calibration. To better address these important issues, modeling frameworks should be integrated with existing, general-purpose toolkits for optimization, parameter estimation and uncertainty quantification.This paper identifies and then examines the key issues that must be addressed in order to make a component-based modeling framework interoperable with general-purpose packages for model analysis. As a motivating example, one of these packages, DAKOTA, is applied to a representative but nontrivial surface process problem of comparing two models for the longitudinal elevation profile of a river to observational data. Results from a new mathematical analysis of the resulting nonlinear least squares problem are given and then compared to results from several different optimization algorithms in DAKOTA.