Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/27/2012
Publication Date: 9/4/2012
Citation: Flerchinger, G.N., Caldwell, T.G., Cho, J., Hardegree, S.P. 2012. Simultaneous heat and water model: Model use, calibration and validation. Transactions of the ASABE. 55(4):1395-1411.
Interpretive Summary: Computer simulation models are powerful management and research tools. Managers can use model to evaluate effects of various decision options, and researchers can use models to investigate process interactions that difficult to measure, test theories, and corroborate scientific observations. However, parameter values necessary for input to a computer model are not always known. When these parameters are critical to the simulated outcome, it may be necessary to calibrate the model by trying various combinations of parameter values to determine which gives the best fit to observed data. Once calibrated, the model must be validated by an independent data set to test the calibrated parameter values. This study describes calibration procedures for the Simultaneous Heat and Water (SHAW) model and investigates three different approaches that are often used for model calibration. Advantages and disadvantages of the three calibration approaches are discussed. This paper is part of a collection of papers that will serve as the basis to develop engineering standards for model calibration and validation.
Technical Abstract: A discussion of calibration and validation procedures used for the Simultaneous Heat and Water model is presented. Three calibration approaches are presented and compared for simulating soil water content. Approaches included a stepwise local search methodology, trial-and-error calibration, and an automated multi-objective parameter optimization algorithm. In the stepwise approach, parameters for each soil horizon were individually varied to determine which parameter could minimize the root mean square deviation (RMSD) between measured and simulated soil water content of the top 20 cm. Subsequently, all other parameters were varied while holding constant the parameter that minimized the RMSD in the previous iteration. Iterations continued until the RMSD was minimized. For the trial-and-error calibration, plots of simulated and measured soil water content were examined and soil parameters of each soil horizon or individual soil layers were varied to obtain a better fit and to minimize RMSD of the top 20 cm as well as the top 60 cm. The automated multi-objective parameter optimization algorithm searched throughout a feasible parameter space for parameter combinations that minimized each of several RMSD objective functions, and then effectively minimized the tradeoffs between the objective functions. Advantages and disadvantages of the three calibration approaches are discussed.