Abstract

Virtual experiments have been designed for the development and validation of coupled surface-subsurface modeling. Potentially, virtual experiments can guide model calibration as well. To address the role of virtual experiments in model calibration, we described an approach for a real-watershed calibration of Penn State Integrated Hydrologic Model (PIHM) guided by the V-shaped catchment simulation. We documented a benchmarking experiment of coupled surface-subsurface modeling on the V-shaped catchment. Then, we analyzed the performance of different hydrologic predictions for the V-shaped catchment, and calculated the correlations. The correlations were found stable, which had the potential to be used as the weights of multi-objective calibration. Therefore, we tested the weighted multi-objective calibration for a real-world watershed by transferring the correlations obtained from the virtual experiments. Expectedly, the parameters calibrated using the weighted approach indicated improvement of the model performance in simulating water table depths and evapotranspiration with little sacrifice of model performance in streamflow. In addition, this study also compares the weighted-average calibration and un-weighted calibration. The results demonstrate the weighted-objective optimization achieved satisfactory compromise for each calibration objective. Overall, the virtual experiment is proved to be an efficient tool to facilitate calibration of complex models.  The proposed weighted-objective approach provides an effective calibration strategy for the multiple observation constraints, which can be applied for the calibration of coupled environmental process models with multiple observations.

Citation

Yu, Xuan; Christopher Duffy; Yu Zhang; Gopal Bhatt; Yuning Shi (2016): Virtual experiments guide calibration strategies for a real-world watershed application of coupled surface-subsurface modeling . Journal of Hydrologic Engineering, 21 (11):. DOI: 10.1061/(ASCE)HE.1943-5584.0001431

This Paper/Book acknowledges NSF CZO grant support.