Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?

Abstract

Abstract. Autocalibration techniques have the potential to enhance the efficiency and accuracy of intricate process-based hydrodynamic and water quality models. In this study, we developed a new R-based autocalibration toolkit for the Environmental Fluid Dynamics Code (EFDC) and implemented it into the recalibration of the Yuqiao Reservoir Water Quality Model (YRWQM), with long-term observations from 2006 to 2015, including dry, normal, and wet years. The autocalibration toolkit facilitated recalibration and contributed to exploring how a model recalibrated with long-term observations performs more accurately and robustly. Previously, the original YRWQM was calibrated and validated with observations of dry years in 2006 and 2007, respectively. Compared to the original YRWQM, the recalibrated YRWQM performed just as well in water surface elevation, with a Kling–Gupta efficiency (KGE) of 0.99, and water temperature, with a KGE of 0.91, while performing better in modeling total phosphorus (TP), chlorophyll a (Chl a), and dissolved oxygen (DO), with KGEs of 0.10, 0.30, and 0.74, respectively. Furthermore, the KGEs improved by 43 %–202 % in modeling the TP–Chl a–DO process when compared to the models calibrated with only dry, normal, and wet years. The model calibrated in dry years overestimated DO concentrations, probably explained by the parameter of algal growth rate that increased by 84 %. The model calibrated in wet years performed poorly for Chl a, due to a 50 % reduction in the carbon-to-chlorophyll ratio, probably triggered by changes in the composition of the algal population. Our study suggests that calibrating process-based hydrodynamic and water quality models with long-term observations may be an important measure to improve the robustness of models under severe hydrological variability. The newly developed general automatic calibration toolkit and a possible hierarchical autocalibration strategy will also be a powerful tool for future complex model calibration.