Comment on gmd-2022-303

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Over recent years, the dramatic hydrological variability has challenged the robustness of process-based hydrodynamic and water quality models to long-term continuous biogeochemical processes. 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 the 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 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 better in modeling total phosphorus (TP), chlorophyll <em>a</em> (Chl <em>a</em>), and dissolved oxygen (DO) with KGEs of 0.10, 0.30, and 0.74 respectively. Furthermore, to analyze the impact of different hydrological years as calibration periods on the model accuracy and robustness, we also calibrated the model in dry, normal, and wet years. The model calibrated in dry years overestimated DO concentrations, probably explained by the parameter of algal growth rate increased by 84 %. The model calibrated in wet years performed poorly for Chl <em>a</em> 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.