A Novel Framework for Calibration and Evaluation of Hydrological Models in Dynamic Catchments

Abstract. Hydrological models often face challenges in accurately simulating dynamic catchment processes due to structural deficiencies caused by oversimplifications. This results in compromised accuracy in capturing dynamic behaviours across different flow phases in seasonal catchments. To address this challenge, this study proposes a robust calibration framework that incorporates dynamic catchment characteristics. Additionally, the potential impacts of objective function configuration and sub-period calibration with dynamic parameters were investigated in this study. A pre-processing framework was developed to bridge models with catchment dynamics by clustering time series into sub-periods with similar hydrological processes. Seven calibration experiments were conducted to explore issues related to time-invariant parameters, objective function configurations, parameter correlations, dimensionality in global optimization, and abrupt parameter shifts. The experiments were conducted using the MOPEX dataset, which includes 219 basins across the United States, and were evaluated based on performance metrics, as well as state variables and fluxes. The recommended calibration scheme effectively addressed challenges in dynamic parameter operations, significantly improving model performance across different flow phases and enhancing the simulation in dynamic catchments. In conclusion, incorporating dynamic parameters based on extracted catchment characteristics effectively mitigates structural deficiencies in hydrological models. This approach improves simulation accuracy across different flow phases, reduces uncertainty, and enhances the model's ability to capture dynamic hydrological processes in seasonal catchments. Our findings provide a practical solution for calibrating hydrological models in seasonal catchments, contributing to better understanding of the hydrological cycle.