Resumen
Long-term streamflow simulations of the Land Surface Models (LSMs) are necessary for the comprehensive evaluation of hydrological responses to climate change. The high complexity and uncertainty in the LSM modelling require the model calibration to improve the simulation performance and stability. Objective functions are commonly used in the calibration process, and the choice of objective functions plays a crucial role in model performance identification. The Kling and Gupta Efficiency (KGE) has been widely used in the hydrological model calibration by the measure of the three components (variability, bias, and correlation) decomposed from the Nash and Sutcliffe Efficiency (NSE). However, there is a clear tendency of systematic errors in the peak flow and/or water balance of streamflow time series optimized by the KGE calibration when the correlation between simulations and observations is relatively low. For a more balanced optimal solution of the KGE, this study has proposed the adjusted KGE (aKGE) by substituting the normalized correlation score in the KGE. The proposed aKGE was assessed by long-term daily streamflow simulation results from the Common Land Model (CoLM) for the calibration (2000?2009) and validation (2010?2019) periods in the Nakdong River Watershed, Korea. The case study demonstrated that the aKGE calibration can improve the simulation performance of high flow and annual average flow with a slightly inferior correlation of flows compared with the KGE and NSE criteria.