Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula

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