Optimizing Lake Surface Water Temperature Simulations Over Large Lakes in China With FLake Model

Abstract

Lake surface water temperature (LSWT) is sensitive to climate change; however, simulated LSWT and its response to climate change remain uncertain. In this study, FLake, a one-dimensional freshwater lake model, is optimized to simulate the LSWTs of 94 large lakes with surface areas greater than 100 km2 in China. While most of these lakes are seasonally ice-covered over the Tibetan Plateau, FLake with default parameters significantly underestimated LSWT in spring and winter and slightly overestimated LSWT in summer and autumn in seasonally ice-covered lakes. We performed sensitivity experiments and calibration in the trial lake (Qinghai Lake). Then, parameter calibrations of three lake-specific properties (albedo, lake mean depth and light extinction coefficient) were performed in all the studied lakes. The optimized FLake substantially improved the simulations of seasonal and interannual variations in LSWT. The root mean square error decreased from 3.64 ± 1.54°C to 1.97 ± 0.72°C, and the mean bias of 96% of the lakes decreased to less than 1°C. Our study showed that the optimized FLake can reproduce the temporal variations in LSWT across China with optimized parameters, providing the possibility to simulate and project the response of LSWT to rapid climate change.