Lake surface water temperature prediction and changing characteristics analysis - A case study of 11 natural lakes in Yunnan-Guizhou Plateau

Abstract

Lake surface water temperature (LSWT) is an important factor for lake ecological environment. Under the influence of natural and human factors, LSWT is generally on the rise. LSWT is affected by lake’s geographical location, climate diversity, biodiversity and cultural diversity of the plateau. To this end, the study used the natural factor (near-surface air temperature) that characterized climate change, the human factors (impermeable surface area, gross domestic product, and population) that characterized human activities, and the physical factors that characterized the lake inherent properties (lake area, watershed area, and water storage) as the dataset, and a hybrid prediction model (ε-SVR-AHP-BPANN) was proposed by combining three algorithms which were ε-support vector regression (ε-SVR), analytic hierarchy process (AHP), and back propagation artificial neural network (BPANN). This model was used to estimate and simulate the LSWT of 11 lakes in the study area from January 2018 to December 2019. Then conducted on the variation characteristics of LSWT for 11 lakes from 2001 to 2019. The results showed that: (1) The hybrid prediction model had a strong predictive power with low error and high generalization (RMSELSWT-day = 0.11, RMSELSWT-night = 0.10, RLSWT-day2 = 0.77, RLSWT-night2 = 0.90). (2) The temporal change analysis showed that, overall, the change rate of LSWT-day showed a warm-cold-warm trend from north to south in Yunnan-Guizhou Plateau, while LSWT-night showed a warm-cold trend. (3) The spatial visualization analysis revealed that there was a general warming trend of LSWT, and the LSWT of northern Erhai Lake were higher than that in southern Erhai Lake, and the distribution of LSWT was regional with the change of latitude.