Contrasting Seasonal Chlorophyll-a Dynamics in Tibetan Plateau Lakes Driven by Climate-Dependent Thermal and Mixing Processes

The Tibetan Plateau (TP) hosts the world’s largest and highest-altitude plateau lake system, whose ecosystems are highly sensitive to climate change. Chlorophyll-a (Chl-a), as a key indicator of phytoplankton biomass and lake trophic status, exhibits pronounced changes in response to long-term climatic and environmental changes. However, knowledge of its seasonal variations in TP lakes remains limited due to a shortage of high-time-resolution analyses based on in situ observations and remote sensing inversion. In this study, we compiled in situ Chl-a data from 95 lakes and combined them with Moderate Resolution Imaging Spectroradiometer (MODIS) ocean-color bands (B8–B16) and the Climate Change Initiative (CCI) Chl-a product to develop a back-propagation neural network model specifically for oligotrophic plateau lakes. The model demonstrated excellent performance across training, testing, and independent validation datasets (R2 = 0.82–0.84; RMSE = 0.44–0.49 μg L−1), confirming its suitability for regional-scale retrievals. Independent external evaluation using 24 observations from 8 TP lakes excluded from model development yielded R2 = 0.85 and RMSE = 0.24 μg L−1. Using this model, we reconstructed Chl-a concentrations during the ice-free period (26 May to 9 December) from 2000 to 2022 for 163 lakes larger than 50 km2 and systematically analyzed their seasonal dynamics within the ice-free period and controlling factors. The mean Chl-a concentration across all study lakes was 1.43 μg L−1, with concentrations significantly higher in autumn (September–November) than in summer (June–August). Fuzzy c-means clustering classified the seasonal dynamics into two groups: Class I lakes (94 lakes, 60%), peaking in August and mainly located in the westerlies-dominated region (88%), were primarily influenced by air temperature, solar radiation, and water temperature; whereas Class II lakes (69 lakes, 40%), peaking in November and predominantly distributed in the monsoon-dominated region (72%), were mainly driven by wind speed and mixed-layer depth. From 2000 to 2022, the timing of Chl-a peaks exhibited contrasting trends, advancing in Class I lakes and delaying in Class II lakes. These results showed the ability of remote sensing to monitor seasonal Chl-a dynamics and regional differentiation in the TP lakes, and their underlying climatic and limnological drivers, as well as high-altitude lake responses to global climate change.