Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches.

The eutrophication of lakes and the subsequent algal blooms have become significant environmental issues of global concern in recent years. With ongoing global warming and intensifying human activities, water quality trends in lakes worldwide varied significantly, and the trend of algal blooms in the next few decades is unclear. However, there is a lack of comprehensive quantitative research on the future projection of lake algal blooms globally due to the scarcity of long-term algal blooms observational data and the complex nonlinear relationships between algal blooms and their driving factors. We aimed to develop a global projection model to evaluate the future trend in algal bloom occurrences in large lakes under various socio-economic development scenarios. We focused our research on 161 natural lakes worldwide, each exceeding 500 km. The results indicated that the Random Forest model performed best (Overall Accuracy: 0.9697, Kappa: 0.8721) among various machine learning models which were applied in this study. The predicted results showed that, by the end of this century, the number of lakes experiencing algal blooms and the intensity of these blooms will worsen under higher forcing scenarios (SSP370 and SSP585) (p < 0.05). In different regions, lakes with increasing algal blooms are mainly distributed in Africa, Asia, and North America, while lakes with decreasing occurrence are primarily found in Europe. Additionally, underdeveloped regions, such as Africa, exhibit greater sensitivity to different SSP scenarios due to high variability in population and economic growth. This study revealed the spatiotemporal distribution of algal blooms in global lakes from 2020 to 2100 and suggested that the intensifying algal blooms due to global warming and human activities may offset the effort of controlling the water quality.