Climate change influences on algal bloom intensity in lakes in the Yangtze River Basin, China from 1985 to 2022

Simeng Wang; Boqiang Qin; Boqing Wen; Chang Huang

doi:10.1016/j.jhazmat.2025.139027

Climate change influences on algal bloom intensity in lakes in the Yangtze River Basin, China from 1985 to 2022

J Hazard Mater. 2025 Jun 23:495:139027. doi: 10.1016/j.jhazmat.2025.139027. Online ahead of print.

Authors

Simeng Wang¹, Boqiang Qin², Boqing Wen³, Chang Huang⁴

Affiliations

¹ School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China.
² School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China. Electronic address: qinbq@niglas.ac.cn.
³ School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China.
⁴ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China.

PMID: 40582154
DOI: 10.1016/j.jhazmat.2025.139027

Abstract

This study investigates the long-term influence of climate change on the spatiotemporal dynamics of harmful algal blooms in lakes larger than 10 km² across the Yangtze River Basin from 1985 to 2022. Using Landsat satellite imagery, we quantified bloom activity using three indices: annual average bloom area, maximum annual bloom area, and annual bloom frequency percentage, and assessed their relationships with climate drivers using a boosted regression tree model. Among the 90 lakes analysed, 40.00 % showed significant decadal decreases in annual average bloom area (p < 0.05), 55.56 % exhibited no significant change, and 4.44 % showed significant increases. While most small and medium-sized lakes (10-100 km²) displayed stable or decreasing trends, a subset of super-large lakes (>500 km²), including Lakes Taihu and Chaohu, exhibited increasing maximum bloom area trends under warmer and wetter conditions. Temperature emerged as the primary climatic driver, explaining 45.5 % of the variance in bloom proportion. Smaller lakes were more sensitive to temperature fluctuations, whereas larger lakes exhibited more persistent blooms, likely due to their complex hydrodynamics and catchment-scale influences. Interactions among temperature, wind speed, and precipitation minus evaporation further modulated bloom patterns, with two-way interaction strengths in the model peaking at 27.41. These findings underscore the need to integrate lake-specific climate sensitivity and nutrient management into adaptive bloom mitigation strategies under future climate scenarios.

Keywords: Algal blooms; Boosted Regression Tree; Machine learning; Yangtze River basin.