Spatiotemporal dynamics and drivers of algal blooms in chinese lakes: A multi-phenological index analysis

Algal blooms (AB) caused by eutrophication pose a global threat to freshwater ecosystems and human water security. Existing studies lack standardized definitions and methodologies for phenological indicators, hindering quantitative comparisons and comprehensive spatial heterogeneity assessments. Using daily MODIS data (2000–2023), this study systematically retrieved three phenological features (Frequency, Persistence and Coverage) with nine indicators for 359 lakes (>10 km²) across four major lake zones in China (excluding the Tibetan Plateau). Spatiotemporal analysis, hierarchical clustering, and driving factor analysis were integrated to elucidate AB dynamics. Key findings include: (1) 63.3 % (228) of lakes experienced AB, among which 46.9 % showing significant increasing trends. Spatial heterogeneity revealed earlier outbreaks in southern lakes, prolonged persistence in central regions, and higher frequency in smaller lakes, and all three features exhibited upward trend. (2) Hierarchical clustering identified four lake types: Type 1 (small area, short persistence, frequent severe AB), Type 2 (large area, minimal frequency, persistence and coverage), Type 3 (medium-large area, high frequency and long persistence), and Type 4 (small-medium area, highest frequency and coverage), with spatial distributions linked to climate and human activities. (3) Natural factors (temperature and precipitation) dominated Frequency and Persistence (temperature-driven in Types 1 and 3; precipitation-driven in Type 4), while Coverage was primarily influenced by human activities (cropland and population density), except in Type 4. This multi-phenological framework clarifies spatiotemporal patterns and drivers of AB in Chinese lakes, offering scientific insights for ecological protection and water quality management.