Spatiotemporal Dynamics and Drivers of Spatiotemporal Dynamics and Drivers of 2014 to 2022

Phytoplankton blooms are a major global environmental issue, affecting aquatic ecosystems, aquaculture, food production, and water supply security. This study systematically investigated the spatiotemporal dynamics of phytoplankton blooms in Dongting Lake from 2014 to 2022 using Floating Algae Index (FAI) time-series data derived from Landsat imagery via the Google Earth Engine (GEE) platform. The research aimed to characterize bloom distribution patterns and assess the influence of environmental and meteorological drivers. Using multiple statistical and spatial methods—including Theil–Sen trend estimation, the Mann–Kendall test, the Hurst index, spatial autocorrelation, and geographic detector analysis—the study explored the nonlinear bivariate relationships underpinning bloom formation. Multiscale temporal analyses (daily, monthly, seasonal, and annual) provided a detailed understanding beyond conventional single-scale studies. The results indicated that algal blooms predominantly occurred in the eastern and southern regions of Dongting Lake, with lower frequency in the west. Bloom extent peaked in summer and autumn. At the daily scale, total phosphorus (TP), chlorophyll a (Chl-a), and air temperature were key promoters of bloom development, whereas total nitrogen (TN) and barometric pressure exhibited inhibitory effects. Monthly analyses revealed significant positive correlations between TN, Chl-a, air temperature, and bloom growth. On seasonal and annual scales, Chl-a concentration closely correlated with bloom intensity. The largest bloom, recorded in 2014, covered 1094.57 km2. This comprehensive analysis elucidated the spatial patterns and multi-year trends of blooms in Dongting Lake and identified seasonal hot spots, interannual variability, and recurring high-risk periods. The findings provide a critical reference for long-term monitoring, management, and risk mitigation of blooms in Dongting Lake and similar ecosystems, supporting optimized water resource management strategies.