Deep forecasting architectures mirror ecological time scales in water quality prediction

Accurate water quality forecasting—specifically tracking phytoplankton dynamics like chlorophyll a—is crucial for safeguarding aquatic ecosystems and global water security. Deep temporal architectures offer powerful non-linear modeling capabilities, promising to overcome the parameterization bottlenecks of traditional process-based models. However, current applications treat these models as black boxes, leaving it obscure how specific architectural modules interact with the intrinsic ecological time scales governing aquatic environments. Here, we reveal a fundamental architectural–ecological duality in water quality forecasting by systematically dissecting nine prediction architectures—spanning classic baselines to modern deep models—across two ecologically contrasting reservoirs. Using chlorophyll a dynamics to represent integrated system responses, we show that patch embedding serves as a universal temporal operator, mitigating noise and enhancing forecasting accuracy by up to 10.1% when integrated into standard baselines. Crucially, inter-variable modeling strategies dictate the effective forecast horizon: channel-independent architectures dominate short-term (1–7 days) chlorophyll a forecasting (Nash–Sutcliffe efficiency up to 0.88) by capturing biomass self-persistence, whereas cross-variable attention architectures excel in medium-term (8–15 days) prediction (efficiency up to 0.83) by uncoupling delayed nutrient–temperature interactions. Explainable AI further confirms a temporal shift in feature reliance from current biomass to lagged drivers over extended horizons. Beyond water quality, this mechanistic alignment between deep learning modules and ecological time scales establishes a scalable blueprint for building interpretable, horizon-adaptive AI frameworks across complex climate and environmental systems.