| Summary: | Riverine blooms have become a challenging global environmental problem owing to strong disturbances from intensified human activities and the construction or operation of hydraulic projects. Previous studies mainly paid attention to algal blooms in the lakes and reservoirs, while less focused on the prediction and prevention of algal blooms in large rivers. As one of the highly regulated rivers in China, the downstream of Han River frequently occurred consecutive algal blooms in recent decades, was selected as the case study. Firstly, algal blooms in the downstream of Han River during 1992–2021 were investigated to find out the key environmental factors governing algal blooms. Secondly, the distribution lag model was applied to ascertain the time lag between key environmental factors and algal growth in January-April 2021. Thirdly, a random forest machine learning (RFML) model was established for prediction and early warning of river algal bloom. Finally, the threshold of controllable hydro-meteorological conditions and control strategies for the algal bloom prevention was proposed. Results reveal that: (1) The importance ranking of key environmental variables for algal bloom are antecedent air temperature, total phosphorus, flow discharge, total nitrogen, solar radiation and river turbidity; (2) The time-lag between algal growth and the key environmental drivers is the previous 1–5 days period; (3) The RFML model based on antecedent environmental variables can effectively predict the concentration of Chl-a; (4) The diatom bloom is very possible to outbreak if the 5-day sliding accumulated temperature is more than 43.13 °C and the average flow discharge is less than 780 m3/s. Our study may provide potential scientific guidance for the preemptive warning and control strategies of riverine blooms.
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