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Abstract While there is a diversity of approaches for modeling phytoplankton blooms, their accuracy in predicting the onset and manifestation of a bloom is still lagging behind what is needed to support effective management. We outline a framework that integrates trait theory and ecosystem modeling to improve bloom prediction. This framework builds on the concept that the phenology of blooms is determined by the dynamic interaction between the environment and traits within the phytoplankton community. Phytoplankton groups exhibit a collection of traits that govern the interplay of processes that ultimately control the phases of bloom initiation, maintenance, and collapse. An example of process‐trait mapping is used to demonstrate a more consistent approach to bloom model parameterization that allows better alignment with models and laboratory‐ and ecosystem‐scale datasets. Further approaches linking statistical‐mechanistic models to trait parameter databases are discussed as a way to help optimize models to better simulate bloom phenology and allow them to support a wider range of management needs. 

China has made a concerted effort to successfully improve water quality of rivers, but lake water quality has not improved. Lakes require controls on both catchment external nutrient loads and in-lake internal loads, where nature-based solutions are coupled with engineered systems to achieve the United Nations Sustainable Development Goals (SDGs). 

Abstract Climate warming in combination with nutrient enrichment can greatly promote phytoplankton proliferation and blooms in eutrophic waters. Lake Taihu, China, is a large, shallow and eutrophic system. Since 2007, this lake has experienced extensive nutrient input reductions aimed at controlling cyanobacterial blooms. However, intense cyanobacterial blooms have persisted through 2017 with a record‐setting bloom occurring in May 2017. Causal analysis suggested that this bloom was sygenerically driven by high external loading from flooding in 2016 in the Taihu catchment and a notable warmer winter during 2016/2017. High precipitation during 2016 was associated with a strong 2015/2016 El Niño in combination with the joint effects of Atlantic Multi‐decadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while persistent warmth during 2016/2017 was strongly related to warm phases of AMO and PDO. The 2017 blooms elevated water column pH and led to dissolved oxygen depletion near the sediment, both of which mobilized phosphorus from the sediment to overlying water, further promoting cyanobacterial blooms. Our finding indicates that regional climate anomalies exacerbated eutrophication via a positive feedback mechanism, by intensifying internal nutrient cycling and aggravating cyanobacterial blooms. In light of global expansion of eutrophication and blooms, especially in large, shallow and eutrophic lakes, these regional effects of climate anomalies are nested within larger scale global warming predicted to continue in the foreseeable future.