Carey, Cayelan C.Woelmer, Whitney M.Lofton, Mary E.Figueiredo, Renato J.Bookout, Bethany J.Corrigan, Rachel S.Daneshmand, VahidHounshell, Alexandria G.Howard, Dexter W.Lewis, Abigail S. L.McClure, Ryan P.Wander, Heather L.Ward, Nicole K.Thomas, R. Quinn2021-08-042021-08-042021-01-182044-2041http://hdl.handle.net/10919/104566Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.application/pdfenCreative Commons Attribution-NonCommercial-NoDerivs 4.0 Internationaldata assimilationFAIR data principlesFLAREhuman-centered designquantified uncertaintyreal-time forecastArticle - Refereedhttps://doi.org/10.1080/20442041.2020.18164212044-205X