Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

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dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorLofton, Mary E.en
dc.contributor.authorFigueiredo, Renato J.en
dc.contributor.authorBookout, Bethany J.en
dc.contributor.authorCorrigan, Rachel S.en
dc.contributor.authorDaneshmand, Vahiden
dc.contributor.authorHounshell, Alexandria G.en
dc.contributor.authorHoward, Dexter W.en
dc.contributor.authorLewis, Abigail S. L.en
dc.contributor.authorMcClure, Ryan P.en
dc.contributor.authorWander, Heather L.en
dc.contributor.authorWard, Nicole K.en
dc.contributor.authorThomas, R. Quinnen
dc.contributor.departmentBiological Sciencesen
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2021-08-04T12:27:42Zen
dc.date.available2021-08-04T12:27:42Zen
dc.date.issued2021-01-18en
dc.description.abstractNear-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.en
dc.description.notesThis work was financially supported by the Western Virginia Water Authority and U.S. National Science Foundation grants CNS-1737424, DEB-1753639, DEB-1926050, and DEB1926388, DBI-1933016, DBI-1933102.en
dc.description.sponsorshipWestern Virginia Water Authority; U.S. National Science FoundationNational Science Foundation (NSF) [CNS-1737424, DEB-1753639, DEB-1926050, DEB1926388, DBI-1933016, DBI-1933102]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/20442041.2020.1816421en
dc.identifier.eissn2044-205Xen
dc.identifier.issn2044-2041en
dc.identifier.urihttp://hdl.handle.net/10919/104566en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectdata assimilationen
dc.subjectFAIR data principlesen
dc.subjectFLAREen
dc.subjecthuman-centered designen
dc.subjectquantified uncertaintyen
dc.subjectreal-time forecasten
dc.titleAdvancing lake and reservoir water quality management with near-term, iterative ecological forecastingen
dc.title.serialInland Watersen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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