A Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

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dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorFigueiredo, Renato J.en
dc.contributor.authorDaneshmand, Vahiden
dc.contributor.authorBookout, Bethany J.en
dc.contributor.authorPuckett, Laura K.en
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2021-02-15T20:03:29Zen
dc.date.available2021-02-15T20:03:29Zen
dc.date.issued2020-11en
dc.description.abstractFreshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real-time iterative water temperature forecasting system (FLARE-Forecasting Lake And Reservoir Ecosystems). FLARE is composed of water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475-day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean square error (RMSE) of daily forecasted water temperatures was 1.13 degrees C at the reservoir's near-surface (1.0 m) for 7-day ahead forecasts and 1.62 degrees C for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87 degrees C for 7-day forecasts and 1.20 degrees C for 16-day forecasts. FLARE successfully predicted the onset of fall turnover 4-14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near-sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open-source system for lake and reservoir water quality forecasting to improve real-time management.en
dc.description.notesThis work was supported by the U.S. National Science Foundation (CNS-1737424, DEB-1753639, DBI-1933016, DEB-1926050, and DEB-1926388); the Virginia Tech Global Change Center; and Fralin Life Sciences Institute. We thank our Ecological Forecasting seminar students, the Smart Reservoir team, and Ecological Forecasting Initiative (EFI) colleagues for helpful feedback on the project; the Western Virginia Water Authority for their long-term support and access to field sites; and Mary Lofton, Ryan McClure, and Whitney Woelmer for their critical help in the field.en
dc.description.sponsorshipU.S. National Science FoundationNational Science Foundation (NSF) [CNS-1737424, DEB-1753639, DBI-1933016, DEB-1926050, DEB-1926388]; Virginia Tech Global Change Center; Fralin Life Sciences Instituteen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1029/2019WR026138en
dc.identifier.eissn1944-7973en
dc.identifier.issn0043-1397en
dc.identifier.issue11en
dc.identifier.othere2019WR026138en
dc.identifier.urihttp://hdl.handle.net/10919/102372en
dc.identifier.volume56en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectdata assimilationen
dc.subjectecological forecastingen
dc.subjectensemble Kalman filteren
dc.subjectFLAREen
dc.subjectGeneral Lake Modelen
dc.subjectwater temperatureen
dc.titleA Near-Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Timeen
dc.title.serialWater Resources Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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