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Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the US

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dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorMcClure, Ryan P.en
dc.contributor.authorMoore, Tadhg N.en
dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorBoettiger, Carlen
dc.contributor.authorFigueiredo, Renato J.en
dc.contributor.authorHensley, Robert T.en
dc.contributor.authorCarey, Cayelan C.en
dc.date.accessioned2023-08-31T17:03:35Zen
dc.date.available2023-08-31T17:03:35Zen
dc.date.issued2023-04en
dc.description.abstractThe US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61 degrees C and a 35-days-ahead RMSE of 2.17 degrees C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.en
dc.description.notesWe thank V Daneshmand, B Steele, K Weathers, and the Forecasting Lake And Reservoir Ecosystems (FLARE) team for helpful insights and research support. Virginia Tech's Advanced Research Computing and B Sandbrook provided computational resources and support. This work was supported by US National Science Foundation grants DEB-1926388, CNS-1737424, DBI-1933016, DBI-1933102, DBI-1942280, and DEB-1926050. Author contributions: RQT, CCC, and RJF co-developed the FLARE forecasting framework and co-lead the FLARE project. RPM led the development of National Ecological Observatory Network (NEON) data processing and FLARE forecasting workflows with assistance from RQT. RPM calibrated lake models with assistance from CCC. TNM assisted with General Lake Model setup and FLARE configuration. WMW co-developed the code for generating historical weather forecasts with RQT. CB led the development of the neonstore package for downloading NEON data and co-developed the code for forecast scoring with RQT. RTH provided lake metadata and assisted with NEON data interpretation. CCC and RQT drafted the manuscript with feedback from all coauthors.en
dc.description.sponsorshipUS National Science Foundation [DEB-1926388, CNS-1737424, DBI-1933016, DBI-1933102, DBI-1942280, DEB-1926050]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/fee.2623en
dc.identifier.eissn1540-9309en
dc.identifier.issn1540-9295en
dc.identifier.urihttp://hdl.handle.net/10919/116178en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectthermal stratificationen
dc.titleNear-term forecasts of NEON lakes reveal gradients of environmental predictability across the USen
dc.title.serialFrontiers in Ecology and the Environmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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