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Predicting Spring Phenology in Deciduous Broadleaf Forests: NEON Phenology Forecasting Community Challenge

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dc.contributor.authorWheeler, Kathryn I.en
dc.contributor.authorDietze, Michael C.en
dc.contributor.authorLeBauer, Daviden
dc.contributor.authorPeters, Jody A.en
dc.contributor.authorRichardson, Andrew D.en
dc.contributor.authorRoss, Arun A.en
dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorZhu, Kaien
dc.contributor.authorBhat, Uttamen
dc.contributor.authorMunch, Stephanen
dc.contributor.authorBuzbee, Raphaela Floreanien
dc.contributor.authorChen, Minen
dc.contributor.authorGoldstein, Benjaminen
dc.contributor.authorGuo, Jessicaen
dc.contributor.authorHao, Daleien
dc.contributor.authorJones, Chrisen
dc.contributor.authorKelly-Fair, Miraen
dc.contributor.authorLiu, Haoranen
dc.contributor.authorMalmborg, Charlotteen
dc.contributor.authorNeupane, Nareshen
dc.contributor.authorPal, Debasmitaen
dc.contributor.authorShirey, Vaughnen
dc.contributor.authorSong, Yiluanen
dc.contributor.authorSteen, McKaleeen
dc.contributor.authorVance, Eric A.en
dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorWynne, Jacob H.en
dc.contributor.authorZachmann, Lukeen
dc.date.accessioned2024-01-16T16:02:32Zen
dc.date.available2024-01-16T16:02:32Zen
dc.date.issued2024-01-01en
dc.description.abstractAccurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.agrformet.2023.109810en
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.urihttps://hdl.handle.net/10919/117361en
dc.identifier.volume345en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0168192323005002?via%3Dihuben
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectPhenologyen
dc.subjectEcological forecastingen
dc.subjectDeciduous broadleafen
dc.subjectBudbursten
dc.titlePredicting Spring Phenology in Deciduous Broadleaf Forests: NEON Phenology Forecasting Community Challengeen
dc.title.serialAgricultural and Forest Meteorologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2023-11-08en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Biological Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen

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