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Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

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dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorLofton, Mary E.en
dc.contributor.authorMcClure, Ryan P.en
dc.contributor.authorWander, Heather L.en
dc.contributor.authorCarey, Cayelan C.en
dc.date.accessioned2024-01-16T19:19:00Zen
dc.date.available2024-01-16T19:19:00Zen
dc.date.issued2022-10en
dc.description.abstractAs climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.en
dc.description.versionPublished versionen
dc.format.extent22 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e2642 (Article number)en
dc.identifier.doihttps://doi.org/10.1002/eap.2642en
dc.identifier.eissn1939-5582en
dc.identifier.issn1051-0761en
dc.identifier.issue7en
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.orcidCarey, Cayelan [0000-0001-8835-4476]en
dc.identifier.pmid35470923en
dc.identifier.urihttps://hdl.handle.net/10919/117368en
dc.identifier.volume32en
dc.language.isoenen
dc.publisherWileyen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/35470923en
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectautoregressive modelen
dc.subjectBayesian modelen
dc.subjectbloomsen
dc.subjectchlorophyll aen
dc.subjectecological forecastingen
dc.subjecthindcasten
dc.subjecthistorical monitoringen
dc.subjectiterative modelen
dc.subjectmanagementen
dc.subjectphytoplanktonen
dc.subjecttime series analysisen
dc.subjectwater qualityen
dc.subject.meshHumansen
dc.subject.meshPhytoplanktonen
dc.subject.meshEcosystemen
dc.subject.meshModels, Theoreticalen
dc.subject.meshForecastingen
dc.subject.meshDrinking Wateren
dc.titleNear-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictabilityen
dc.title.serialEcological Applicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2022-03-07en
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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