Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

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dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorBrooks, Evan B.en
dc.contributor.authorJersild, Annika L.en
dc.contributor.authorWard, Eric J.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorAlbaugh, Timothy J.en
dc.contributor.authorDinon-Aldridge, Heatheren
dc.contributor.authorBurkhart, Harold E.en
dc.contributor.authorDomec, Jean-Christopheen
dc.contributor.authorFox, Thomas R.en
dc.contributor.authorGonzález-Benecke, Carlosen
dc.contributor.authorMartin, Timothy A.en
dc.contributor.authorNoormets, Askoen
dc.contributor.authorSampson, David A.en
dc.contributor.authorTeskey, Robert O.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.coverage.countryUnited Statesen
dc.date.accessioned2021-09-14T16:40:34Zen
dc.date.available2021-09-14T16:40:34Zen
dc.date.issued2017-07-26en
dc.date.updated2021-09-14T16:40:27Zen
dc.description.abstractPredicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6ĝ€ × ĝ€105ĝ€km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.en
dc.description.versionPublished versionen
dc.format.extentPages 3525-3547en
dc.format.extent23 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier14 (Article number)en
dc.identifier.doihttps://doi.org/10.5194/bg-14-3525-2017en
dc.identifier.eissn1726-4189en
dc.identifier.issn1726-4170en
dc.identifier.issue14en
dc.identifier.orcidThomas, R. Quinn [0000-0003-1282-7825]en
dc.identifier.orcidWynne, Randolph H. [0000-0003-3649-835X]en
dc.identifier.orcidAlbaugh, Timothy [0000-0002-0216-0134]en
dc.identifier.urihttp://hdl.handle.net/10919/104994en
dc.identifier.volume14en
dc.language.isoenen
dc.publisherCopernicusen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000406345300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectLife Sciences & Biomedicineen
dc.subjectPhysical Sciencesen
dc.subjectEcologyen
dc.subjectGeosciences, Multidisciplinaryen
dc.subjectEnvironmental Sciences & Ecologyen
dc.subjectGeologyen
dc.subjectMODEL-DATA FUSIONen
dc.subjectCANOPY STOMATAL CONDUCTANCEen
dc.subjectRADIATION-USE EFFICIENCYen
dc.subjectSOUTHERN UNITED-STATESen
dc.subjectLOBLOLLY-PINEen
dc.subject3-PG MODELen
dc.subjectTHROUGHFALL REDUCTIONen
dc.subjectWATER AVAILABILITYen
dc.subjectGROWTH-RESPONSESen
dc.subjectFERTILIZATIONen
dc.subjectMeteorology & Atmospheric Sciencesen
dc.subject04 Earth Sciencesen
dc.subject05 Environmental Sciencesen
dc.subject06 Biological Sciencesen
dc.titleLeveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experimentsen
dc.title.serialBiogeosciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Natural Resources & Environmenten
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Forest Resources and Environmental Conservationen
pubs.organisational-group/Virginia Tech/University Distinguished Professorsen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/CNRE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten
pubs.organisational-group/Virginia Tech/Post-docsen

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