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dc.contributor.authorEller, CB
dc.contributor.authorRowland, L
dc.contributor.authorOliveira, RS
dc.contributor.authorBittencourt, PRL
dc.contributor.authorBarros, FV
dc.contributor.authorda Costa, ACL
dc.contributor.authorMeir, P
dc.contributor.authorFriend, AD
dc.contributor.authorMencuccini, M
dc.contributor.authorSitch, S
dc.contributor.authorCox, P
dc.date.accessioned2018-10-24T10:13:41Z
dc.date.issued2018-10-08
dc.description.abstractThe current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration (E) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.en_GB
dc.description.sponsorshipThis study was funded by the Newton Fund through the Met. Office Climate Science for Service Partnership Brazil (CSSP Brazil), UK NERC independent fellowship grant no. NE/N014022/1 to L.R., UK NERC grant no. NE/J010154/1 to S.S., UK NERC grant no. NE/J011002 to S.S., P.M. and M.M., ARC grant DP170104091 to P.M., and CNPQ grant no. 457914/2013-0/MCTI/CNPq/FNDCT/LBA/ESECAFLOR to A.C.L.d.C.en_GB
dc.identifier.citationVol. 373 (1760), article 20170315en_GB
dc.identifier.doi10.1098/rstb.2017.0315
dc.identifier.urihttp://hdl.handle.net/10871/34410
dc.language.isoenen_GB
dc.publisherRoyal Societyen_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/30297470en_GB
dc.rights© 2018 The Authors. Open access. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.en_GB
dc.subjectdroughten_GB
dc.subjectoptimality theoryen_GB
dc.subjectplant hydraulicsen_GB
dc.subjectstomatal modelsen_GB
dc.subjecttropical foresten_GB
dc.titleModelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulicsen_GB
dc.typeArticleen_GB
dc.date.available2018-10-24T10:13:41Z
exeter.place-of-publicationEnglanden_GB
dc.descriptionThis is the final version. Available on open access from the Royal Society via the DOI in this recorden_GB
dc.descriptionData accessibility The JULES soil moisture output used in this study as well as the meteorological driving data were obtained from the TRENDY project. The full TRENDY dataset (http://dgvm.ceh.ac.uk/index.html) is available, subject to the individual modelling group approval, via a request to S.S. (s.a.sitch@exeter.ac.uk). The sap flux data used for model validation is published in da Costa et al. [59]. The R code for the models used in this paper and the plant input data for each site used in this study are available as electronic supplementary material.en_GB
dc.identifier.journalPhilosophical Transactions of the Royal Society B: Biological Sciencesen_GB


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