Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

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dc.contributor.authorBarnes, Mallory L.
dc.contributor.authorFarella, Martha M.
dc.contributor.authorScott, Russell L.
dc.contributor.authorMoore, David J. P.
dc.contributor.authorPonce-Campos, Guillermo E.
dc.contributor.authorBiederman, Joel A.
dc.contributor.authorMacBean, Natasha
dc.contributor.authorLitvak, Marcy E.
dc.contributor.authorBreshears, David D.
dc.date.accessioned2023-09-15T01:25:58Z
dc.date.available2023-09-15T01:25:58Z
dc.date.issued2021-12-02
dc.description© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Keywords: carbon cycle.
dc.description.abstractDryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.
dc.description.sponsorshipFunding for some of the flux data collection and analysis in this study comes from The Office of Science, U.S. Department of Energy and the U.S. Department of Agriculture. DDB was supported by USDA National Institute of Food and Agriculture McIntire Stennis project 1016938 (ARZT-1390130-M12-222). The authors would like to acknowledge three anonymous reviewers whose suggestions greatly improved this manuscript.
dc.identifier.citationBarnes, M.L., Farella, M.M., Scott, R.L. et al. Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling. Commun Earth Environ 2, 248 (2021). https://doi.org/10.1038/s43247-021-00308-2
dc.identifier.otherhttps://doi.org/10.1038/s43247-021-00308-2
dc.identifier.urihttps://hdl.handle.net/20.500.14096/420
dc.language.isoen
dc.publisherSpringer Nature
dc.titleImproved dryland carbon flux predictions with explicit consideration of water-carbon coupling
dc.typeArticle

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