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dc.contributor.authorWilliams, Mathew
dc.contributor.authorRichardson, Andrew D.
dc.contributor.authorReichstein, M.
dc.contributor.authorStoy, Paul C.
dc.contributor.authorPeylin, Phili
dc.contributor.authorVerbeeck, Hans
dc.contributor.authorCarvalhais, N.
dc.contributor.authorJung, Martin
dc.contributor.authorHollinger, David Y.
dc.contributor.authorKattge, J.
dc.contributor.authorLeuning, R.
dc.contributor.authorLuo, Yiqi
dc.contributor.authorTomelleri, E.
dc.contributor.authorTrudinger, C.
dc.contributor.authorWang, Ying-Ping
dc.date.accessioned2018-11-01T15:31:54Z
dc.date.available2018-11-01T15:31:54Z
dc.date.issued2009-07
dc.identifier.citationWilliams, Mathew, Richardson, Andrew D., Reichstein, M., Stoy, Paul C., Peylin, Phili, Verbeeck, Hans, Carvalhais, N., Jung, Martin, Hollinger, David Y., Kattge, J., Leuning, R., Luo, Yiqi, Tomelleri, E., Trudinger, C., Wang, Ying-Ping. (2009) Improving land surface models with FLUXNET data. Biogeosciences 6: 1341-1359. DOI: 10.5194/bg-6-1341-2009.en_US
dc.identifier.issn1726-4189
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/14968
dc.description.abstractThere is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.en_US
dc.description.sponsorshipNERC CarbonFusion International Opportunities granten_US
dc.language.isoenen_US
dc.rightsCCBY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/legalcodeen_US
dc.titleImproving land surface models with FLUXNET dataen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1341en_US
mus.citation.extentlastpage1359en_US
mus.citation.issue7en_US
mus.citation.journaltitleBiogeosciencesen_US
mus.citation.volume6en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.5194/bg-6-1341-2009en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage8en_US


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CCBY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
Except where otherwise noted, this item's license is described as CCBY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.

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