Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8636
DC FieldValueLanguage
dc.contributor.authorMenzer, Olaf-
dc.contributor.authorMeiring, Wendy-
dc.contributor.authorKyriakidis, Phaedon-
dc.date.accessioned2016-07-11T11:33:44Z-
dc.date.available2016-07-11T11:33:44Z-
dc.date.issued2015-01-
dc.identifier.citationAtmospheric Environment, 2015, vol. 101, pp. 312-327en_US
dc.identifier.issn13522310-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8636-
dc.description.abstractA small, but growing, number of flux towers in urban environments measure surface–atmospheric exchanges of carbon dioxide by the eddy covariance method. As in all eddy covariance studies, obtaining annual sums of urban CO2 exchange requires imputation of data gaps due to low turbulence and non-stationary conditions, adverse weather, and instrument failures. Gap-filling approaches that are widely used for measurements from towers in natural vegetation are based on light and temperature response models. However, they do not account for key features of the urban environment including tower footprint heterogeneity and localized CO2 sources. Here, we present a novel gap-filling modeling framework that uses machine learning to select explanatory variables, such as continuous traffic counts and temporal variables, and then constrains models separately for spatially classified subsets of the data. We applied the modeling framework to a three year time series of measurements from a tall broadcast tower in a suburban neighborhood of Minneapolis-Saint Paul, Minnesota, USA. The gap-filling performance was similar to that reported for natural measurement sites, explaining 64% to 88% of the variability in the fluxes. Simulated carbon budgets were in good agreement with an ecophysiological bottom-up study at the same site. Total annual carbon dioxide flux sums for the tower site ranged from 1064 to 1382 g C m−2 yr−1, across different years and different gap-filling methods. Bias errors of annual sums resulting from gap-filling did not exceed 18 g C m−2 yr−1 and random uncertainties did not exceed ±44 g C m−2 yr−1 (or ±3.8% of the annual flux). Regardless of the gap-filling method used, the year-to-year differences in carbon exchange at this site were small. In contrast, the modeled annual sums of CO2 exchange differed by a factor of two depending on wind direction. This indicated that the modeled time series captured the spatial variability in both the biogenic and anthropogenic CO2 sources and sinks in a reproducible way. The gap-filling approach developed here may also be useful for inhomogeneous sites other than urban areas, such as logged forests or ecosystems under disturbance from fire or pests.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofAtmospheric Environmenten_US
dc.rightsCopyright © Elsevier B.V.en_US
dc.subjectCarbon dioxideen_US
dc.subjectEddy covarianceen_US
dc.subjectGap-fillingen_US
dc.subjectUrban ecosystemen_US
dc.subjectMachine learningen_US
dc.subjectUncertaintyen_US
dc.subjectSpatial heterogeneityen_US
dc.titleAnnual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-fillingen_US
dc.typeArticleen_US
dc.collaborationUniversity of Californiaen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.atmosenv.2014.11.006en_US
dc.dept.handle123456789/54en
dc.relation.volume101en_US
cut.common.academicyearemptyen_US
dc.identifier.spage312en_US
dc.identifier.epage327en_US
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.journal.journalissn1352-2310-
crisitem.journal.publisherElsevier-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4222-8567-
crisitem.author.parentorgFaculty of Engineering and Technology-
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