DOI: 10.1016/j.atmosenv.2014.11.006
Scopus记录号: 2-s2.0-84911944287
论文题名: Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling
作者: Menzer O ; , Meiring W ; , Kyriakidis P ; C ; , McFadden J ; P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 101 起始页码: 312
结束页码: 327
语种: 英语
英文关键词: Carbon dioxide
; Eddy covariance
; Gap-filling
; Machine learning
; Spatial heterogeneity
; Uncertainty
; Urban ecosystem
Scopus关键词: Artificial intelligence
; Budget control
; Carbon
; Carbon dioxide
; Ecosystems
; Filling
; Learning systems
; Random errors
; Time series
; Towers
; Eddy covariance
; Gap filling
; Spatial heterogeneity
; Uncertainty
; Urban ecosystem
; Urban growth
; carbon dioxide
; carbon dioxide
; carbon flux
; disturbance
; ecophysiology
; eddy covariance
; error analysis
; heterogeneity
; source-sink dynamics
; spatial variation
; uncertainty analysis
; urban ecosystem
; Article
; carbon footprint
; error
; explanatory variable
; landscape
; machine learning
; seasonal variation
; time
; time series analysis
; United States
; urban area
; Minneapolis
; Minnesota
; Saint Paul
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: A 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 1382gCm-2yr-1, across different years and different gap-filling methods. Bias errors of annual sums resulting from gap-filling did not exceed 18gCm-2yr-1 and random uncertainties did not exceed ±44gCm-2yr-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. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82130
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geography, University of California, Santa Barbara, CA, United States; Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, United States
Recommended Citation:
Menzer O,, Meiring W,, Kyriakidis P,et al. Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling[J]. Atmospheric Environment,2015-01-01,101