Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8636
Title: Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling
Authors: Menzer, Olaf
Meiring, Wendy
Kyriakidis, Phaedon
Keywords: Carbon dioxide
Eddy covariance
Gap-filling
Urban ecosystem
Machine learning
Uncertainty
Spatial heterogeneity
Issue Date: Jan-2015
Publisher: Elsevier Science Limited
Source: Atmospheric Environment, 2015, Volume 101, pages 312-327
Abstract: 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 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.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/8636
ISSN: 1352-2310
DOI: 10.1016/j.atmosenv.2014.11.006
Rights: Copyright © Elsevier B.V.
Appears in Collections:Άρθρα/Articles

Show full item record

SCOPUSTM   
Citations 50

2
checked on Jun 27, 2017

WEB OF SCIENCETM
Citations 20

2
checked on May 24, 2017

Page view(s)

13
Last Week
0
Last month
1
checked on Jun 28, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.