A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers
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Published:2021-06-28
Issue:1
Volume:10
Page:123-140
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ISSN:2193-0864
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Container-title:Geoscientific Instrumentation, Methods and Data Systems
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language:en
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Short-container-title:Geosci. Instrum. Method. Data Syst.
Author:
Mahabbati AtbinORCID, Beringer JasonORCID, Leopold MatthiasORCID, McHugh Ian, Cleverly JamesORCID, Isaac Peter, Izady Azizallah
Abstract
Abstract. The errors and uncertainties associated with gap-filling algorithms of
water, carbon, and energy fluxes data have always been one of the main
challenges of the global network of microclimatological tower sites that use the eddy covariance (EC) technique. To address these concerns and find more efficient gap-filling algorithms, we reviewed eight algorithms to estimate missing values of environmental drivers and nine algorithms for
the three major fluxes typically found in EC time series. We then examined
the algorithms' performance for different gap-filling scenarios utilising
the data from five EC towers during 2013. This research's objectives were (a) to evaluate the impact of the gap lengths on the performance of each
algorithm and (b) to compare the performance of traditional and new
gap-filling techniques for the EC data, for fluxes, and separately for their corresponding meteorological drivers. The algorithms' performance was
evaluated by generating nine gap windows with different lengths, ranging
from a day to 365 d. In each scenario, a gap period was chosen randomly,
and the data were removed from the dataset accordingly. After running each
scenario, a variety of statistical metrics were used to evaluate the
algorithms' performance. The algorithms showed different levels of
sensitivity to the gap lengths; the Prophet Forecast Model (FBP) revealed
the most sensitivity, whilst the performance of artificial neural networks (ANNs), for instance, did not vary as much by changing the gap length. The algorithms' performance generally decreased with increasing the gap length, yet the differences were not significant for windows smaller than 30 d. No significant differences between the algorithms were recognised for the meteorological and environmental drivers. However, the linear algorithms showed slight superiority over those of machine learning (ML), except the random forest (RF) algorithm estimating the ground heat flux (root mean square errors – RMSEs – of 28.91 and 33.92 for RF and classic linear regression – CLR, respectively). However, for the major fluxes, ML algorithms and the MDS showed superiority over the other algorithms. Even though ANNs, random forest (RF), and eXtreme Gradient Boost (XGB) showed comparable performance in gap-filling of the major fluxes, RF provided more consistent results with slightly less bias against the other ML algorithms. The results indicated no single algorithm that outperforms in all situations, but the RF is a potential alternative for the MDS and ANNs as regards flux gap-filling.
Publisher
Copernicus GmbH
Subject
Atmospheric Science,Geology,Oceanography
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