English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. The errors and uncertainties associated with gap-filling algorithms ofwater, carbon, and energy fluxes data have always been one of the mainchallenges 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 forthe three major fluxes typically found in EC time series. We then examinedthe algorithms' performance for different gap-filling scenarios utilisingthe 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 eachalgorithm and (b) to compare the performance of traditional and newgap-filling techniques for the EC data, for fluxes, and separately for their corresponding meteorological drivers. The algorithms' performance wasevaluated by generating nine gap windows with different lengths, rangingfrom 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 eachscenario, a variety of statistical metrics were used to evaluate thealgorithms' performance. The algorithms showed different levels ofsensitivity to the gap lengths; the Prophet Forecast Model (FBP) revealedthe 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.

A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers