## 4 Discussion
### Model Performance  
The REddyProc-based gap-filling and partitioning algorithms developed and maintained by the Max Planck Institute for Biogeochemistry are widely accepted as one of the standard methods in the EC flux measurement community [22; 26]. The program determines flux gaps during the low turbulent mixing values (u-star filtering) based on low friction velocity, which are identified using moving point method, breakpoint detection, or bootstrapping uncertainty analysis. The purpose for this filtering process was to remove biased flux estimates caused by undetectable advection found in the stable nocturnal surface layer below the IRGA sensor head [38]. Meanwhile, it also uses a look-up table approach based on similarities of meteorological conditions found within a 7- or 14-day time frame for gap-filling. In the original paper, [PERSON] et al. [22] benchmarked their results against the BGC online tool (BGC16, 2016 version MPI-BGC in Jena Sect. 3). However, comparisons were only made between the BGC16 and REddyProc predicted values, which indicated excellent agreement; but no comparisons against EC-measured real data were performed. Therefore, we think the benchmarking results obtained from this study could be very informative to the EC flux measurement community. Additionally, [PERSON] et al. [39] and [PERSON] et al. [6] also pointed out that the standard deviation of the vertical wind speed could serve as a better sorting parameter, but the results obtained using both were similar. Therefore, we accepted the more popular u-star-based filtering results in this study as one of the main criteria for selecting high-quality data. The results from this study indicated that non-parametric, non-linear, kernel methods, such as SVM generally provided superior gap-filling performance than REddyProc. The ANN-based models could be used for predicting both NEE and LE gaps, but the performance stability remained questionable, which could be caused by the uncertainties associated with the structure parameters (e.g., number of hidden layers and neurons in each layer). Overall, gap-filling LE data could be completed with much greater accuracies than NEE. This is anticipated, because the carbon dynamics on an ecosystem level are governed by much more complex plant-soil-microbes interplays than LE, thus, leading to greater challenges for the modeling processes. The 10-fold CV approach used much more training data than either the 7- or 14-day FW methods. However, only one model was constructed for each routine (training and testing), consisting of nine-fold training and one-fold testing. On the other hand, both the 7- and 14-day FW methods required training and testing on each individual gap data point, thus, resulted in a much greater number of model constructions, but with substantially less training data within each routine. Overall, the 10-fold CV approach was computationally cheaper and more time-efficient. The 7- or 14-day FW methods inherently considered the time-series correlations, as only those observed data temporally close to the artificial gap data points were used for model training. Therefore, the slightly improved performance observed from the FW methods indicated that there exists some time-series correlation in the dataset but not significantly impacting the overall gap-filling performance. Finally, except for the abnormality observed under the ANN-based 14-day FW NEE gap-filling models, little difference was found between the 7- versus 14-day FW methods. This indicated that: although the threshold time length for accurate model training was unknown, a 7-day FW dataset (2 \(\times\) 7 days \(\times\) 48 half hours = 672 sample vectors for each training process) should contain enough information for constructing satisfactory prediction models. To date, only a few studies have been conducted in designing and evaluating gap-filling methods for EC flux data. For example, approaches using the mean diurnal variation and the meteorological similarity-based look-up table approaches were compared by [PERSON] et al.