# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 4 Discussion
### Can the Long-Period-Gap-Filled Flux Data Capture the Interannual Variability? We found considerable discrepancies in interannual variability between the SVR\({}_{\text{all}}\) and the observations (Figure 8), even though the model performance for seasonality was excellent. We also estimated the uncertainties in annual sums introduced by the gap-filling approach by determining the standard deviation of the differences between modeled and measured annual fluxes in Figure 8. The standard deviations were 112 g C m\({}^{-2}\) year\({}^{-1}\) for GPP, 91 g C m\({}^{-2}\) year\({}^{-1}\) for RE, 83 g C m\({}^{-2}\) year\({}^{-1}\) for NEE, and 50 mm year\({}^{-1}\) for ET, which were almost twice as high as the uncertainties previously quantified using the Monte Carlo approach. This supports the idea that it is more difficult to capture the interannual variability than the seasonal variability. For RE and ET, in particular, the estimations from SVR\({}_{\text{all}}\) and the observations were negatively correlated. Note that the interannual variability derived from SVR\({}_{\text{all}}\) was much smaller than that from the observations. Micro-/bio-meteorological phenomena can be described as cycles composed of various processes with different time scales, e.g., day, season, year, several years/decades, century, millennium. Most gap-filling methods are based on the fact that similar phenomena happen repeatedly as time passes [5]. Such repeatability decreases exponentially with the length of the time scale, highlighting the difficulty of predicting very long variations such as interannual variability. There is also a possibility that the observations could not capture the cycles that determined the interannual variability (lack of temporal representativeness [37]). The discrepancies could be improved by training the SVR-based model using data from the closest two years around the target year, i.e., previous year and following year except for 2007 and 2014 (SVR2 years). This suggests that using longer datasets for training generally produces better model performance, but there is a greater possibility that interannual variations caused by ecosystem state changes (e.g., changes in crop variety and cultivation area) will not be captured. [PERSON] et al. [38] reported that four different varieties of rice (Dongjin No. 1 (2003-2008), Nampyung (2009), Onnuri (2010-2011), and Saenuri (2012-2015)) were cultivated at the site, each having different efficiency in terms of water/light/carbon use. This change will have lowered the predictability of SVRall. It is challenging to consider such changes in variety, as far more data are required for training the model. Additionally, in the case of barley cultivation, the cultivation area changed considerably every year at the site (Korean Statistical Information Service, [[http://kosis.kr/eng/](http://kosis.kr/eng/)]([http://kosis.kr/eng/](http://kosis.kr/eng/))). To strengthen our inference, we also tested the flux data (without long gaps, Table S1) measured at the Gwangneung deciduous forest in the Korea National Arboretum (GDK; 37\({}^{\circ}\)44\({}^{\prime}\)56\({}^{\prime\prime}\) N, 127\({}^{\circ}\) 8\({}^{\prime}\)57\({}^{\prime\prime}\) E, 252 m a.s.l.) [17; 39]. Gwangneung is a royal forest that surrounds the musoleum of King [PERSON] of the Joseon dynasty. Hence, over the last 550 years, this area has been protected to minimize human disturbance [40]. Unlike at the HFK site, SVRall outperformed SVR2 years, supporting our inference (Figure 9). The estimations and observations of RE were again negatively correlated. We speculated that the poor predictability of RE may have been because we overlooked a driver that controls the interannual variability, such as supplements of organic matter (e.g., manure, residues after harvest, litter, deadwood).