Continuous time-series of CO₂, water, and energy fluxes are useful for evaluating the impacts of climate-change and management on ecosystems. The eddy covariance (EC) technique can provide continuous, direct measurements of ecosystem fluxes, but to achieve this gaps in data must be filled. Research-standard methods of gap-filling fluxes have tended to focus on CO₂ fluxes in temperate forests and relatively short gaps of less than two weeks. A gap-filling method applicable to other fluxes and capable of filling longer gaps is needed.

To address this challenge, we propose a novel gap-filling approach, Random Forest Robust (RFR). RFR can accommodate a wide range of data gap sizes, multiple flux types (i.e. CO₂, water and energy fluxes). We configured RFR using either three (RFR₃) or ten (RFR₁₀) driving variables. RFR was tested globally on fluxes of CO₂, latent heat (LE), and sensible heat (H) from 94 suitable FLUXNET2015 sites by using artificial gaps (from 1 to 30 days in length) and benchmarked against the standard marginal distribution sampling (MDS) method.

In general, RFR improved on MDS's R² by 15% (RFR₃) and by 30% (RFR₁₀) and reduced uncertainty by 70%. RFR's improvements in R² for H and LE were more than twice the improvement observed for CO₂ fluxes. Unlike MDS, RFR performed well for longer gaps; for example, the R² of RFR methods in filling 30-day gaps dropped less than 4% relative to 1-day gaps, while the R² of MDS dropped by 21%.

Our results indicate that the RFR method can provide improved gap-filling of CO₂, H and LE flux timeseries. Such improved continuous flux measurements, with low bias, can enhance our understanding of the impacts of climate-change and management on ecosystems globally.

CO ₂、水和能量通量的连续时间序列可用于评估气候变化和管理对生态系统的影响。涡流协方差 (EC) 技术可以提供对生态系统通量的连续、直接测量，但要实现这一点，必须填补数据空白。间隙填充通量的研究标准方法倾向于关注温带森林中的CO ₂通量和不到两周的相对较短的间隙。需要一种适用于其他助焊剂并能够填充更长间隙的间隙填充方法。

为了应对这一挑战，我们提出了一种新颖的间隙填充方法，即随机森林鲁棒 (RFR)。RFR 可以适应范围广泛的数据间隙大小、多种通量类型（即 CO ₂、水和能量通量）。我们使用三个 (RFR ₃ ) 或十个 (RFR ₁₀ ) 驱动变量来配置 RFR 。RFR使用人工间隙（长度从 1 到 30 天）对来自 94 个合适的 FLUXNET2015 站点的 CO ₂、潜热 (LE) 和显热 (H) 的通量进行了全球测试，并以标准边际分布抽样 (MDS) 为基准） 方法。

一般而言，RFR 在 MDS 的 R ^{2 上}提高了 15% (RFR ₃ ) 和 30% (RFR ₁₀ )，并将不确定性降低了 70%。RFR对 H 和 LE 的R ²的改进是观察到的 CO ₂通量改进的两倍多。与 MDS 不同，RFR 对于较长的间隙表现良好；例如，将R ²的RFR方法在填充物30天的间隙相对于1天的间隙小于4％下降，而将R ² MDS的下降了21％。

我们的结果表明，RFR 方法可以提供改进的 CO ₂、H 和 LE 通量时间序列的间隙填充。这种具有低偏差的改进的连续通量测量可以增强我们对气候变化和管理对全球生态系统影响的理解。