涡度相关技术连续观测的碳水通量是准确评估生态系统固碳持水等生态功能的重要基础数据, 由于通量观测数据的缺失十分常见且比例较高, 引入现代机器学习算法以发展缺失数据的插补方法对降低研究结果不确定性具有重要意义。该研究利用青藏高原东北隅高寒金露梅(Potentilla fruticosa)灌丛已发布的2003-2005年水、热、CO₂通量数据集, 结合气温、大气水汽压、风速、太阳短波辐射、表层土壤温度和表层土壤含水量等主要环境因子构建了增强回归树模型(BRT)以插补缺失通量数据, 并与中国通量观测研究联盟(ChinaFLUX)的数据序列进行了比对, 以评估BRT在通量数据集成分析中的应用。BRT对大样本(N > 10 000)通量数据具有较好的模拟效果, 观测值与模拟值的回归斜率为1.01-1.05 (R² > 0.80)。BRT表明植被生长季(5-10月)白天30 min净CO₂交换量(NEE)主要受控于太阳短波辐射和大气水汽压, 二者对NEE变异的相对贡献之和为74.7%。表层土壤温度是生长季夜间及非生长季全天30 min NEE的主要驱动因子, 其相对贡献为68.5%。30 min显热通量(H)和潜热通量(LE)均主要受控于太阳短波辐射(相对贡献大于58.6%)。BRT插补的30 min缺失通量数据均显著小于ChinaFLUX的插补结果。除逐日NEE无显著差异外(p = 0.14), BRT的逐日生态系统总交换(GEE)、生态系统呼吸(RES)、H和LE极显著小于ChinaFLUX的数据序列分别约17.5%、21.0%、2.7%和2.2%, 但由于量级差异较小, 二者具有较高的一致性(数据序列的回归斜率在0.95-1.17)。除逐月GEE和RES外, BRT的逐月NEE、H和LE与ChinaFLUX的数据序列无显著(p > 0.09)差异。相对于ChinaFLUX数据插补方法, BRT不需要复杂的数学表达就可模拟主要环境因子的非线性作用特征, 从而进行缺失通量数据的插补, 是通量数据集成分析的一种可行方法。

Aims The continuous observation datasets of water, heat, and carbon fluxes measured by the eddy covariance technique are important basis for accurate assessment of regional carbon sequestration and water-holding capacity. However, the rate of gaps in flux datasets is high and common due to various reasons, and different gap-filling methods increase the uncertainties of the related studies. The aim of this study is to introduce and test the applicability of boosted regression trees model (BRT), one of the up-to-date machine learning algorithms, for the gap- filling to flux datasets.

Methods Based on the published valid dataset of water, heat and CO₂ flux, and main environmental factors, including air temperature, atmospheric water vapor pressure, wind speed, solar shortwave radiation, topsoil temperature, and topsoil water content of an alpine Potentilla fruticosa scrubland on the northeastern Qingzang Plateau from 2003 to 2005, the BRT were trained to fill flux data gaps and the results were compared to those corresponding data serials provided by Chinese Flux Observation and Research Network (ChinaFLUX).

Important findings The results showed that the BRT performed well for a large amount of samples (N > 10 000) and the regression slopes of observation data against predicted value were between 1.01 and 1.05 with R² > 0.80. The BRT revealed that the daytime 30-min CO₂ flux (net ecosystem CO₂ exchange, NEE) in the growing season (i.e., May to October) was mainly controlled by solar shortwave radiation and atmospheric vapor pressure, whose relative contributions to NEE variability were up to 74.7%. The topsoil temperature was the determinant for NEE at night during the growing season and the whole day during the non-growing season, and its relative contribution was 68.5%. The 30-min sensible heat flux (H) and latent heat flux (LE) were both linearly related to solar radiation, and their relative contributions were above 58.6%. 30-min flux data gap amount filled by the BRT was significantly less than those by ChinaFLUX. Except for daily net ecosystem CO₂ exchange (p = 0.14), daily gross ecosystem CO₂ exchange (GEE), ecosystem respiration (RES), H, and LE of the BRT were significantly less than those of ChinaFLUX by 17.5%, 21.0%, 2.7%, and 2.2%, respectively. However, there was a reasonable consistency between the daily fluxes of 2003-2005 interpolated by the BRT and by ChinaFLUX due to the small magnitude difference (the regression slopes of the two data series were between 0.95 and 1.17). Except for monthly GEE and RES, monthly NEE, H, and LE of the BRT had no significant difference between the BRT and ChinaFLUX (p > 0.09). Compared with the ChinaFLUX gap-filling method, BRT can simulate the nonlinear relationships between fluxes and environmental factors without complicated mathematical expressions and quantify the relative contribution of environmental factors to the flux data gaps, which is a feasible technique for the integrated analysis of flux data.