A high-resolution Net Ecosystem Exchange dataset (500m, 8-day intervals) for the Qinghai-Tibet Plateau

We developed a high-resolution NEE dataset (500m, 8-day intervals) for the QTP, utilizing in-situ measurements from 36 plateau sites and 80 forest sites outside it, combined with remote sensing and meteorological data. Various combinations of environmental variables and machine learning algorithms were applied to assess prediction uncertainties. The results indicated that the annual mean NEE for the QTP averaged -147.63 ± 15.03 Tg C yr^-1, with an annual increase of -0.96 Tg C yr^-1 from 2002 to 2022. Meadows contributed most (38.1%) to total NEE, followed by shrubs (26.7%), forests (23%), steppes (11.8%), and wetlands (0.5%). Compared to previous studies, this research utilized more eddy covariance observations and covered all QTP ecosystem types, except for deserts. The updated NEE prediction dataset for the QTP provided essential data for advancing regional carbon cycle research.

我们开发了一套针对青藏高原（QTP）的高分辨率净生态系统交换（NEE）数据集（空间分辨率500米，时间分辨率8天），该数据集整合了高原内36个站点及高原外80个森林站点的原位观测数据、遥感数据与气象数据。研究通过环境变量与机器学习算法的多种组合，评估了预测不确定性。结果显示，QTP的年平均NEE为-147.68±15.02 Tg C yr^-1，2002至2022年间的年增长速率为0.94 Tg C yr^-1。草甸对总NEE的贡献最大（38.1%），其次为灌丛（26.7%）、森林（23%）、草原（11.8%）和湿地（0.5%）。与以往研究相比，本研究采用了更多涡度协方差（Eddy Covariance）观测数据，并覆盖了QTP除荒漠外的所有生态系统类型。这份更新后的QTP NEE预测数据集为推进区域碳循环研究提供了关键基础数据。