Evapotranspiration and water budget (2003-2021) of an alpine shrubland on the northeastern Qinghai-Tibetan Plateau, China

Water provision ability is a pivotal ecological service of high-altitude alpine regions and is controlled by evapotranspiration (ET), precipitation, and soil water storage whereas the underlying ecohydrological processes remain highly unquantified. Since August 2002, ET has been continuously measured by an eddy covariance system, which comprised a 3-D ultrasonic anemometer (CAST3, Campbell Scientific, Logan, UT, USA) and an open-path infrared gas analyzer (Li7500, Li-Cor, Lincoln, NE, USA), mounted at a height of 2.5 m aboveground. The raw data frequency of 3-D wind speed, virtual temperature, and the density of CO2 and water vapor was uniform 10 Hz. The micro-climate was simultaneously observed by an adjacent auto weather station. These variables were mainly composed of 1.5 m air temperature and water vapor pressure (HMP45C, Vaisala, Finland), 1.5 m wind speed and direction (034A and 014A, RM-Young, USA), 1.5 m four-component radiation (CNR-4, Kipp&Zonen, Netherlands) and photosynthetically photon flux density (LI-190SB, Li-Cor, USA), 0.5 m precipitation (52202, RM-Young, USA), −0.05 m (a negative value means belowground) soil heat fluxes (HFT-3, Campbell Scientific, USA), −0.05 m soil temperature (105T, Campbell Scientific, USA), and −0.10 m and −0.20 m volumetric soil water content (CS616, Campbell Scientific, USA). The auto weather station was synchronized with the eddy covariance system and measured at 30-minute intervals. Because a tilting bucket precipitation gauge cannot accurately monitor snow and hail, monthly precipitation data were corrected by the corresponding observations of Haibei Station if necessary, which were measured manually two times (8:00 and 20:00, Beijing Standard Time) each day according to the conventional meteorological observation specifications.Satellite-derived normalized difference vegetation index (NDVI) was used to capture the temporal variations of plant growth and then to quantify the importance of vegetation to water balance. The 16-day NDVI was centered on the flux tower at a spatial resolution of 250 m and was generated by the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/MODIS/modis.html). Only the growing season NDVI (> 0.15) was used for further analyses, since alpine plants are dormant during the non-growing season. The abnormal values in NDVI serials probably caused by high cloud coverage were discarded and temporally interpolated. The 30-minute ET was recomputed from raw frequency data by EddyPro 7.0.4 (Li-Cor, USA). The main procedures included spike removal, 2-D coordinate rotation, time-lag compensation, Webb-Pearman-Leuning air density correction, low- and high-frequency spectral correction, and quality check. We discarded the poorest quality observations flagged “2” and kept the others (Foken et al., 2005). The latent heat flux dataset was filtered to remove outliers (<-100 W m-2 or >500 W m-2) and night-time friction velocity (> 0.15 m s-1) because of sensor malfunction or low turbulence. ET data gaps mainly occurred during the non-growing season and the nighttime and were gap-filled by thEvapotranspiration; soil water storage change; water budget; alpine shrublands; eddy covariance techniques; Qinghai-Tibetan Plateaue boosted regression trees model (BRT). BRTs of the growing season and non-growing season were separately trained by the valid data of ET and key environmental controls, including Ta, water vapor pressure, solar radiation, wind speed, soil temperature, and soil water content. The fitted BRT, combined with environmental controls corresponding to ET gaps, was used to fill those gaps.

水源供给能力是高海拔高寒地区关键的生态系统服务（ecological service），其受控于蒸散量（evapotranspiration, ET）、降水量与土壤蓄水量，但其背后的生态水文过程仍未得到充分量化。自2002年8月起，研究团队采用涡度协方差系统（eddy covariance system）持续监测ET数据：该系统由三维超声风速仪（3-D ultrasonic anemometer, CAST3, Campbell Scientific, 美国犹他州洛根市坎贝尔科学公司）与开放式红外气体分析仪（open-path infrared gas analyzer, Li7500, Li-Cor, 美国内布拉斯加州林肯市利科公司）组成，安装于地面以上2.5米高度。三维风速、虚温、CO₂与水汽密度的原始数据采样频率统一为10 Hz。相邻布设的自动气象站同步监测微气候变量，主要包括：1.5米高度的气温与水汽压（HMP45C, Vaisala, 芬兰）、1.5米高度的风速与风向（034A与014A, RM-Young, 美国）、1.5米高度的四分量辐射（CNR-4, Kipp&Zonen, 荷兰）与光合有效辐射（photosynthetically photon flux density, LI-190SB, Li-Cor, 美国）、0.5米高度的降水量（52202, RM-Young, 美国）、-0.05米（负值表示地下深度）的土壤热通量（HFT-3, Campbell Scientific, 美国）、-0.05米深度的土壤温度（105T, Campbell Scientific, 美国），以及-0.10米与-0.20米深度的体积土壤含水量（CS616, Campbell Scientific, 美国）。自动气象站与涡度协方差系统同步，采样间隔为30分钟。由于翻斗式雨量计无法准确监测降雪与冰雹，若有需要，月降水量数据将通过海北站的对应观测数据进行校正——海北站每日按照常规气象观测规范，手动进行两次观测（北京时间8:00与20:00）。
归一化植被指数（normalized difference vegetation index, NDVI）用于捕捉植物生长的时间动态特征，进而量化植被对区域水平衡的重要性。该16天合成的NDVI以通量塔为中心，空间分辨率为250米，由橡树岭国家实验室分布式主动档案中心（Oak Ridge National Laboratory Distributed Active Archive Center, ORNL DAAC, http://daac.ornl.gov/MODIS/modis.html）生成。仅使用生长季NDVI（>0.15）开展后续分析，因高寒植物在非生长季处于休眠状态。对因高云覆盖导致的NDVI序列异常值进行剔除，并通过时间插值填补缺失。
利用EddyPro 7.0.4（Li-Cor, 美国）将原始高频数据重新计算得到30分钟尺度的ET数据，主要处理步骤包括：野点剔除、二维坐标旋转、时滞补偿、Webb-Pearman-Leuning空气密度校正、高低频光谱校正以及质量检验。剔除标记为“2”的低质量观测值，保留其余数据（Foken et al., 2005）。对潜热通量数据集进行滤波处理，移除异常值（<-100 W·m⁻²或>500 W·m⁻²）以及因传感器故障或湍流较弱导致的夜间摩擦风速（>0.15 m·s⁻¹）的观测。ET数据的缺失值主要出现在非生长季与夜间，采用提升回归树模型（boosted regression trees model, BRT）进行插补。分别利用有效ET数据与关键环境控制变量（包括气温Ta、水汽压、太阳辐射、风速、土壤温度与土壤含水量），训练生长季与非生长季的BRT模型。将拟合得到的BRT模型与对应缺失ET时段的环境控制变量相结合，完成缺失值插补。本数据集的核心关键词包括：蒸散量、土壤蓄水量变化、水量平衡、高寒灌丛、涡度协方差技术、青藏高原（Qinghai-Tibetan Plateau）。