The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau.The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5)Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange.(6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling(NEE_fsd) as a measure for spatial and temporal variation.

青藏高原野外观测研究平台是开展青藏高原科学观测和研究的前沿阵地。基于高原地表过程与环境变化的陆面-边界层立体综合观测为青藏高原地气相互作用机理及其影响研究提供了大量的珍贵数据。本数据集综合了珠穆朗玛大气与环境综合观测研究站、藏东南高山环境综合观测研究站、那曲高寒气候环境观测研究站、纳木错多圈层综合观测研究站、阿里荒漠环境综合观测研究站、慕士塔格西风带环境综合观测研究站2005-2016年逐小时大气、土壤和涡动观测数据。包含了由多层风速风向、气温、湿度以及气压、降水组成的梯度观测数据，辐射四分量数据，多层土壤温湿度和土壤热通量观测数据以及感热通量、潜热通量和二氧化碳通量组成的湍流数据。这些数据能广泛的应用于青藏高原气象要素特征分析、遥感产品评估和遥感反演算法的发展、数值模拟的评估和发展等研究中。

太阳总辐射和直接辐射采用国产辐射表(TBQ-4-1,TBS-2,China)测量，温湿度采用自动气象站(HOBO weather station, Model H21, Onset Company, USA)测量。本数据为太阳辐射和气象要素数据，包括太阳总辐射和直接辐射，波长范围270-3200nm，单位W/m2。温湿度和水汽压单位分别为℃、%、hPa。太阳辐射和气象要素数据来源于数据提供者的测量。数据覆盖时间为2013-2016年。该数据集可以用于中国亚热带地区的太阳辐射及其变化机制等相关研究。

我们基于中国数字测震台网记录的发生在印度洋的8个地震（2009-2018）的波形资料，利用观测和三维理论波形互相关方法，获得了印度、尼泊尔和中国西南部地区的929个高质量的ScS-S走时残差（Differential traveltimes.dat）。这些时差显示出高达10s的横向变化，表明D”区剪切波速度在横向300km的距离上可以达到7%。结果表明，化学异常和可能的熔体有助于古老的俯冲带下地幔底部结构的形成，我们的研究为此提供了新的观测证据。

西亚地区荒漠化专题数据主要包括：西亚地区沙化土地分布图和西亚地区退化草地分布图，空间分辨率为30m。西亚地区沙化土地分布图包含的土地类型有沙地、盐碱地、裸土地和裸岩石砾地，西亚地区退化草地分布图将草地划分为高覆盖草地、中覆盖草地和低覆盖草地三类。数据由中国科学院新疆生态与地理研究所遥感与GIS重点实验室生产，生产费用由“中国科学院战略性先导科技专项XDA20030101资助”，数据空间分辨率为30m。数据主要是基于2015年TM、ETM遥感影像数据，基于去云、镶嵌与裁剪、拼接、阴影处理等预处理，借助eCognition软件进行面向对象的地类分类，实现软件自动分类和人工信息提取相结合，最后对分类结果进行人工检查与修正。数据验证方式为野外实地验证和高精度影像验证两种方式，验证精度达到85%以上。

本数据集包括青藏高原及其周边共5个采样点碳质气溶胶包括有机碳和黑碳的浓度和空间分布信息。本数据包含的黑碳和有机碳数据采用膜采样，滤膜为石英滤膜，采样器为大流量采样器，切割粒径为总悬浮颗粒物（TSP），每个滤膜采样周期为24h或48h。采用热光法测定其有机碳和黑碳含量，方法检出限分别为0.43和0.12 ug/cm2。此外，还计算了黑碳的吸光参数（MAC）。该数据集将作为青藏高原及其周边区域碳质气溶胶污染状况及背景值的参考数据集。

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels)，’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.

1）数据内容：包含中亚地区，区域范围：30°N～60°N，40°E～90°E； 2）数据来源：对CMIP数据集进行加工，采用双线性插值方法将不同分辨率模式数据插值到0.5°× 0.5°,CRU观测数据1901年——2014年； 3）数据质量：时间长度较长，数据质量良好，缺测值统一用999标识； 3）数据应用成果集前景：数据已用于进行对中亚地区温度模拟能力评估，通过计算并分析中亚地区的温区的域平均、相对误差、均方根误差、泰勒图、EOF分解、季节变化等评估气候系统模式模拟中亚地区历史气候变化的能力。 4) 数据可靠性：通过对比分析观测和模拟资料的年变化，数据结果均呈显著的增温趋势，通过对数据结果进行相关性检验，均通过99%信度检验。同时，CMIP计划数据和CRU数据也是较为常用的数据集，在很多进行气候变化的研究中，也经常采用这样的数据。

在全球变暖的背景下，干旱发生的频率和强度呈增加趋势，由于干旱灾害所引发的水资源匮乏、粮食危机、生态恶化（如荒漠化）等，直接威胁到国家的粮食安全和社会经济发展，干旱灾害风险评估及应急管理的技术水平亟待提高。“一带一路”沿线区域生态环境脆弱、农业耕地集中、干旱灾害频繁，利用遥感卫星监测大区域的干旱水平及其时空变化，对于科学掌握“一带一路”地区的干旱格局、区域分异特征，及其对农业耕地的影响具有重要的科学和现实意义。土壤相对湿度指数是表征土壤干旱的指标之一，能直接反映作物可利用水分的状况。

三江源国家公园包括长江源、黄河源、澜沧江源3 个园区，总面积为12.31 万平方公里，介于东经89°50'57"—99°14'57"，北纬32°22'36"—36°47'53"，占三江源国土面积的31.16%。 本数据集是基于《三江源国家公园总体规划》中的三江源国家公园区位图进行数字化而产生。数据包含长江源园区、黄河源园区和澜沧江园区的边界。 数据格式为Shapefile格式。推荐使用arcmap打开数据。

该数据集描述了雅鲁藏布江流域的降水时空分布，融合了 CMA、GLDAS、ITP-Forcing、MERRA2、TRMM五套再分析降水产品和卫星降水产品， 并结合流域内9个国家气象站和166个水利部雨量筒的观测降水制作而成，时间范围为1981-2016年，时间分辨率为3 h，空间分辨率为5 km，单位是mm/h。该数据将为雅江流域的研究提供更好的数据支撑，可用于研究流域水文过程对气候变化的响应等领域。具体使用信息请看随数据一同上传的说明文档。