This data set is extracted from the second Glacier Inventory Data Set of China for Three River Source area. The file is SHP format. The attribute data are as follows: Glc_Name (glacier name), Drng_Code (basin code), FCGI_ID (first glacier catalogue code), GLIMS_ID (GLIMS glacier code), Mtn_Name (mountain system name), Pref_Name (administrative division), Glc_Long (glacier longitude), Glc_Lati (glacier latitude), Glc_Area (glacier area), Abs_Accu (absolute area accuracy), Rel_Accu (relative area accuracy), Deb_Area (surface Moraine Area), Deb_A_Accu (absolute accuracy of surface moraine Area), Deb_R_Accu (relative accuracy of surface moraine area)、Glc_Vol_A (estimation of glacier volume 1)、Glc_Vol_B (estimation of glacier volume 2)、Max_Elev (maximum glacier elevation)、Min_Elev (minimum glacier elevation)、Mean_Elev (average glacier elevation)、MA_Elev (median area height of glacier)、Mean_Slp (average glacier slope)、Mean_Asp (average glacier slope direction)、Prm_Image (major remote sensing data)、Aux_Image (auxiliary remote sensing data)、Rep_Date (glacier catalogue represents date)、Elev_Src (elevation data source)、Elev_Date (elevation represents date)、Compiler (glacier cataloguing editor)、Verifier (glacier cataloguing verifier).

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).

The data set contains NPP products data produced by the maximum synthesis method of the three source regions of the Yellow River, the Yangtze River and the Lancang River. The data of remote sensing products MOD13Q1, MOD17A2, and MOD17A2H are available on the NASA website (http://modis.gsfc.nasa.gov/). The MOD13Q1 product is a 16-d synthetic product with a resolution of 250 m. The MOD17A2 and MOD17A2H product data are 8-d synthetic products, the resolution of MOD17A2 is 1 000 m, and the resolution of MOD17A2H is 500 m. The final synthetic NPP product of MODIS has a resolution of 1 km. The downloaded MOD13Q1, MOD17A2, and MOD17A2H remote sensing data products are in HDF format. The data have been processed by atmospheric correction, radiation correction, geometric correction, and cloud removal. 1) MRT projection conversion. Convert the format and projection of the downloaded data product, convert the HDF format to TIFF format, convert the projection to the UTM projection, and output NDVI with a resolution of 250 m, EVI with a resolution 250 m, and PSNnet with resolutions of 1 000 m and 500 m. 2) MVC maximum synthesis. Synthesize NDVI, EVI, and PSNnet synchronized with the ground measured data by the maximum value to obtain values corresponding to the measured data. The maximum synthesis method can effectively reduce the effects of clouds, the atmosphere, and solar elevation angles. 3) NPP annual value generated from the NASA-CASA model.

The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.

The data set contains vegetation quadrat survey data for Qumalai, Mado and Hoh Xil from August 3, 2017, to August 9, 2017. The main survey contents are coverage, altitude and above-ground biomass. It covers three vegetation types: alpine grassland, alpine wetland and alpine meadow. The latitude, longitude, altitude, total coverage, species name and quantity of the quadrat were recorded, and three samples of each species were selected to measure the altitude, the total above-ground biomass, and the above-ground biomass of each category.

On August 22, 2018, a DJI camera was used in the fixed sample of Lancang River headwaters. The overlap degree of adjacent photos was not less than 70% according to the set flight route. The Orthophoto Image and DSM were generated using the photographs taken. The Orthophoto Image included three bands of red, green and blue, with a ground resolution of 2.5 cm, a shooting area of 1000m x 1000m and a DSM resolution of 4.5 cm. Due to the communication failure, the middle four airstrips were not photographed, so there was a band in the middle of the image missing.

On August 19, 2018, the wetland sample in Qumali County, located in the source area of the Yangtze River, was aerially photographed by DJI Elf 4 UAV. A total of 31 routes were set up, flying at a height of 100 m, and the overlap of adjacent photographs was not less than 70%. A total of 1551 aerial photographs were obtained and stored in two folders named "Drone Photoes Part1" and "Drone Photoes Part2".

This data comes from the National Catalogue Service for Geographic Information, which was provided to the public free of charge by the National Basic Geographic Information Center in November 2017. We spliced ​​and trimmed Three Rivers Source Region as a whole to facilitate its use in the study of Three Rivers Source Region. The current status of the data is 2015. This dataset is 1:25 million traffic data in the Three Rivers Source Region area, including two layers of highway (LRDL) and railway (LRRL). Highways (LRDL) include national, provincial, county, rural, and other highways; railways (LRRL) include standard-gauge, narrow-gauge, subway, and light rail.        Highway (LRDL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 420301 RN Road number X828 NAME Road name Zhuoxiao fork-Baola Peak fork RTEG Road Level 4 TYPE Road type elevated         Meaning of highway attribute items: Attribute item Code Description GB 420101 National road 420102 National road in building 420201 Provincial road 420102 Provincial highway in building 420301 County road 420302 County road in building 420400 Country road 420800 Machine tillage 440100 Simple road 440200 Village road 440300 Trail         Railway (LRRL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 410101 RN Railway number 0907 NAME Railway name Qinghai-Tibet Railway TYPE Rail type

The Three-River-Source National Park with an area of 123,100 km2 and include three sub regions, they are source region of the Yangtze River in the national park, source region of Yellow River in the national park and source region of Lancang River in the national park. The national park is located between longitude 89°50'57" -- 99°14'57", latitude 32°22'36" -- 36°47'53". It accounts for 31.16% of the total area of Three-River-Source region. This data set is generated by digitizing the location map of Three-River-Source national park in the comprehensive planning of Three-River-Source national park. The data include the boundary for the national park. Data format is Shapefile. Arcmap is recommended to open the data.

This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, water system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example NAME Name Ramsay Laboniwa PINYIN Chinese Pinyin Lamusailabaoniwa CLASS Toponymic Classification Code HB

This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 residential place names (AANP) in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AANP): Attribute Item Description Fill in Example NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun CLASS Geographical Name Classification Code AK GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township

The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.