Mountain glaciers are important freshwater resources in Western China and its surrounding areas. It is at the drainage basin scale that mountain glaciers provide meltwater that humans exploit and utilize. Therefore, the determination of glacierized river basins is the basis for the research on glacier meltwater provisioning functions and their services. Based on the Randolph glacier inventory 6.0, Chinese Glacier Inventories, China's river basin classifications (collected from the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences), and global-scale HydroBASINS (www.hydrosheds.org), the following dataset was generated by the intersection between river basins and glacier inventory: (1) Chinese glacierized macroscale and microscale river basins; (2) International glacierized macroscale river basin fed by China’s glaciers; (3) Glacierized macroscale river basin data across High Mountain Asia. This data takes the common river basin boundaries in China and the globe into account, which is poised to provide basic data for the study of historical and future glacier water resources in China and its surrounding areas.
SU Bo
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chengyan, MU Mei, PENG Xiaoqing, WU Xiaodong
This data set is the data set of Lake elements in Hoh Xil area of Qinghai Province, which records the main lake characteristics and water quality sampling and analysis data in detail. There are many lakes in Hoh Xil area of Qinghai Province, which is one of the concentrated distribution areas of lakes in Qinghai Tibet Plateau. The basic characteristics of Lake Development in this area are: large quantity, many types and complex structure. According to preliminary statistics, there are 107 lakes with an area of more than 1km2, with a total area of 3825km2 and a lake degree of about 0.05. The original data of the data set is digitized from the book "natural environment of Hoh Xil region in Qinghai Province", which includes 35 main lake characteristic data and 60 lake water chemical analysis data. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
LI Bingyuan
This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2020. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.
WANG Xufeng
The data set contains the monthly net primary productivity data of 2012-2015. The data is based on the temperature, precipitation, solar radiation and other climatic elements of the daily value data set of China's surface climate data, as well as the data of evapotranspiration et, potential PET, photosynthetic effective absorption ratio FPAR, NDVI and maximum light utilization rate, which are calculated by CASA model. The calculation results are verified by the data of Sanjiangyuan sampling point, The correlation coefficient is 0.718. The data set can be directly used for the analysis of grassland vegetation change in the Qinghai Tibet Plateau, providing the basis for dynamic monitoring of grassland change, and for the management of Grassland Change in the Qinghai Tibet Plateau.
FAN Jiangwen, XIN Liangjie, ZHANG Haiyan, YUAN Xiu
Based on 2015 ESA global land cover data (ESA GlobCover), combined with the Tsinghua university global land cover data (FROM GLC)、NASA MODIS global land cover data (MCD12Q1)、University of Maryland global land data (UMD)、USGS global land data (IGBP DISCover),we build the LUC classification system in the Tibet Plateau and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Tibet Plateau V1.0.
XU Erqi
Gf-2 satellite is the first civil optical remote sensing satellite independently developed by China with a spatial resolution better than 1 meter. It is equipped with two high-resolution 1-meter panchromatic and 4-meter multi-spectral cameras, and the spatial resolution of the sub-satellite can reach 0.8 meters. This data set is the remote sensing image data of 6 jing gaofen-2 satellite in 2017.The folder list is: GF2_PMS1_E100.5_N37.2_20171013_L1A0002678101 GF2_PMS1_E100.5_N37.4_20171013_L1A0002678097 GF2_PMS1_E100.6_N37.6_20171013_L1A0002678096 GF2_PMS2_E100.3_N37.4_20170810_L1A0002534662 File naming rules: satellite name _ sensor name _ center longitude _ center latitude _ imaging time _L****
China Centre for Resources Satellite Data and Application
The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of the Yangtze River (in the south of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
The data set is remote sensing image of Resource 3 No. 02 (ZY3-02). ZY3-02 was successfully launched from Taiyuan Satellite Launch Center at 11:17 on May 30, 2016 by Long March 4 B carrier rocket. China-made satellite imagery will be further strengthened in the areas of land surveying and mapping, resource survey and monitoring, disaster prevention and mitigation, agriculture, forestry and water conservancy, ecological environment, urban planning and construction, transportation and other fields. List of files: ZY302_PMS_E98.8_N37.4_201707_L1A0000156704 ZY302_PMS_E100.4_N37.0_20171127_L1A0000217243 ZY302_TMS_E99.5_N37.0_20170717_L1A0000160059 ZY302_TMS_E100.3_N36.6_20171127_L1A0000217279 ZY302_TMS_E100.4_N37.0_20170529_L1A0000139947 Folder Naming Rules: Satellite Name Sensor Name Central Longitude Central Latitude Acquisition Time L1****
China Centre for Resources Satellite Data and Application
This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
GAO Qingbo
This data set is the remote sensing data of gaofan-1 satellite, including the data of two scenes of PMS1 camera on 2017-8-13 and 2017-10-5, one scene of PMS2 camera on 2017-5-27, and one scene of WFV2 and WFV3 camera on September 23, 2018.File list: GF1_PMS1_E99.1_N37.2_20170813_L1A0002539236 GF1_PMS1_E101.2_N36.4_20171005_L1A0002653985 GF1_PMS2_E100.3_N37.7_20170527_L1A0002384098 GF1_WFV2_E98.4_N37.6_20180927_L1A0003481737 GF1_WFV3_E100.4_N37.3_20180927_L1A0003481706
ZHOU Shengming
Monthly meteorological data of Sanjiangyuan includes 32 national standard meteorological stations. There are 26 variables: average local pressure, extreme maximum local pressure, date of extreme maximum local pressure, extreme minimum local pressure, date of extreme minimum local pressure, average temperature, extreme maximum temperature, date of extreme maximum temperature, extreme minimum temperature and date of extreme minimum temperature, average temperature anomaly, average maximum temperature, average minimum temperature, sunshine hours, percentage of sunshine, average relative humidity, minimum relative humidity, date of occurrence of minimum relative humidity, precipitation, days of daily precipitation >=0.1mm, maximum daily precipitation, date of maximum daily precipitation, percentage of precipitation anomaly, average wind speed, maximum wind speed, date of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed and occurrence date of maximum wind speed. The data format is txt, named by the site ID, and each file has 26 columns. The names and units of each column are explained in the SURF_CLI_CHN_MUL_MON_readme.txt file. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters
ZHU Weiwei
Soil data are extremely important at both global and local scales, and in the absence of reliable soil data, land degradation assessments, environmental impact studies and sustainable land management interventions are severely hampered。By Soil information data in the urgent need of the World, especially under the background of the convention on climate change, international institute for applied systems analysis (IIASA) and the UN food and agriculture organization (FAO) and the Kyoto protocol on Soil carbon measurement and the United Nations food and agriculture organization (FAO)/international global agriculture ecological assessment (GAEZ v3.0) jointly established under the sponsorship of a new generation of World Soil Database (Harmonized World Soil Database version 1.2) (HWSD V1.2). The 2010 data set of soil texture on the qinghai-tibet plateau was culled from the world soil database.Data format :grid format, projected as WGS84.The main soil classification system used is fao-90.Unique verification identifier of core soil institution unit: Mu_global-hwsd database soil mapping unit identifier that connects GIS layers. MU_SOURCE1 and MU_SOURCE2- source database mapping unit identifiers； SEQ- soil unit sequence in the composition of soil mapping unit; Soil classification system USES fao-7 classification system or fao-90 classification system (SU_SYM74 resp.su_sym90) or fao-85 (SU_SYM85). The main fields of the soil property sheet include: ID(database ID) MU_GLOBAL(soil unit identifier) (global) SU_SYMBOL Soil mapping unit SU_SYM74(FAO74classify ); SU_SYM85(FAO85classify); SU_SYM90（FAO90The soil name in a soil classification system)； SU_CODE Soil mapping unit code SU_CODE74 Soil unit name SU_CODE85 Soil unit name SU_CODE90 Soil unit name DRAINAGE(19.5); REF_DEPTH(Soil reference depth); AWC_CLASS(19.5); AWC_CLASS(Soil available water content); PHASE1: Real (The soil phase); PHASE2: String (The soil phase); ROOTS: String (Depth classification of obstacles to the bottom of the soil)； SWR: String (Characteristics of soil moisture content)； ADD_PROP: Real (A specific soil type in a soil unit that is associated with agricultural use)； T_TEXTURE(Topsoil texture); T_GRAVEL: Real (Percentage of aggregate volume on top)；( unit：%vol.) T_SAND: Real (Top sand content)； ( unit：% wt.) T_SILT: Real (surface silt content);(unit: % wt.) T_CLAY: Real (clay content on top);(unit: % wt.) T_USDA_TEX: Real (top-level USDA soil texture classification);(unit: name) T_REF_BULK: Real (top soil bulk density);(unit: kg/dm3.) T_OC: Real (top organic carbon content);(unit: % weight) T_PH_H2O: Real (top ph) (unit: -log(H+)) T_CEC_CLAY: Real (the cationic exchange capacity of the clay layer at the top);(unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of topsoil) (unit: cmol/kg) T_BS: Real (top basic saturation);(unit: %) T_TEB: Real (top exchange base);(unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top-level sulfate content);(unit: % weight) T_ESP: Real (top layer exchangeable sodium salt);(unit: %) T_ECE: Real (top-level conductivity).(unit: dS/m) S_GRAVEL: Real (percentage of bottom gravel volume);(unit: % vol.) S_SAND: Real (content of underlying sand);(unit: % wt.) S_SILT: Real (substratum silt content);(unit: % wt.) S_CLAY: Real (clay content in the bottom layer);(unit: % wt.) S_USDA_TEX: Real (USDA underlying soil texture classification);(unit: name) S_REF_BULK: Real (bulk density of underlying soil);(unit: kg/dm3.) S_OC: Real (bottom organic carbon content);(unit: % weight) S_PH_H2O: Real (base ph) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying cohesive soil);(unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of underlying soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation);(unit: %) S_TEB: Real (underlying exchangeable base);(unit: cmol/kg) S_CACO3: Real (content of underlying carbonate or lime) (unit: % weight) S_CASO4: Real (substrate sulfate content);(unit: % weight) S_ESP: Real (underlying exchangeable sodium salt);(unit: %) S_ECE: Real (underlying conductivity).(unit: dS/m) This database is divided into two layers, in which the top layer (T) has a soil thickness of (0-30cm) and the bottom layer (S) has a soil thickness of (30-100cm).。 Refer to the instructions for other attribute values HWSD1.2_documentation.pdf，The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description andHWSD.mdb。
Food and Agriculture Organization of the United Nations（FAO）
Based on the average NDVI (spatial resolution 250m) of MODIS during the growing season from 2000 to 2018, the trend of NDVI was calculated by using Mann-Kendall trend detection method. Three parks of Three River Source National Park are calculated (CJYQ: Yangtze River Park; HHYYQ: Yellow River Park; LCJYQ: Lancang River Park). CJYQ_NDVI_trend_2000_2018_ok.tif: Changjiang Source Park NDVI trend. CJYQ_NDVI_trend_2000_2018_ok_significant.tif: Changjiang Source Park NDVI change trend, excluding the area that is not significant (p > 0.05). CJYYQ_gs_avg_NDVI_2000.tif: The average NDVI of the Yangtze River Source Park in 2000 growing season. Unit NDVI changes every year.
WANG Xufeng
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