The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.

This dataset is based on the sixth edition of the MODIS normalized difference vegetation index product (2001-2014) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey USGS EROS. The NDVI has a time resolution of 16 days and a spatial resolution of 0.05 degree. First,the NDVI data products were re-sampled from the spatial resolution of 0.05 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.

The data is the distribution map of 100,000 deserts in the Tarim River Basin. This data uses 2000 TM images as the data source to interpret, extract and revise, and uses remote sensing and geographic information system technology in combination with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code), ashm_id (desert code), of which desert code is as follows: flowing sand 2341010, semi-flowing sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000, saline-alkali land 2343000

Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.

The data is tailored from "China's 1:1 million wetland data". "China's 1:1 million wetland data" mainly reflects the national wetland information in the 2000's, which is expressed by the decimal system of geographical coordinates. The main contents include: types of wetland, water supply types of wetland, soil types, main vegetation types, geographical areas, etc. The information classification and coding standard of China sustainable development information sharing system has been implemented. Data source of the database: 1:20 swamp map (internal version), 1:500000 swamp map of Qinghai Tibet Plateau (internal version), 1:1 million swamp survey data and 1:4 million swamp map of China; processing steps: data source selection, preprocessing, digitization and coding of swamp wetland elements, data editing and processing, establishment of topological relationship, edge connection processing, projection conversion, place name and other attribute databases Link and get property data.

The source of the data is a 1:2500000-scale map series, "Geocryological Map of Russia and Neighboring Republics", published by Russia from 1991 to 1996, which is labelled in Russian and includes a total of 16 images. In 1998, Zaitsev and others translated it into English. In this study, seven of the images were digitized: 1) Distribution of frozen and unfrozen ground, 2) Mean annual temperature of unfrozen ground at the depth of zero annual amplitude (note that there is some uncertainty because the depth of zero amplitude is not provided, and data on this parameter is generally lacking), 3) Thickness of permafrost, 4) Depth from the surface and thickness of relict permafrost, 5) Distribution of permafrost containing cryopegs, 6) Thickness of permafrost containing cryopegs, 7) Distribution of permafrost with depth. 1. The data include multiple vector layers: (1) permafrost distribution, (2) permafrost temperature, (3) permafrost thickness, (4) permafrost formation conditions, and (5) the correction image. 2. The permafrost distribution map includes the following fields: AREA, PERIMETER, FROZEN_, FROZEN_ID: POLY_, POLY_, RINGS_OK, RINGS_NOK, A, FROZEN_SOI (frozen soil layer), and temperature. FROZEN_SOI are the Chinese and English representations of the type of frozen soil, respectively. 4. Frozen soil properties: Frozen soil Continuous predominantly unfrozen 1-5 Continuous permafrost -3- -5 Continuous unfrozen ground 4-6 Discontinuous permafrost 0.5- -2 Predominantly continuous permafrost -1- -3 Predominantly unfrozen ground 1-3 5. Projection information: PROJCS["Asia_North_Equidistant_Conic", GEOGCS["GCS_North_American_1927", DATUM["North_American_Datum_1927", SPHEROID["Clarke_1866",6378206.4,294.9786982]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433]], PROJECTION["Equidistant_Conic"], PARAMETER["False_Easting",0.0], PARAMETER["False_Northing",0.0], PARAMETER["longitude_of_center",100.0], PARAMETER["Standard_Parallel_1",15.0], PARAMETER["Standard_Parallel_2",58.3], PARAMETER["latitude_of_center",60.0], UNIT["Meter",1.0]]

The ground sample data was collected by LAI-2000 canopy analyzer, and the collection area was located in Dayekou, Wuxing Village (2012) and other areas. The main measure of vegetation is corn. The LAI value of the corn was obtained using the LAI2000, and the observation was repeated twice in a pattern of “one up and four down”. The leaf area of each leaf of the corn plant was obtained using CD202, and a total of three corns were collected.

From the beginning of June to the beginning of August, 2011, permafrost investigation was carried out in the West Branch of the headwater of Heihe River. Along the section between hot water dabanya and Shimian mine fork of erga highway, with the decrease of altitude, 7 thermowells T1, T2, T3, T4, T7, T5 and T6 were successively arranged, and thermotubes were arranged for ground temperature monitoring. The instrument used is a thermistor thermometer developed by the State Key Laboratory of Permafrost Engineering, Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences. The resistance value is measured by fluke multimeter, and then converted into temperature value with accuracy of ± 0.05 ° C. In order to reduce the impact of the road on the drilling temperature, it is required that the vertical distance between all the drilling holes and the road shall be at least greater than 100 m when determining the hole location. Except that T1 temperature hole of hot water Daban pass is located in the south of erga highway, the other six holes are located in the north of the highway.

According to the monthly temperature observation data of each conventional meteorological station in Heihe River basin set up by China Meteorological Administration, the annual air freeze-thaw index of each meteorological station is calculated, and then the annual average value of 1960-2004 is obtained. Finally, based on the regression relationship between the multi-year mean value of air freeze-thaw index and altitude of each meteorological station, and with the aid of 1 km DEM data, the spatial distribution map of air freeze-thaw index in Heihe River Basin is constructed.

This data mainly includes ten day runoff data of Yingluo gorge and Zhengyi gorge in Heihe River Basin, among which the time range of Yingluo gorge data is 1944-2010 and Zhengyi gorge data is 1947-2010. Source: Heihe River Basin Authority. Data unit: 100 million cubic meters / 10 days. Data format: Excel "Yingluo gorge 2" and "Yingluo gorge 2 (2)" in the data table are the ten day runoff data of Yingluo gorge, the same as "Yingluo gorge" in the data table, and Yingluo gorge 2 (2) contains the chart.

"Hydrological ecological economic process coupling and evolution of Heihe River basin governance under the framework of water rights" (91125018) project data collection - economic and social data of Heihe River 2010 . 1. Data overview: Economic and social data of Heihe River 2010. 2. Data content: Economic and social data of Ganzhou District, Linze County and Gaotai County of Heihe River Basin 2010.

1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.