The dataset of airborne LiDAR mission in the Dayekou flight zone on Jun. 20, 2008 included peak pulse data (*.LAS), full waveform data (.lgc), CCD photos, DEM, DSM and DOM. The DEM, DSM and DOM data are stored along with the Dataset of airborne LiDAR mission in the Dayekou flight zone on Jun. 23, 2008. The flight routes were as follows: {| ! flight route ! startpoint lat ! startpoint lon ! endpoint lat ! endpoint lon ! altitude (m) ! length (km) ! photos |- | 1 || 38°32′05.38″ || 100°12′24.59″ || 38°29′32.76″ || 100°18′35.69″ || 3650 || 10.1 || 49 |- | 2 || 38°32′11.13″ || 100°12′28.42″ || 38°29′42.06″ || 100°18′30.89″ || 3650 || 9.9 || 46 |- | 3 || 38°32′16.88″ || 100°12′32.24″ || 38°29′47.81″ || 100°18′34.72″ || 3650 || 9.9 || 47 |- | 4 || 38°32′22.63″ || 100°12′36.07″ || 38°29′56.20″ || 100°18′32.15″ || 3650 || 9.7 || 45 |- | 5 || 38°32′28.38″ || 100°12′39.90″ || 38°30′02.62″ || 100°18′34.33″ || 3650 || 9.7 || 47 |- | 6 || 38°32′37.44″ || 100°12′35.66″ || 38°30′10.63″ || 100°18′32.68″ || 3650 || 9.8 || 44 |- | 7 || 38°32′46.50″ || 100°12′31.43″ || 38°30′19.72″ || 100°18′28.37″ || 3650 || 9.8 || 47 |}

The object of this dataset is to support the atmospheric correction data for the satellite and airborne remote-sensing. It provides the atmospheric aerosol and the column content of water vapor. The dataset is sectioned into two parts: the conventional observations data and the observations data synchronized with the airborne experiments. The instrument was on the roof of the 7# in the Wuxing Jiayuan community from 1 to 24 in June. After 25 June, it was moved to the ditch in the south of the Supperstaiton 15. The dataset provide the raw observations data and the retrieval data which contains the atmosphere aerosol optical depth (AOD) of the wavebands at the center of 1640 nm, 1020 nm, 936 nm, 870 nm, 670 nm, 500 nm, 440 nm, 380 nm and 340 nm, respectively, and the water vapor content is retrieved from the band data with a centroid wavelength of 936 nm. The continuous data was obtained from the 1 June to 20 September in 2012 with a one minute temporal resolution. The time used in this dataset is in UTC+8 Time. Instrument: The sun photometer is employed to measure the character of atmosphere. In HiWATER, the CE318-NE was used.

The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.

The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the Biandukou foci experimental area on Oct. 18, 2007, during the pre-observation period. The ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners. Simultaneous with the satellite overpass, numerous ground data were collected: the soil temperature , volumetric soil moisture (cm^3/cm^3), soil salinity (s/m), soil conductivity (s/m) by the Hydra probe, the surface radiative temperature by the handheld infrared thermometer, gravimetric soil moisture, volumetric soil moisture, and soil bulk density by drying soil samples from the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Those provide reliable ground data for the development and validation of soil moisture, soil freeze/thaw algorithms and the forward model from active remote sensing approaches.

The Sentinel-1A/B satellite uses a near-polar sun-synchronous orbit with an orbital altitude of 693 km, an orbital inclination of 98.18°, and an orbital period of 99 minutes. It is equipped with a C-band Synthetic Aperture Radar (SAR) with a designed service life of 7 years (12 years expected). Sentinel-l has a variety of imaging methods that enable different polarization modes such as single-polarization and dual-polarization. Sentinel-1A SAR has four working modes: Strip Map Mode (SM), Extra Wide Swath (EW), Interferometric Wide Swath (IW) and Wave Mode (WV). Satellite A was successfully launched in April 2014. The revisit period of the same region was 12 days. Satellite B successfully operated on orbit in April 2016. The current revisiting period reached 3 to 6 days. After the operation of two satellites, the S1 data acquisition frequency in the Antarctic region increased greatly. This data set comprises the Sentinel-1 SAR data for the Antarctic ice sheet and the Greenland Ice Sheet area. The data band comprises C-band extra wide multiview data with a resolution of 20 m*40 m. The temporal resolution is 12 days and is related to the round-trip period, the width is 400 km, the noise level is -25 dB, and the radiation measurement accuracy is 1.0 dB. The annual temporal coverage of these data is October to the next March in the Antarctic and April to September in Greenland, and the spatial coverage comprises the Antarctic ice sheet ice shelf area and Greenland ice sheet.

The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature of different kinds of underlying surface, including greenhouse film, the roof, road, ditch, concrete floor and so on, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal infrared sensor and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time and other details On 25 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers recorded. On 26 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers while greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 29 June, 2012, concrete floor surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 30 June, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 10 July, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 26 July, 2012, asphalt road, concrete floor, bare soil and melonry surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of WiDAS go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 2 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, twelve sites were selected according to the flight strip of the WiDAS sensor, and for each site one plot surface temperatures were recorded continuously during the sensor of WiDAS go into the region. On 3 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, fourteen sites were selected according to the flight strip of the WiDAS sensor, and for each site three plots surface temperatures were recorded continuously during the sensor of WiDAS go into the region. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. The observation heights of the self-recording point thermometer for the greenhouse film and the concrete floor were 0.5 m and 1 m, respectively. All instruments were calibrated three times (on 6 July, 5 August and 20 September, 2012) using black body during observation. 3. Data storage All the observation data were stored in excel.

The dataset of photosynthesis was observed by LI-6400XT Portable Photosynthesis System in the artificial oasis eco-hydrology experimental area of the Heihe River Basin. Observation items included two main crops in the middle reaches of Heihe river: wheat and maize, which located in the town of Pingchuan in Linze and the Super Station of Wuxing, respectively. Observation periods lasted from mid-May to September. This dataset included the raw observation data and the pretreatment data of wheat and maize observed by LI-6400 during the observation periods. Objectives of observation: The photosynthetic datasets can be used in the study of plant physiological ecology characteristic and the simulation and validation for the eco-hydrological models. Instrument and theory of the observation: (1) Measuring instrument: LI-6400XT Portable Photosynthesis System; (2) Measuring theory: Using the infrared gas analyzer to measure the change of CO2 concentration, and then measuring the differences of CO2 concentration between the sample chamber and the referenced chamber so as to acquire the net productivity of the leaf. Time and site of observation: (1) Observation site of the wheat: in the town of Pingchuan in Linze; Observation time: 2012-05-17，2012-06-08 to 2012-6-13; (2) Observation site of the maize: in the Super Station of Wuxing; Observation time: from 2012-05-19 to 2012-08-15. The time used in this dataset is in UTC+8 Time. Data processing: The raw data of LI-6400 were archived in text format and can be opened by text editor or excel, the preprocessed data were in Excel format. Every time period of observation was archived in a single document, named as “date + type + time”, every leaf was recorded 3 times, and then added a remark.

The assessment of changes in the atmospheric water cycle and the associated impacts in a key area of the Tibetan Plateau under the background of the global warming was a major component of the research project “The Environmental and Ecological Science of West China” run by the National Natural Science Foundation of China. The leading executive of the project was Xiangde Xu from the Chinese Academy of Meteorological Sciences. The project ran from January 2006 to December 2008. The following data were collected by the project of the Sino-Japan Joint Research Center of Meteorological Disaster (JICA Project): 1. Observation category, time period and number of stations 1) JICA AWS data: From January to July of 2008, 73 automatic stations (including 5 automatic stations of the Chinese Academy of Sciences) collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 2) JICA GPS water vapour data: From January to October of 2008, 24 observation stations collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 3) JICA encrypted observation GPS sonde data: From March to July of 2008, observations were made in Tibet, Yunnan, Sichuan and other provinces or autonomous regions (detailed observation time and location data can be found in the data catalogue). 2. Observation categories, data content 1) GPS water vapour Data content: serial number, station name (Chinese), station number, longitude, latitude, altitude, year, month, day, time, surface pressure, surface air temperature, relative humidity, total delay (m), precipitation (cm) (Measurement interval: 1 hour). 2) GPS encrypted sonde Data content: air pressure P, temperature T, relative humidity RH, V component, U component, vertical height H, dew point temperature Td, water vapour content Mr, wind direction Wd, wind speed Ws, longitude Lon, latitude Lat, radar height RdH. A value of "-999.90" means no observation data. 3) AWS Data content: station number, longitude, latitude, elevation, site level, total cloud volume, wind direction, wind speed, sea level pressure, 3-hour pressure variable, past weather 1, past weather 2, 6-hour precipitation, low cloud form, low cloud volume, low cloud height, dew point, visibility, current weather, temperature, medium cloud form, high cloud form, 24-hour temperature variable, 24-hour pressure variable. Project Science Advisers: Guoguang Zheng, Xiaofeng Xu, Xiuji Zhou, Zechun Li, Jifan Niu, Jianmin Xu, Lianshou Chen, Dahe Qin, Yihui Ding Project Superintendent: Jixin Yu Project Executives: Renhe Zhang, Xiangde Xu Data set hosting organizations: Chinese Academy of Meteorological Sciences, JICA Project Implementation Expert Group, State Key Laboratory of Severe Weather, JICA Project Implementation Office. Collaborative organizations involved in the production of the data set: Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, National Satellite Meteorological Center, The Research Center for Atmospheric Sounding Techniques, National Meteorological Center, National Meteorological Information Center, National Climate Center, Sichuan Meteorological Department, Yunnan Meteorological Department, Tibet Autonomous Region Meteorological Department, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Tianjin Meteorological Department. Data set implementation organizations: Beijing Headquarters of JICA Project; JICA Project Sub-center in Sichuan Province, Yunnan Province, Tibet Autonomous Region and Institute of Tibetan Plateau Research, Chinese Academy of Sciences.

The dataset of spectral reflectance measurements was obtained in the A'rou foci experimental area on Mar. 12, 2008. The land was covered with snow 10cm deep. Observed objects included snow, sandstone, grass and ice. The data were archived in the ASCII format, with the first five rows as the file header and the following two columns as wavelength (nm) and reflectance (percentage) respectively, and can be opened by .txt or wordpad. Raw data were binary files direct from ASD (by ViewSpecPro).

Third Pole 1:100,000 airport and runway data set include：airport（Tibet_Airport）and(Tibet_Airport_runways) vector space data set and its attribute name:Airport name(Name）、Name of airport（CNTRY_NAME）、Airport country abbreviation（CNTRY_CODE）、latitude（LATITUDE）、longitude（LONGITUDE）. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface

This dataset contains the flux measurements from site No.1 eddy covariance system (EC) in the flux observation matrix from 4 June to 17 September 2012. The site (100.35813° E, 38.89322° N) was located in a cropland (vegetable surface) in the Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1552.75 m. The EC was installed at a height of 3.8 m; the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (Gill&Li7500A) was 0.2 m. Raw data acquired at 10 Hz were processed using the Eddypro post-processing software (Li-Cor Company, http://www.licor.com/env/products/ eddy_covariance/software.html), including spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, angle of attack correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. Moreover, the observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC), which was proposed by Foken and Wichura [1996]: class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), representing high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day; the missing data were replaced with -6999. Moreover, suspicious data were marked in red. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m^3), CO2 mass density (CO2, mg/m^3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m^2), latent heat flux (LE, W/m^2), carbon dioxide flux (Fc, mg/ (m^2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xlsx format. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.

By applying supply-demand balance analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, and the results were used to assess the vulnerability of the water resources system in the basin. The IPAT equation was used to establish a future water resource demand scenario, which involved setting various variables, such as the future population growth rate, economic growth rate, and water consumption per unit GDP. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydro-meteorological Institute, a model of the variation trends of the basin under a changing climate was designed. The glacial melting scenario was used as the model input to construct the runoff scenario in response to climate change. According to the national regulations of the water resource allocation in the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the grain production-related land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources in scenarios of climate change, glacial melting and population growth was analysed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities in the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.