The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
Based on AVHRR-CDR SR products, a daily cloud-free snow cover extent dataset with a spatial resolution of 5 km from 1981 to 2019 was prepared by using decision tree classification method. Each HDF4 file contains 18 data elements, including data value, data start date, longitude and latitude, etc. At the same time, to quickly preview the snow distribution, the daily file contains the snow area thumbnail, which is stored in JPG format. This data set will be continuously supplemented and improved according to the real-time satellite remote sensing data and algorithm update (up to may 2019), and will be fully open and shared.
HAO Xiaohua
The dataset include ground-based passive microwave brightness temperature, multi-angle brightness temperature, ten-minute 4-component radiation and snow temperature, daily snow pit data and hourly meteorological data observed at Altay base station（lon：88.07、lat: 44.73）from November 27, 2015 to March 26, 2016. Daily snow pit parameters include: snow stratification, stratification thickness, density, particle size, temperature. These data are stored in five NetCDF files: TBdata. nc, TBdata-multiangle. nc, ten-minute 4 component radiation and snow temperature. nc, hourly meteorological and soil data. nc and daily snow pit data.nc. TBdata. nc is brightness temperature at 3 channels for both polarizations automatically collected by a six-channel dual polarized microwave radiometer RPG-6CH-DP. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. TBdata-multiangle.nc is 7 groups of multi-angle brightness temperatures at 3 channels for both polarizations. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. The ten-minute 4 component radiation and snow temperature.nc contains 4 component radiation and layered snow temperatures. The contents include Year, month, day, hour, minute, SR_DOWN, SR_UP, LR_DOWN, LR_UP, T_Sensor, ST_0cm, ST_5cm, ST_15cm, ST_25cm, ST_35cm, ST_45cm, ST_55cm. The hourly meteorological and soil data.nc contains hourly weather data and layered soil data. The contents include Year, month, day, hour, Tair, Wair, Pair, Win, SM_10cm, SM_20cm, Tsoil_5cm, Tsoil_10cm, Tsoil_15 cm, Tsoil_20cm. The daily snow pit data.nc. is manual snow pit data. The observation time was 8:00-10:100 am local time. The contents include Year, month, day, snow depth, thickness_layer1, thickness_layer2, thickness_layer3, thickness_layer4, thickness_layer5, thickness_layer6, Long_layer1, Short_layer1, Long_layer2, Short_layer2, Long_layer3, Short_layer3, Long_layer 4, Short_layer4, Long_layer5, Short_layer5, Long_layer6, Short_layer 6, Stube, Snow shovel_0-10, Snow shovel _10-20, Snow shovel _20-30, Snow shovel _30-40, Snow shovel _40-50, Snow fork_5, Snow fork _10, Snow fork _15, Snow fork_20, Snow fork_25, Snow fork_30, Snow fork_35, Snow fork_40, Snow fork_45, Snow fork_50, shape1, shape2, shape3, shape4, shape5,
DAI Liyun
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).
WANG Lei
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
This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"
KANG Jian, LI Xin, MA Mingguo
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since the end of June 2013. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification.
KANG Jian, LI Xin, MA Mingguo
First, Data Description The data includes stable hydrogen and oxygen isotope data of snow melt water, river water and soil water from July 2013 to April 2014. Second, Sampling Sites The snowmelt water sampling point is located in the middle of the third area, with a latitude and longitude of 99°53′28.004′′E, 38°13′25.781′′N, and the number of acquisitions is 3 times; The river water sampling point is located at the exit of the Hulugou Basin, with a latitude and longitude of 99°52′47.7′′E, 38°16′11′′N, and the sampling frequency is once a week; The soil water sampling point is located in the middle and lower part of the Hongnigou catchment area, with a sampling depth of 90cm and 180cm underground, and a latitude and longitude of 99°52'25.98′′E, 38°15′36.11′′N. Third, Testing Method The samples were measured by L2130-i ultra-high precision liquid water and water vapor isotope analyzer.
CHANG Qixin, SUN Ziyong
1、 Data Description: the data includes the content of silica in snowmelt water and soil water in hulugou small watershed from May 2013 to April 2014. 2、 Sampling location: the sampling point of snowmelt water is located near 600m below No.2 meteorological station, with ground elevation of 3514.45m, longitude and latitude of 99 ° 53 ′ 20.655 ″ e, 38 ° 14 ′ 14.987 ″ n. The sampling point of soil water is located at 300m above and below the No.2 meteorological station, with the longitude and latitude of 99 ° 53 ′ 31.333 ″ E and 38 ° 13 ′ 50.637 ″ n. 3、 Measurement method: the content of silica in the sample was measured by ICP-AES. Silicon dioxide is replaced by the value of Si in the solution.
SUN Ziyong, CHANG Qixin
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since January 2014. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. Please refer to "waternet data document 20141206. Docx" for details
KANG Jian, LI Xin, MA Mingguo
The data set is the meteorological and observational data of hulugou shrub experimental area in the upper reaches of Heihe River, including meteorological data, albedo data and evapotranspiration data under shrubs. 1. Meteorological data: Qilian station longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3232.3m, scale meteorological data from January 1, 2012 to December 31, 2013. Observation items include: temperature, humidity, vapor pressure, net radiation, four component radiation, etc. The data are daily scale data, and the calculation period is 0:00-24:00 2. Albedo: daily surface albedo data from January 1, 2012 to July 3, 2014, including snow and non snow periods. The measuring instrument is the radiation instrument on the 10m gradient tower in hulugou watershed. Among them, the data from August 4 to October 2, 2012 was missing due to instrument circuit problems, and the rest data quality was good 3. Evapotranspiration: surface evapotranspiration data of Four Typical Shrub Communities in hulugou watershed. The observation period is from July 18 to August 5, 2014, which is the daily scale data. The data include precipitation data, evaporation and infiltration data observed by lysimeter. The data set can be used to analyze the evapotranspiration data of alpine shrubs and forests. The evapotranspiration of grassland under canopy was measured by a small lysimeter with a diameter of 25 cm and a depth of 30 cm. Two lysimeters were set up in each shrub plot, and one lysimeter was set for each shrub in transplanting experiment. The undisturbed undisturbed soil column with the same height as the barrel is placed in the inner bucket, and the outer bucket is buried in the soil. During the embedding, the outer bucket shall be 0.5-1.0 cm higher than the ground, and the outer edge of the inner barrel shall be designed with a rainproof board about 2.0 cm wide to prevent surface runoff from entering the lysimeter. Lysimeter was set up in the nearby meteorological stations to measure grassland evapotranspiration, and a small lysimeter with an inner diameter of 25 cm and a depth of 30 cm was also set up in the sample plot of Picea crassifolia forest to measure the evaporation under the forest. All lysimeters are weighed at 20:00 every day (the electronic balance has a sensing capacity of 1.0 g, which is equivalent to 0.013 mm evaporation). Wind proof treatment should be taken to ensure the accuracy of measurement. Data processing method: evapotranspiration is mainly calculated by mass conservation in lysimeter method. According to the design principle of lysimeter lysimeter, evapotranspiration is mainly determined by the quality difference in two consecutive days. Since it is weighed every day, it is calculated by water balance.
SONG Yaoxuan, LIU Zhangwen
The dataset of fresh snow properties observations was obtained at the temporary sampling plot in the Qilian county on Mar. 20, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included: (1) Snow parameters such as snow depth, snow grain size by the handheld microscope, and snow density by the snow shovel (2) Fresh snow albedo by the total radiometer (3) Fresh snow spectrum by ASD Two files including raw data and preprocessed data were archived.
GE Chunmei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, LIU Yan, ZHANG Pu
The dataset of snow spectral reflectance observations was obtained in the Binggou watershed foci experimental area from Dec. 5 to Dec. 15, 2007 during the pre-observation period. The aims of the measurements were to verify feasibility of the predetermined observation schemes and to collect data for retrieval from remote sensing approaches. All data were acquired by ASD spectrometer from Xinjiang Meteorological Administration. Observation items included: (1) Random observations on snow spectrum in the chosen snowpack at the Binggou cold region hydrometeorological station on Dec. 5, 6 and 7, 2007 (2) Snow spectrum observations in BG-A simultaneous with MODIS and Terra MISR on Dec. 10, 2007 (3) The pure and the mixed snow pixel spectrum in BG-A on Dec. 15, 2007 (4) Multi-angle snow spectrum in the chosen snowpack in BG-A on Dec. 15, 2007 Seven subfolders including raw data and pre-processed data are named after the acquisition time, Dec. 5, 2007, Dec. 6, 2007, Dec. 7, 2007, Dec. 10, 2007, Dec. 13, 2007, Dec. 15, 2007 and Dec. 15, 2007, respectively.
ZHANG Pu, LIU Yan
The dataset of ground truth measurements for snow synchronizing with the airborne PHI mission was obtained in the Binggou watershed foci experimental area on Mar. 24, 2008. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A. (2) Snow parameters as the snow surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, and snow density by the aluminum case in BG-A1, BG-A2, BG-B, BG-D, BG-E and BG-F5 (three sampling units each) from 11:11-12:35 (BJT) with the airplane overpass. 64 points were selected by four groups. (3) Snow albedo by the total radiometer in BG-A. (4) The snow spectrum by ASD (Xinjiang Meteorological Administration) in BG-A11 Two files including raw data and preprocessed data were archived.
BAI Yanfen, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LIANG Xingtao, LIU Zhigang, QU Wei, REN Jie, FANG Li, LI Hua, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow was obtained, synchronizing with airborne microwave radiometers (K&Ka bands) mission in the Binggou watershed foci experimental area on Mar. 29, 2008. Those provide reliable ground data for retrieval of snow properties and parameters, especially snow depth and snow water equivalent study. Observation items include (1) snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) snow parameters in BG-A (18 points), BG-B (20 points), BG-EF (20 points) and BG-I (20 points): snow depth by the ruler, the snow temperature (mean of two measurements) by the probe thermometer, snow grain size by the handheld microscope, snow density by the cutting ring for each snow layer, and the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer. For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. Two files including raw data and pre-processed data were archived.
BAI Yanfen, BAI Yunjie, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, LIU Yan, ZHANG Pu
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.1 (freeze/thaw status), No. 2 (snow parameters) and No. 3 (freeze/thaw status) quadrates of the A'rou foci experimental areas on Mar. 12, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:29 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Center and corner points of each subsite were chosen for all observations except for the cutting ring measurements which only observed the center points. In No. 1 quadrate, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, soil volumetric moisture by ML2X, the soil volumetric moisture profile (10cm, 20cm, 30cm, 40cm, 60cm and 100cm) by PR2, the mean soil temperature from 0-5cm by the probe thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). In No. 2 quadrate, simultaneous with ASAR, snow parameters were measured, the snow surface temperature by the thermal infrared probe, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, the snow surface temperature and the snow-soil interface temperature by the thermal infrared probe, snow spectrum by ASD, and snow albedo by the total radiometer. In No. 3 quadrate soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm by the probe thermometer (5# and 7#), the surface radiative temperature by the hand-held infrared thermometer (5#), and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Besides, GPR (Ground Penetration Radar) observations were also carried out in No. 1 quadrate of A'rou. Those provide reliable ground data for retrieval and verification of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yanfen, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, QU Wei, CHANG Cun, DOU Yan, MA Zhongguo, YU Meiyan, ZHAO Jin, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, PATRICK Klenk, YUAN Xiaolong
The dataset of ground-based microwave scatterometer and snow parameter observations was obtained in the Binggou watershed experimental area on Mar. 16, 2008. Observation items included: (1) Snow backscattering coefficient by the scatterometer (2) Snow parameters as the snow surface temperature by the probe thermometer, snow grain size by the handheld microscope, snow density by the snow shovel, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer in BG-I. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) at the Dadongshu mountain pass; the major and minor axis and shape of the snow layer grain through the snow sieve. (4) Snow albedo by the total radiometer from 10:29 to 15:00 (5) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork at the Dadongshu mountain pass Two subfolders including raw data and preprocessed data were archived.
LIU Zengcan, LIU Zengcan, QIN Wei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, ZHANG Pu, JIANG Tenglong
The dataset of ground truth measurements for snow synchronizing with MODIS was obtained in the Binggou watershed foci experimental area on Mar. 14, 2008. Those provide reliable data for snow-cover extent mapping and the retrieval of the snow surface temperature from MODIS remote sensing approaches. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface (ground surface) temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler, snow density by the snow shovel, the snow grain size by the handheld microscope and the snow surface temperature synchronizing with MODIS. (2) Snow albedo by the total radiometer in BG-A from 11:10-13:24 on Mar. 14, 2008. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with MODIS in BG-A and BG-I. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".
LI Xin
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