MODIS daily cloud-free snow cover area product for Sanjiangyuan from 2000 to 2018

The dataset was produced based on MODIS data. Parameters and algorithm were revised to be suitable for the land cover type in the Three-River-Source Regions. By using the Markov de-cloud algorithm, SSM/I snow water equivalent data was fused to the result. Finally, high accuracy daily de-cloud snow cover data was produced. The data value is 0(no snow) or 1(snow). The spatial resolution is 500m, the time period is from 2000-2-24 to 2018-12-31. Data format is geotiff, Arcmap or python+GDAL were recommended to open and process the data.

0 2020-10-13

Snow depth product for Sanjiangyuan from 1980 to 2018

This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2018. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.

0 2020-10-13

MODIS Snow cover product dataset in China (2000-2004)

The data set is from February 24, 2000 to December 31, 2004, with a resolution of 0.05 degrees, MODIS data, and the data format is .hdf. It can be opened with HDFView. The data quality is good. The missing dates are as follows: 2000 1 -54 132 219-230 303 2001 111 167-182 2002 079-086 099 105 2003 123 324 351-358 2004 219 349 The number after the year is the nth day of the year Pixel values ​​are as follows: 0: Snow-free land 1-100: Percent snow in cell 111: Night 252: Antarctica 253: Data not mapped 254: Open water (ocean) 255: Fill An example of file naming is as follows: Example: "MOD10C1.A2003121.004.2003142152431.hdf" Where: MOD = MODIS / Terra 2003 = Year of data acquisition 121 = Julian date of data acquisition (day 121) 004 = Version of data type (Version 4) 2003 = Year of production (2003) 142 = Julian date of production (day 142) 152431 = Hour / minute / second of production in GMT (15:24:31) The corner coordinates are: Corner Coordinates: Upper Left (70.0000000, 54.0000000) Lower Left (70.0000000, 3.0000000) Upper Right (138.0000000, 54.0000000) Lower Right (138.0000000, 3.0000000) Among them, Upper Left is the upper left corner, Lower Left is the lower left corner, Upper Right is the upper right corner, and Lower Right is the lower right corner. The number of data rows and columns is 1360, 1020 Geographical latitude and longitude coordinates, the specific information is as follows: Coordinate System is: GEOGCS ["Unknown datum based upon the Clarke 1866 ellipsoid",     DATUM ["Not specified (based on Clarke 1866 spheroid)",         SPHEROID ["Clarke 1866", 6378206.4,294.9786982139006,             AUTHORITY ["EPSG", "7008"]]],     PRIMEM ["Greenwich", 0],     UNIT ["degree", 0.0174532925199433]] Origin = (70.000000000000000, 54.000000000000000)

0 2020-10-12

Absorptive impurity data of snow and ice in Altay (2016-2017) v1.0

Soluble organic carbon (DOC) in snow and ice can effectively absorb the solar radiation in the ultraviolet and near ultraviolet band, which is also one of the important factors leading to the enhancement of snow and ice ablation. Through the continuous snow samples from November 2016 to April 2017 in Altay area, the data of DOC, TN and BC of snow in kuwei station in Altay area were obtained through the experimental analysis and test with the instrument. The time resolution was weeks and the ablation period was daily. 1. Unit: Doc and TN unit μ g-1 (PPM), BC unit ng g-1 (ppb), MAC unit M2 g-1

0 2020-10-01

Cloud-free Fractional Snow Cover from Blended MODIS and FY-2 VISSR

The continuous snow cover area in time and space is one of key elements to study of land surface energy and water exhange, mountain hydrology, land surface model, numerical weather forecast and climate change. However, the large number of clouds causes data gaps in the snow cover area from optical remote sensing. The MODIS observations of Terra and aqua, FY-2E and FY-2F VISSR are used to obtain fractional snow cover (subpixel snow cover) which is less affected by the cloud, and the snow cover of the remaining cloud pixels is supplemented according to the time series information. Finally the cloudless daily snow fraction is obtained. This data set includes the daily fractional snow cover at 5 km spatial resolution in the Tibetan Plateau and China.

0 2020-10-01

The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)

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.

0 2020-10-01

Data set of spatial and temporal distribution of water resources in Yarlung Zangbo River from 1998 to 2016

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).

0 2020-08-27

HiWATER: WATERNET observation dataset in the upper of Heihe River Basin (2015)

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"

0 2020-08-18

Long-term series of daily global snow depth (1979-2017)

The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1979~2017, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1

0 2020-08-03

Meteorological, albedo and evapotranspiration data set of hulugou shrub experimental area in the upper reaches of Heihe River (2012-2014)

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.

0 2020-08-01