Current Browsing: snow


HiWATER: WATERNET observation dataset in the upper reaches of the Heihe River Basin (2014)

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

2020-03-13

HiWATER:WATERNET observation dataset in the upper reaches of the Heihe River Basin (2013)

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.

2020-03-13

Time series data of snow area ratio in the Arctic (2000-2019)

The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.

2020-03-13

Long-term series of daily snow depth in Euroasia (1980-2016)

The “long-term series of daily snow depth in Eurasia” was produced using the passive microwave remote sensing data. The temporal range is 1980~2016, and the coverage is the Eurasia continent. The spatial resolutions is 0.25° 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 includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to data specification “Snow depth dataset of Eurasian (Version 1.0) (1980-2016).doc”

2020-03-13

The silicon dioxide content of snowmelt water and soil water in Hulugou small watershed (2013-2014)

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.

2020-03-12

Snow Cover Days

"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. The snow day map of Heihe River Basin is one of the hydrological and water resources in the atlas, with the scale of 1:2500000, the positive axis and equal volume conic projection, and the standard latitude of 25 47 n. Data source: this map shows the distribution of annual average snow days in 10 hydrological years in the whole Heihe River Basin from August 1, 2001 to July 31, 2011. The original data comes from MODIS daily snow products modisa 1 and myd10a1 provided by the National Snow and Ice Data Center (NSIDC) of the United States, as well as the long-term series snow depth data set of China provided by the scientific data center for cold and dry regions (WESTDC).

2020-03-05

Daily cloudless MODIS Snow area ratio data set of the QTP (2000-2015)

The daily cloudless MODIS Snow area ratio data set (2000-2015) of the Qinghai Tibet Plateau is based on MODIS daily snow product - mod10a1, which is obtained by using a cloud removal algorithm based on cubic spline interpolation. The data set is projected by UTM with spatial resolution of 500m, providing daily snow cover FSC results in the Tibetan Plateau. The data set is a day-to-day document, from 24 February 2000 to 31 December 2015. Each file is the result of snow area proportion on that day, the value is 0-100%, which is envi standard file, the naming rule is: yyyddd_fsc_0.5km.img, where yyyy represents the year, DDD represents Julian day (001-365 / 366). Files can be opened and viewed directly with envi or ArcMap. The original MODIS Snow data product for cloud removal comes from the mod10a1 product processed by the National Snow and Ice Data Center (NSIDC). This data set is in the format of HDF and uses the sinusional projection. The attributes of the daily cloudless MODIS Snow area ratio data set (2000-2015) on the Qinghai Tibet Plateau consist of the spatial-temporal resolution, projection information and data format of the data set. Temporal and spatial resolution: the temporal resolution is day by day, the spatial resolution is 500m, the longitude range is 72.8 ° ~ 106.3 ° e, and the latitude is 25.0 ° ~ 40.9 ° n. Projection information: UTM projection. Data format: envi standard format. File naming rules: "yyyyddd" + ". Img", where yyyy stands for year, DDD stands for Julian day (001-365 / 366), and ". Img" is the file suffix added for easy viewing in ArcMap and other software. For example, 2000055 ﹐ FSC ﹐ 0.5km.img represents the result on the 55th day of 2000. The envi file of this data set is composed of header file and body content. The header file includes row number, column number, band number, file type, data type, data record format, projection information, etc.; take 2000055 ﹣ FSC ﹣ 0.5km.img file as an example, the header file information is as follows: ENVI Description = {envi file, created [sat APR 27 18:40:03 2013]} Samples = 5760 Lines = 3300 Bands = 1 Header offset = 0 File type = envi standard Data type = 1: represents byte type Interleave = BSQ: data record format is BSQ Sensor type = unknown Byte order = 0 Map Info = {UTM, 1.500, 1.500, - 711320.359, 4526650.881, 5.0000000000e + 002, 5.0000000000e + 002, 45, north, WGS-84, units = meters} Coordinate system string = {projcs ["UTM [u zone [45N], geocs [" GCS [WGS [1984], data ["d [WGS [1984", organization ID ["WGS [1984", 6378137.0298.257223563]], prime ["Greenwich", 0.0], unit ["degree", 0.01745532925199433]]] project ["transfer [Mercator"]] parameter ["false [easting", 500000.0], parameter ["false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [false [false [easting ", 500000.0], parameter], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter [false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter ["false_northing", 0.0], parameter ["central_meridian", 87.0], parameter ["scale" _Factor ", 0.9996], parameter [" latitude ﹣ of ﹣ origin ", 0.0], unit [" meter ", 1.0]]} Wavelength units = unknown, band names = {2000055}

2020-01-16

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

2020-01-12

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.

2019-12-20

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.

2019-12-17