The monthly precipitation data set of China's alpine mountains includes the qilian mountains (1960-2013), tianshan mountains (1954-2013) and Yangtze river source (1957-2014). The distributed hydrological model needs high-precision spatial distribution information of precipitation as input.Because of the scarcity of stations, the precipitation interpolation at stations cannot reflect the spatial distribution of precipitation in the alpine mountainous areas.Generation method of this dataset: (1) collect precipitation data of national meteorological stations and hydrological stations in various regions, and add precipitation observation data of field stations of Chinese academy of sciences above an altitude of 4000m; (2) use the temperature data of each station to correct the collected precipitation data of different precipitation types; (3) establish the relationship between precipitation data and altitude, longitude and latitude, and fit monthly to generate monthly precipitation data set of 1km scale. The interpolation year of this data is 1954-2014. The data projection method is Albers projection. The spatial interpolation precision is 1-km, and the time precision is monthly data.The results show that the interpolation precipitation is reliable. The data is stored in ASCII files. The file names of the monthly precipitation data files of tianshan mountain and Yangtze river source are in the form yyyymm.txt. YYYY is the year and MM is the month.The monthly precipitation data of qilian mountain is named as: month_10001.txt, this file is the precipitation data of January 1960, successively month_10002.txt is the precipitation of February 1960, and month_10013.txt is the precipitation data of January 1961,......Month_10648.txt represents the precipitation data for December 2013.Each ASCII file represents the grid precipitation data of the day in mm.
CHEN Rensheng, LIU Junfeng
The No. 8 hydrological section is located at Gaotai Heihe River Bridge (39 ° 23′22 .93 ″ N, 99 ° 49′37 .29″ E, 1347 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.8 hydrological section from 17 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.
YUE Tianxiang, ZHAO Na
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
Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).
YUE Tianxiang, ZHAO Na
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope direction data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
The routine meteorological observation data set of four times a day provided by the data management center of Heihe plan is adopted, including 13 stations. The daily evaporation was statistically sorted out, and the monthly evaporation data of 2000-2009 years was calculated. The spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly evaporation distribution trend is obtained; if the coefficient of variation is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station evaporation value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) After the trend is removed, the residuals are fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average evaporation distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average evaporation in 2000-2009. Spatial resolution: 500M.
YUE Tianxiang, ZHAO Na
The station data information of 21 regular meteorological observation stations in Heihe River Basin and surrounding areas and 13 national benchmark stations around Heihe River provided by Heihe plan data management center are used to make statistics and collation of daily wind speed and calculate the monthly wind speed data of 1961-2010 for many years. The spatial stability analysis is carried out to calculate the variation coefficient. If the variation coefficient is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly wind speed distribution trend is obtained; if the variation coefficient is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station wind speed value and the geographical terrain factors (longitude and latitude, elevation, slope, aspect, etc.) The trend of monthly wind speed distribution is obtained, and the residual after removing the trend is fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average wind speed distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average wind speed for many years from 1961 to 2010. Spatial resolution: 500M.
YUE Tianxiang, ZHAO Na
Adopt aster with 30 meter resolution provided by Heihe project data management center GDEM data and 90 meter resolution SRTM data are two sets of grid data, as well as multi-source point data. These point data include radar point cloud elevation data in the middle and upper reaches; elevation data extracted from soil sample points and vegetation sample in the data management center of Heihe plan; elevation data extracted from climate and hydrological stations; and elevation sample data measured by the research group. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high-precision DEM data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final DEM surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 5-day Lai synthesis results in 2015 are provided by the 1 km / 5-day Lai data set of Heihe River Basin. The data set is constructed by using the data of Terra / MODIS, Aqua / MODIS, as well as the domestic satellites fy3a / MERSI and fy3b / MERSI to construct the multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results in 2015. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
Regional climate mode RegCM4.3 parameter settings are as follows: horizontal resolution is 40 km, east and west 112 divisions Point, 84 grid points in north-south direction, grid center is 74.21 ° E, 44.76 ° N, projection is Lambert projection, vertical stratification It is divided into 23 layers, and the top pressure is 50 hPa; the initial boundary field uses ERA40 with a horizontal resolution of 2.5 °. NCEP / NCAR then analyzes the data, the time series takes the pattern data and the site interpolation data is the common part of the time series. SST data selection GISST (1948-2002), the planet boundary layer in the model is selected as Holtslag format, cumulus convection The scheme is Emanuel MIT format, the side boundary conditions are exponential relaxation conditions, and the land surface process is described as BATs. In mode Terrain data selection USGS GTOPO 30 30 ″ DEM digital elevation model, surface vegetation data selection USGS points GLCC (global land cover characterization) with a resolution of 30 ″. The data set is daily data, including surface pressure (hpa), 2m maximum temperature (K), 2m minimum temperature (K), 2m average temperature (K), and average surface precipitation (kg · m-2 · s-1) Naming rules, xx_xxxx_YYYY.nc. Where YYYY is the year, xx represents Central Asia, and xxxx is the drive data name (ERA40, NCEP1 respectively).
BAI Lei, LI Lanhai, CHEN Xi, YIN Gang
The Chinese regional surface meteorological element data set is a set of near-surface meteorological and environmental element reanalysis data set developed by the Qinghai-Tibet Plateau Research Institute of the Chinese Academy of Sciences. The data set is based on the existing Princeton reanalysis data, GLDAS data, GEWEX-SRB radiation data and TRMM precipitation data in the world, and is made by combining the conventional meteorological observation data of China Meteorological Administration. The temporal resolution is 3 hours and the horizontal spatial resolution is 0.1, including 7 factors (variables) including near-surface air temperature, near-surface air pressure, near-surface air specific humidity, near-surface full wind speed, ground downward short wave radiation, ground downward long wave radiation and ground precipitation rate. The physical meaning of each variable: | Meteorological Element || Variable Name || Unit || Physical Meaning | near-surface temperature ||temp|| K || instantaneous near-surface (2m) temperature | surface pressure || pres|| Pa || instantaneous surface pressure | specific humidity of near-surface air || shum || kg/ kg || instantaneous specific humidity of near-surface air | near ground full wind speed || wind || m /s || instantaneous near ground (anemometer height) full wind speed | downward short wave radiation || srad || W/m2 || 3-hour average (-1.5 HR ~+1.5 HR) downward short wave radiation | Downward Long Wave Radiation ||lrad ||W/m2 ||3-hour Average (-1.5 hr ~+1.5 hr) Downward Long Wave Radiation | precipitation rate ||prec||mm/hr ||3-hour average (-3.0 HR ~ 0.0 HR) precipitation rate For more information, please refer to the "User's Guide for China Meteorological Al Forcing Dataset" published with the data. The main changes in the latest version (01.06.0014) are: 1. Extend the data to December 2015 (except for short-wave and long-wave data, only until October 2015; the data from November to December 2015 are interpolated based on GLDAS data, and the error may be too large); 2. Set the minimum wind speed at 0.05 m/s; 3. Fixed a bug in the previous radiation algorithm to make our short wave and long wave data more reasonable in the morning and evening periods. 4. bug of precipitation data has been corrected, and the period involved in the change is 2011-2015.
YANG Kun, HE Jie
This dataset provides the estimated results of land cover change (IGBP classification) in 2040, 2070 and 2100 of Heihe River under the latest cmip5 based greenhouse gas emission scenario RCPs (representative concentration pathways). Spatial resolution: 1km. Time period: RCP (2.6, 4.5, 8.5) three scenarios, each scenario corresponding to three time periods: t1:2040, t2:2070, t3:2100. File naming rules: take "HLCs rcp26_" as an example to explain: in the naming, "HLCs" refers to the land cover scenario of Heihe River Basin, rcp26 refers to the rcp2.6 scenario of cmip5, "_40" refers to the future scenario period of 2040, the complete file name means the land cover prediction data of Heihe River Basin in 2040 under the rcp26 scenario, and so on.
FAN Zemeng, YUE Tianxiang
Based on the data information of 21 regular meteorological observation stations in Heihe River Basin and its surrounding areas and 13 national benchmark stations around Heihe River provided by the data management center of Heihe plan, the daily air temperature is statistically sorted out, and the monthly air temperature data of 1961-2010 for many years is calculated, and the spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, then Calculate the relationship between the station and geographical terrain factors by geographical weighted regression, and get the monthly temperature distribution trend; if the coefficient of variation is less than or equal to 100%, calculate the relationship between the station temperature value and geographical terrain factors (longitude, latitude, elevation) by ordinary least square regression, and get the monthly temperature distribution trend; use HASM (high accuracy surface modeling) for the residual after removing the trend Method). Finally, the monthly average temperature distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: average monthly temperature for many years from 1961 to 2010. Spatial resolution: 500M.
ZHAO Na, YUE Tianxiang
Water demand in the middle and lower reaches of Heihe River (mainly including water demand for living, livestock, industry, agriculture, tertiary industry, artificial forest and grass ecology in the middle reaches of Heihe River in current year, 2020 and 2030; water demand for living, industry, tertiary industry and ecology in Ejina Banner in the middle reaches of Heihe River in current year, 2020 and 2030)
JIANG Xiaohui
Microwave emissivity of the surface characterization of the object to launch the ability of microwave radiation, spaceborne passive microwave emissivity can on macro, large scale integral expression of epicontinental microwave radiation is a passive microwave surface parameters in quantitative inversion experience for one of the important basic data, is also on the large scale understand epicontinental microwave radiation in a way.This data set is considered to carry on the Aqua satellite advanced microwave scanning radiometer (amsr-e) and moderate resolution imaging spectroradiometer (MODIS) synchronous observation characteristics, using the MODIS land surface temperature and atmospheric water vapor data as input, by considering the effects of atmospheric emissivity estimation model, produced a global sky conditions during the running of amsr-e sensor (June 2002 ~ October 2011) of the epicontinental multichannel bipolar microwave instantaneous emission rate.Through product low-frequency radio signal, data alignment, statistic analysis, the different emissivity characteristics of surface coverage condition, frequency dependence and correlation studies conducted confirmatory analysis, the results show that the instantaneous dynamic details of emissivity is rich, standard deviation within 0.02 month daily variation, the change of time and space, frequency dependent on and related to the understanding of the natural physical process. This data set includes amsr-e global land surface daily, daily, daily, monthly and monthly products in the whole life cycle, which can be used to carry out satellite based passive microwave remote sensing simulation, land surface model, and inversion research of land surface temperature, snow cover, atmospheric precipitation/moisture/precipitation.The projection coordinates of the data adopt the standard EASE-GRID projection, and the data storage method is binary floating point lattice (the size of the matrix is 1383*586). After the data is obtained, ENVI/IDL and other software or the corresponding program code can be read in the form of binary files. All land surface emissivity data produced are named according to the following rules: RADI_AMSRE_EM # # # # _yyymmdd_EG_V. Bin For example, file name: RADI_AMSRE_EM01_20060101_EG_V# EM##: 01 means daily, 05 means 5 days, 10 means ten days, HM means half a month, MO means a month Yyyymmdd: yyyy means year, mm means month, and dd means date V##: version number, such as 0.1, 1.0, etc., the units digit is the official version RADI: institute of remote sensing and digital earth, Chinese academy of sciences AMSRE: advanced microwave scanning radiometer
QIU Yubao
This dataset includes data recorded by the Hydrometeorological observation network obtained from the automatic weather station (AWS) at the observation system of Meteorological elements gradient of Sidaoqiao barren-land station between 9 July, 2013, and 31 December, 2013. The site (101.133° E, 41.999° N) was located on a barren-land surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 878 m. The installation heights and orientations of different sensors and measured quantities were as follows: four-component radiometer (CNR4; 24 m, south), two infrared temperature sensors (SI-111; 24 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), and soil temperature profile (AV-10T; 0, -0.02 and -0.04 m). The observations included the following: four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), and soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm) (℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. Data were missing during 24 September, 2013 and 26 September, 2013 because of the malfunction of datalogger. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2013-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
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