The dataset of CMA operational meteorological stations observations in the Heihe river basin were provided by Gansu Meteorological Administration and Qinghai Meteorological Administration. It included: (1) Diurnal precipitation, sunshine, evaporation, the wind speed, the air temperature and air humidity (2, 8, 14 and 20 o'clock) in Mazongshan, Yumen touwnship, Dingxin, Jinta, Jiuquan, Gaotai, Linze, Sunan, Zhangye, Mingle, Shandan and Yongchang in Gansu province (2) the wind direction and speed, the temperature and the dew-point spread (8 and 20 o'clock; 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 and 50hpa) in Jiuquan, Zhangye and Mingqin in Gansu province and Golmud, Doulan and Xining in Qinghai province (3) the surface temperature, the dew point, the air pressure, the voltage transformation (3 hours and 24 hours), the weather phenomena (the present and the past), variable temperatures, visibility, cloudage, the wind direction and speed, precipitation within six hours and unusual weather in Jiuquan, Sunan, Jinta, Dingxin, Mingle, Zhangye, Gaotai, Shandan, Linze, Yongchang and Mingqin in Gansu province and Tuole, Yeniugao, Qilian, Menyuan, Xining, Gangcha and Huangyuan in Qinhai province.
1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.
The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.
Data source: China l Meteorological Administration Network; Data Content: Daily Rainfall Data Series of Heihe River Basin from 1990 to 2004; Evaporation Data of Heihe River Basin from 2000 to 2012. Data Spatial Range: Rainfall Data (Yingluoxia, Shandan, Gaoya, Pingchuan, Ganzhou Pingshan Lake, Zhengyixia Gorge, Liyuan River); Evaporation Data (Zhangye, Gaotai, Dingxin, Jiuquan, Jinta, Shandan, Ejina, Hequ)
This dataset contains the flux measurements from the Guazhou station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from September 24 to December 31 in 2018. The site (95.673E, 41.405N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2016 m. The EC was installed at a height of 4.0 m, and 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 (CSAT3&Li7500A) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature 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. 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): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 collected 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, and the missing data were replaced with -6999. 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/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), 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 *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2011) for data processing) in the Citation section.
Surface evapotranspiration (ET) is an important variable that connects the land energy balance, water cycle and carbon cycle. The accurate acquisition of ET is helpful to the research of global climate change, crop yield estimation, drought monitoring, and it is of great significance to regional and global water resource planning and management. The methods of obtaining evapotranspiration mainly include ground observation, remote sensing estimation, model simulation and assimilation. The high-precision surface evapotranspiration data can be obtained by ground observation, but the spatial representation of observation stations is very limited; remote sensing estimation, model simulation and assimilation methods can obtain the spatial continuous surface evapotranspiration, but there are problems in the verification of accuracy and the rationality of spatial-temporal distribution pattern. Therefore, this study makes full use of a large number of high-precision station observation data, combined with multi-source remote sensing information, to expand the observation scale of ground stations to the region, to obtain high-precision, spatiotemporal distribution of continuous surface evapotranspiration. Based on the "Heihe River Integrated Remote Sensing joint experiment" (water), "Heihe River Basin Ecological hydrological process integrated remote sensing observation joint experiment" (hiwater), the accumulated station observation data (automatic meteorological station, eddy correlator, large aperture scintillation instrument, etc.), 36 stations (65 station years, distribution map is shown in Figure 1) are selected in combination with multi-source remote sensing data (land cover) Five machine learning methods (regression tree, random forest, artificial neural network, support vector machine, depth belief network) were used to construct different scale expansion models of surface evapotranspiration, and the results showed that: compared with The other four methods, random forest method, are more suitable for the study of the scale expansion of surface evapotranspiration from station to region in Heihe River Basin. Based on the selected random forest scale expansion model, taking remote sensing and air driven data as input, the surface evapotranspiration time-space distribution map (etmap) of Heihe River Basin during the growth season (May to September) from 2012 to 2016 was produced. The results show that the overall accuracy of etmap is good. The RMSE (MAPE) of upstream (las1), midstream (las2-las5) and downstream (las6-las8) are 0.65 mm / day (18.86%), 0.99 mm / day (19.13%) and 0.91 mm / day (22.82%), respectively. In a word, etmap is a high-precision evapotranspiration product in Heihe River Basin, which is based on the observation data of stations and the scale expansion of random forest algorithm. Please refer to Xu et al. (2018) for all station information and scale expansion methods, and Liu et al. (2018) for observation data processing.
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.
1. Data overview: Eddy covariance system is a micrometeorological measurement method.It USES the principle of vorticity correlation to measure the material exchange and energy exchange of the atmosphere cushion surface with a fast response sensor.The core of open circuit eddy covariance system is composed of CR1000 data collector, CSAT3 3d ultrasonic wind speed and direction sensor, and li-7500 open circuit CO2/H2O gas analyzer (EC150).The eddy covariance system is a newly purchased instrument of this project, which takes a long time to order. It was installed in early October 2011, and the data is relatively short.This data set is the vorticity covariance data of qilian station from October 1, 2011 to December 31, 2011 at 30min. 2. Data content: The observation items are: horizontal wind speed Ux (m/s), horizontal wind speed Uy (m/s), vertical wind speed Uz (m/s), ultrasonic temperature Ts (Celsius), co2 concentration (mg/m^3), water vapor concentration (g/m^3), pressure press (KPa).The data sampling rate is 10Hz per second. 3. Space and time range: Geographical coordinates: longitude: 99° 52’e;Latitude: 38°15 'N;Height: 3232.3 m
1. Data overview: This data set is eddy covariance Flux data of qilian station from January 1, 2013 to December 31, 2013. 2. Data content: The observation items are: horizontal wind speed Ux (m/s), horizontal wind speed Uy (m/s), vertical wind speed Uz (m/s), ultrasonic temperature Ts (Celsius), co2 concentration co2 (mg/m^3), water vapor concentration h2o (g/m^3), pressure press (KPa), etc.The data is 30min Flux data. 3. Space and time range: Geographical coordinates: longitude: 99° 52’e;Latitude: 38°15 'N;Height: 3232.3 m
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 email@example.com
LinksNational Tibetan Plateau Data Center