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.
2020-04-07
I. overview The data set includes wind and sand activity data of Ulanbuh Desert and Kubuqi Desert along the upper Yellow River from April to May 2011 and April 2012, mainly including wind speed profile, surface roughness, wind-sand flow structure, sand transport rate data under different vegetation coverage and different parts of sand dunes. II. Data Processing Instructions The wind speed and direction are observed by 014A wind speed sensor 024A wind direction sensor and CR200 data acquisition instrument produced by MetOne company, and the sediment transport amount is observed by stepped sediment collection instrument. III. Description of Data Content The data are stored in EXCEL table, mainly including wind speed profile, surface roughness, wind-sand flow structure and sand transport rate data under different vegetation coverage. IV. Data Usage Instructions This paper evaluates the sandstorm hazards along the Yellow River, estimates the amount of sandstorm entering the Yellow River in the upper reaches of the Yellow River, and provides data support for the establishment of an early warning system for sandstorm hazards in the region.
2020-03-29
The data set contains data of three stations in the middle reaches: (1) the eddy related flux observation data of point 4 in the flux observation matrix from May 31 to September 17, 2012. The station is located in the Yingke irrigation area of Zhangye City, Gansu Province, and the underlying surface is the village. The longitude and latitude of the observation point are 100.35753e, 38.87752n and 1561.87m above sea level. The height of the eddy correlator is 4.2m (after August 19, the height of the eddy correlator is adjusted to 6.2m), the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 17cm. (2) Eddy related flux data of point 12 in the flux observation matrix from May 28 to September 21, 2012. The site is located in the farmland of Daman irrigation area, Zhangye City, Gansu Province, with corn as the underlying surface. The longitude and latitude of the observation point are 100.36631e, 38.86515n and 1559.25m above sea level. The height of the eddy correlator is 3.5m, the sampling frequency is 10Hz, the ultrasonic direction is north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 15cm. (3) Eddy related flux data of point 14 in the flux observation matrix from May 30 to September 21, 2012. The site is located in the farmland of Yingke Irrigation District, Zhangye City, Gansu Province, with corn as the underlying surface. The longitude and latitude of the observation point are 100.35310e, 38.85867n and 1570.23m above sea level. The height of the eddy correlator is 4.6m, the sampling frequency is 10Hz, the ultrasonic direction is north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 15cm. The original observation data of the eddy correlator is 10Hz. The published data is the 30 minute data processed by the edire software. The main processing steps include: outliers elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. At the same time, the quality evaluation of each flux value is mainly the test of atmospheric stability (Δ st) and turbulence similarity (ITC). The 30min flux value output by edire software was also screened: (1) data in case of instrument error; (2) data in 1H before and after precipitation; (3) data with loss rate greater than 3% in every 30min of 10Hz original data; (4) observation data with weak turbulence at night (U * less than 0.1M / s). The average period of observation data is 30 minutes, 48 data in a day, and the missing data is marked as - 6999. Suspicious data caused by instrument drift and other reasons shall be identified with red font. The published observation data include: date / time, wind direction WDIR (?), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD uuy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), stability Z / L (dimensionless), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), two Carbon dioxide flux FC (mg / (M2S)), quality mark of sensible heat flux QA ﹤ HS, quality mark of latent heat flux QA ﹐ Le, quality mark of carbon dioxide flux QA ﹐ FC. The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into three levels (quality identification 0: (Δ st < 30, ITC < 30); 1: (Δ st < 100, ITC < 100); the rest is 2). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; data is stored in *. XLS format. For station information, please refer to Liu et al. (2015), and for observation data processing, please refer to Liu et al. (2011) and Xu et al. (2013).
2020-03-28
This data is the NCEP/DOE reanalysis data of 6h interval nested downscaling by WRF model in northwest China to a horizontal resolution of 12km, 364 grid points in the east-west direction, 251 grid points in the north-south direction and 31 layers in the vertical direction. The simulation time starts from 1979-01-01,06:00:00 and ends at 2013-12-31,23:00:00. The parameterization schemes of the model are as follows: Kain Frisch cumulus convection scheme, WSM3 cloud microphysics scheme, RRTM long wave scheme, Dudhia short wave scheme, Noah land surface model, YSU planetary boundary layer scheme. The file naming rules in the data set are: wrf_t2_YYYY.nc and wrf_rain_YYYY.nc, where YYYY is the annual abbreviation, t2 is the 2m temperature (unit ℃), and rain is the total surface precipitation (unit mm).
2020-03-28
Based on the data information provided by the data management center of Heihe project, the daily humidity data of 21 regular meteorological observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River were collected and 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 humidity 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 humidity value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) The residual after removing the trend was fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average humidity 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 humidity for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
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.
2020-03-28
Based on the data 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 sunshine hours are statistically sorted out and the monthly sunshine hours data of 1961-2010 for many years are calculated. 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 sunshine hours distribution trend is obtained; if the variation coefficient is less than or equal to 100%, the ordinary least square regression is used to calculate the sunshine hours and the geographical terrain factors (longitude, latitude, elevation, slope, aspect, etc.) of the station ）The distribution trend of sunshine hours per month is obtained, and the residuals after removing the trend are fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average sunshine hours 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 sunshine hours for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
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.
2020-03-28
The data are from 2011 to 2012. A 30m×30m Picea crassifolia canopy interception sample plot was set up in the Picea crassifolia sample plot at an altitude of 2800m m. A siphon raingauge model DSJ2 (Tianjin Meteorological Instrument Factory) was set up on the open land of the river about 50m from the sample plot to observe the rainfall outside the forest and its characteristics. Penetrating rain in the forest adopts a combination of manual observation and automatic observation. Automatic observation is mainly realized through a penetrating rain collection system arranged in the interception sample plot, which consists of a water collecting tank and an automatic recorder. Two 400cm×20cm water collecting tanks are connected with DSJ2 siphon rain gauge, and the change characteristics of penetrating rain under the forest are continuously recorded by an automatic recorder. Due to the spatial variability of the canopy structure of Picea crassifolia forest in the sample plot, a standard rainfall tube for manual observation is also arranged in the sample plot to observe the penetrating rain in the forest. Ninety rainfall tubes with a diameter of 20cm are arranged in the sample plot at intervals of 3m. After each precipitation event ends and the penetrating rain in the forest stops, the amount of water in the rain barrel will be emptied and the penetrating rain in the barrel will be measured with the rain cup.
2020-03-14
This data is the water level data of 2011-2012, which is observed by water level recorder. From July 14 to September 9, 2011, the observation was recordered every five minutes; from June 4 to July 10, 2012, the observation was recordered every ten minutes. The data content is the temperature and atmospheric pressure inside the hole, and the data is the daily scale data. The data shall be opened with HOBO software.
2020-03-14
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