The data set includes the observation data of river water level and velocity at No. 6 point in the dense observation of runoff in the middle reaches of Heihe River from January 1, 2014 to December 31, 2014. The observation point is located in Gaoya National Hydrological Station, zhaojiatunzhuang, Ganzhou District, Zhangye City, Gansu Province. The riverbed is sandy gravel with stable section. The longitude and latitude of the observation point are n39 ° 08'06.35 ", E100 ° 25'58.23", 1420 m above sea level, and 50 m wide river channel. Hobo pressure water level gauge is used for water level observation, with acquisition frequency of 60 minutes. Data description includes the following two parts: Water level observation, 60 minutes in unit (cm) in 2014; Data covers the period of January 1, 2014 solstice December 31, 2014; Flow observation, unit (m3); According to the monitoring flow of different water levels, the flow curve of water levels was obtained, and the change process of runoff was obtained by observing the process of water levels.The missing data are uniformly represented by the string -6999. For information of hydrometeorological network or station, please refer to Li et al.(2013), and for observation data processing, please refer to He et al.(2016).
Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).
Ec-earth-heihe USES the output of the global model of ec-earth as the driving field to simulate the 6-hour data of the Heihe river basin in 2006-2080 under the scenarios of 1980-2005 and RCP4.5.Spatial scope: the grid center of the simulation area is located at (40.30n, 99.50e), the horizontal resolution is 3 km, and the number of simulated grid points in the model is 161 (meridional) X 201 (zonal). Projection: LAMBERT conformal projection, two standard latitudes of 30N and 60N. Time range: from January 1, 1980 to December 31, 2010, with an interval of 6 hours. Description of file contents: monthly storage by grads without format.Except the maximum and minimum temperature as the daily scale, the other variables are all 6-hour data. MATLAB can be used to read, visible tmax_erain_xiong_heihe.m file description. Data description of heihe river basin: 1) Anemometer west wind (m/s) abbreviation usurf 2) Anemometer south wind(m/s), abbreviation vsurf 3) Anemometer temperature (deg K) abbreviation tsurf 4) maximal temperature (deg K) abbreviation tmax 5) minimal temperature (deg K) abbreviated tmin 6) Anemom specific humidity (g/kg) abbreviation qsurf 7) Accumulated precipitation (mm/hr) abbreviation precip 8) Accumulated evaporation (mm/hr) abbreviation evap 9) Accumulated sensible heat (watts/m**2/hr) abbreviation sensible 10) Accumulated net infrared radiation (watts/m * * 2 / hr) abbreviation netrad File name definition: Abbreviation-ec-earth-6hour，YTD For example, precip-ec-earth-6hour.198001，Is the data of 6-hour precipitation in January, 1980 (1) historical 6-hour data driven by the ec-earth global climate model from 1980 to 2005 (2) produce 6-hour data of heihe river basin under the scenario of RCP 4.5 for the global climate model ec-earth from 2006 to 2080
This dataset includes one scene acquired on (yy-mm-dd) 2012-07-25, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 0.6 m and 2.4 m, respectively. The data product level of this image is Level 2A. QuickBird dataset was acquired through purchase.
This dataset is the FPAR observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 19 July, 2012 (UTC+8). Measurement instruments: AccuPAR (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: For corn, to measure the incoming PAR on the canopy, transmission PAR under the canopy, reflected PAR on the canopy, reflected PAR under the canopy. For orchard and white poplar forest, to measure the incoming PAR outside of the canopy, transmission PAR under the canopy. Corresponding data: Land cover, plant height, crop rows identification
Proba (project for on board autonomy) is the smallest earth observation satellite launched by ESA in 2001. Chris (compact high resolution imaging Spectrometer) is the most important imaging spectrophotometer on the platform of proba. It has five imaging modes. With its excellent spectral spatial resolution and multi angle advantages, it can image land, ocean and inland water respectively for different research purposes. It is the only on-board sensor in the world that can obtain hyperspectral and multi angle data at the same time. It has high spatial resolution, wide spectral range, and can collect rich information in biophysics, biochemistry, etc. At present, there are 23 scenes of proba Chris data in Heihe River Basin. The coverage and acquisition time are as follows: 4 scenes in Arjun dense observation area, 2008-11-18, 2008-12-05, 2009-03-29, 2009-05-22; 1 scene in pingdukou dense observation area, 2009-07-13; 7 scenes in Binggou basin dense observation area, 2008-11-19, 2008-11-26, 2008-12-06, 2009-01-10, 2009-03-04, 2009-03-30, 2009-03-31; dayokou basin dense observation area, 2009-07-13 There are two views in the observation area, 2008-10-23, 2009-06-08; one in Linze area, 2008-06-23; one in Minle area, 2008-10-22; seven in Yingke oasis dense observation area, 2008-04-30, 2008-05-09, 2008-06-04, 2008-07-01, 2008-07-19, 2009-05-31, 2009-08-10. The product level is L1 without geometric correction. Except that there are only four angles for the images of 2009-03-29 and 2009-05-24 in the Arjun encrypted observation area, each image has five different angles. The remote sensing data set of the comprehensive remote sensing joint experiment of Heihe River, proba Chris, was obtained through the "dragon plan" project (Project No.: 5322) (see the data use statement for details).
The dataset of the automatic meteorological observations (2008-2009) was obtained at the Pailugou grassland station (E100°17'/N38°34', 2731m) in the Dayekou watershed, Zhangye city, Gansu province. The items included multilayer (1.5m and 3m) of the air temperature and air humidity, the wind speed (2.2m and 3.7m) and direction, the air pressure, precipitation, the global radiation, the net radiation, co2 (2.8m and 3.5m), the multilayer soil temperature (10cm, 20cm, 40cm, 60cm, 120cm and 160cm), soil moisture (10cm, 20cm, 40cm, 60cm, 120cm and 160cm), and soil heat flux (5cm, 10cm and 15cm). For more details, please refer to Readme file.
Data content: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA (version 06, global precipitation observation program), the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.
This dataset contains the automatic weather station (AWS) measurements from Bajitan Gobi station in the flux observation matrix from 13 May to 21 September, 2012. The site (100.30420° E, 38.91496° N) was located in a Gobi surface, which is near Zhangye city, Gansu Province. The elevation is 1562 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45AC; 5 m and 10 m, towards north), air pressure (PTB110; 2 m), rain gauge (TE525M; 10 m), wind speed (03001; 5 m and 10 m, towards north), wind direction (03001; 10 m, towards north), a four-component radiometer (CNR1; 6 m, towards south), two infrared temperature sensors (IRTC3; 6 m, vertically downward), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (ECh2o-5; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFT3; 3 duplicates, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m, RH_5 m and RH_10 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_5 m and Ws_10 m, m/s), wind direction (WD_10 m, °), 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 IR_2, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
This dataset contains the flux observation matrix measurements obtained from the automatic weather station (AWS) at the Daman superstation between 10 May and 26 September, 2012. The site (100.37223° E, 38.85551° N) was located in a cropland (maize surface) in the Daman irrigation, which is near Zhangye, Gansu Province. The elevation is 1556.06 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m), four-component radiometer (PSP&PIR; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, vertically downward), photosynthetically active radiation (LI-190SB; 12 m, towards south), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil heat flux (HFP01SC; 3 duplicates with one below the vegetation; and the other between plants, -0.06 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m, m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30 m, and WD_40 m, °), air pressure (press, hpa), precipitation (rain, mm), 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 IR_2, ℃), photosynthetically active radiation (PAR, μmol/ (s m^-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm, ℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.