This data is the runoff and evapotranspiration generated by the precipitation in the growing season of the upper reaches of Heihe River from 1992 to 2015. Temporal resolution: year (growingseason), spatial resolution: 0.00833°. The data include precipitation (mm), evapotranspiration (mm), runoff (mm) and soil water content (m3 / m3). The data are obtained by using meteorological, soil and vegetation parameters based on Eagleson eco hydrological model. The simulated rainfall runoff is verified by using the observed runoff data in the growing season of 6 sub basins in the upper reaches of Heihe River (Heihe main stream, Babao River, yeniugou, Liyuan River, Wafangcheng and Hongshui River). The variation range of correlation coefficient (R) is 0.53-0.74, RMSE is 32.46-233.18 mm, and the relative error range is -0.66-0.0005; The difference between simulated evapotranspiration and gleam et is − 115.36 mm to 44.1 mm. The simulation results can provide some reference for hydrological simulation in the upper reaches of Heihe River.
ZHANG Baoqing
This dataset contains 10 years (2010-2019) global daily surface soil moisture . The resolution is 36 km , the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017，2021). This study transfers the merits of SMAP to FY-3B/MWRI through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with FY-3B/MWRI brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content，which is comparable with that of SMAP. (evaluation accuracy of 14 dense ground network globally.)
YAO Panpan, LU Hui, ZHAO Tianjie, WU Shengli , SHI Jiancheng
A long-term (1980-2017) land evaporation (E) product with a spatial resolution of 0.25 degree. This is a merged product from three model-based E products using the Reliability Ensemble Averaging (REA) method which minimizes errors. These include the fifth-generation ECMWF Re-Analysis (ERA5), the second Modern-Era Retrospective analysis for Research and Applications (MERRA2), and the Global Land Data Assimilation System (GLDAS). To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain daily and monthly mean data (e.g., REA_1980_day.nc and REA_1980_mon.nc). The dataset is stored in NetCDF format, containing the variable E, representing land evaporation, produced in millimeters (mm) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.875E to 179.875E, the latitude from -59.875N to 89.875N. Complete time coverage is from January 1, 1980, to December 31, 2017.
LU Jiao, WANG Guojie, CHEN Tiexi, LI Shijie, HAGAN Daniel, KATTEL Giri, PENG Jian, JIANG Tong, SU Buda
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the future 50 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 2020-2070 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data, and the meteorological forcings are obtained from the ensemble mean of 38 CMIP6 models under SSP2-4.5 scenario. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the past 40 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 1980-2019 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
This dataset is an 8-year (2011-2018) global spatiotemporally consistent surface soil moisture dataset with a 25km spatial grid resolution and daily temporal step in unit of cm3/cm3. This dataset is developed by applying a linear weight fusion algorithm based on the Triple Collocation Analysis (TCA) to merge the five soil moisture data products, i.e., SMOS, ASCAT, FY3B, CCI and SMAP in two steps. The first step is to fuse the SMOS, ASCAT and FY3B soil moisture products from 2011 to 2018. The second step is to refuse the merged soil moisture product in the first step, CCI and SMAP products from 2015 to 2018, and to obtain the finally merged soil moisture product from 2011 to 2018. In addition, the measured soil moisture data from seven ground observation networks around the world are used to evaluate and analyze the merged soil moisture product. The fused soil moisture product has the global spatial coverage ratio of more than 80%. With rhe minimum RMSE (root mean square error) of 0.036 cm3/cm3.
JIA Li , XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, HU Guangcheng
1. Glacial lake data sets (1960s−2020) This data set contains glacial lake data for the 1960s, 2016, 2017, 2018, 2019, and 2020, mapped from Korona KH-4, Sentinel-2, and Sentinel-1 imagery. 2. Potential Outburst Flood Hazard level of Bhutanese glacial lakes This data contains the Potential Outburst Flood Hazard level of Bhutanese glacial lakes with an area greater than 0.05 km2 (n=278). The value for each hazard assessment criteria is also provided in the data attributes.
RINZIN Sonam, ZHANG Guoqing
The data include four types: water levels of 244 lakes extracted in CryoSat-2 L1B Baseline D (2010-2020); water levels of 356 lakes extracted in ICESat-2 ATL13 (2018-2020); water levels of 125 lakes extracted in Sentinel-3A SRAL L2 (2016- 2020); water levels in 120 lakes extracted from Sentinel-3B SRAL L2 (2018-2020). Data include date, decimal date, water level, standard deviation, and geographic location of each lake. Please see the paper for detailed data processing procedures.
XU Fenglin, ZHANG Guoqing
This data set is the version 2 of "High temporal and spatial resolution precipitation data of Upper Brahmaputra River Basin (1981-2016) ", with additional data from 2017 to 2019. This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2019, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei, WANG Lei, LI Xiuping, ZHOU Jing
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
1) Data content: daily water level change data of Nam Co in 2019. The coordinates of observation points are 90.96 ° E, 30.77 ° N, 4730m above sea level, and the underlying surface is alpine grassland. (2) Data source and processing method: measure by manually reading the water level gauge. The original observation data shall be processed and quality controlled by a specially assigned person according to the observation records. (3) Data quality description: because the data is obtained by manual reading of water gauge, it is greatly affected by the harsh environment, and the data is missing and discontinuous in some periods. (4) Data application prospect: the data can be applied to scientific research fields such as Lake hydrology and hydrological process in high and cold areas.
WANG Junbo
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station in the Hai River Basin from January 1 to December 31 in 2020. There were two types of LASs at Huailai Station: BLS450 and zzlas, produced by Germany and China, respectively. The north tower was set up with the zzlas receiver and the BLS450 transmitter, and the south tower was equipped with the zzlas transmitter and the BLS450 receiver. The site (north: 115.8023E，40.3596N; south: 115.7825E，40.3522N) was located in Donghuayuan Town, Huailai County, Hebei Province. The underlying surfaces between the two towers were corn. The elevation is 480 m. The effective height of the LASs was 14 m, and the path length was 1870m. The data were sampled 1 minute. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion. (2) The data were rejected when the demodulation signal was small. (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). Several instructions were included with the released data. (1) The data were primarily obtained from BLS450 measurements, and missing flux measurements from the BLS450 instrument were substituted with measurements from the zzlas instrument. The missing data were denoted by -6999. (2) The dataset contained the following variables: Date/Time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for observation experiment or sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset includes the observation data from 01 Jan. 2020 through 31 Dec. 2020, collected by lysimeters, which are located at 115.788E, 40.349N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost and overrange data were replaced by -6999; 2) deleting the abnormal data due to maintenances; 3) keeping the consistent date and time format (e.g. 2020-01-01 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xlsx.
LIU Shaomin, ZHU Zhongli, XU Ziwei
This dataset contains the flux measurements from the scintillometer at Arou Superstation in the Heihe integrated observatory network. The north tower was set up with the receiver, and the south tower was equipped with transmitter. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The raw data acquired at 1 min intervals for the near infrared scintillometer and 200 Hz for the optical µwave scintillometer were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat and latent heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) raw data processing and calculating the intensity variance. (2) Calculating the structural parameters of air refractive index. (3) Calculating the meteorological structural parameters. (4) Calculating the sensible and latent heat flux。 The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index for near infrared scintillometer (Cn2, m-2/3), intensity variance for LAS, MWS and OMS (Var_LAS, Var_MWS, Var_OMS), the sensible heat flux (H, W/m^2), and the latent heat flux (LE, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, SHI Shengjin, XU Ziwei
The data set contains the plant liquid flow meter data (101.1346 ° e, 41.9900 ° n) of the mixed forest station in the surface process comprehensive observation network of Heihe River Basin from October 20, 2019 to December 7, 2020. The study area is located in the Populus euphratica forest in Ejina Banner, Alashan League, inner Mongolia Autonomous Region, at an altitude of 874m. According to different heights and DBH of Populus euphratica forest, sample trees are selected to install the developed plant liquid flow instrument. Each sample tree is installed with two groups, with a height of 1.3m. The original observation data of the plant liquidometer is the temperature difference between the probes, and the time is 10 minutes. The published data is the temperature difference data delta every 10 minutes_ T (℃), liquid flow rate V (cm / h) and daily transpiration t (mm / D). Firstly, the liquid flow rate and liquid flow volume are calculated according to the temperature difference between the probes, and then the transpiration t is calculated according to the Populus euphratica forest area and tree spacing at the observation point. At the same time, the post-processing of the calculated rate and flux values: (1) eliminate the data obviously beyond the physical meaning or beyond the instrument range; (2) Missing data are marked with - 6999; (3) Suspicious data caused by probe failure and other reasons shall be identified in red font, and the data confirmed to be problematic shall be eliminated. Please refer to Liu et al. (2018) for site information and Qiao et al. (2015) for observation data processing.
LIU Shaomin, SHI Shengjin, XU Ziwei
This dataset includes data recorded by the Heihe integrated observatory network obtained from a mesoscale soil moisture measurement system of soil moisture of Daman Superstation from January 1 to December 31, 2020. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, C), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
QIAO Yunfeng, LIU Shaomin, XU Ziwei
This dataset contains the soil moisture data from August 31, 2020 to December 22, 2020 (except some data interruption events in short durations due to rainfall and field irrigation). This instrument is installed at the Yucheng Comprehensive Experimental Station, CAS (Yucheng, Shandong，116°22' E，36°40'N，23m above sea level) in the field of winter wheat and summer maize during the monitoring period. The instrument was place directly on the smooth ground so that it’s close contact with soil surface. The data sampling interval is once an hour which can be remotely set through the app installed in a mobile phone or manually monitor any time in situ. The instrument can collect the frequency values by three groups of electrodes with different specifications passing through the soil profile. The data processing procedure is as follow: the different frequency values collected by this instrument will be uploaded to the cloud platform through the Internet of Things；the system removes abnormal values from database；averages and normalizes the remaining data, and then calculates and outputs soil volumetric moisture at different soil layers by a built-in calibration function.
QIAO Yunfeng, LIU Shaomin
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2020. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 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_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_10 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 IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs) (W/m^2), soil temperature (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) (℃), 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) (%, volumetric water content). 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. 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: 2020-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 14 to December 31 in 2020. The site (115.7880° E, 40.3491°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 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 0.15 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 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. 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 10% 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. 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 Guo et al. (2015) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2020. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 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/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), 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) (℃), 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) (%, volumetric water content), and average soil temperature (TCAV, ℃). 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. 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: 2020-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
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