Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.

0 2022-05-17

In-situ water quality parameters of the lakes on the Tibetan Plateau (2009-2020)

This dataset provides the in-situ lake water parameters of 124 closed lakes with a total lake area of 24,570 km2, occupying 53% of the total lake area of the TP.These in-situ water quality parameters include water temperature, salinity, pH,chlorophyll-a concentration, blue-green algae (BGA) concentration, turbidity, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), and water clarity of Secchi Depth (SD).

0 2022-04-18

Great Lakes region of Central Asia basic data set hydrology (2015)

Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in the basin. It is also used to refer to the amount of water passing through a certain section of the river in a certain period of time, i.e. runoff. Runoff data plays an important role in the study of hydrology and water resources, which affects the social and economic development of Adam land. This data is the flow of five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan), which comes from the hydrometeorological bureaus of Central Asian countries. The time scale is the average annual data of 2015. This data provides basic data for the project, which is convenient to analyze the situation of eco hydrological water resources in Central Asia, and provides data support for project data analysis.

0 2022-04-18

Monthly streamflow of Panj River in the Amu Dyrya catchment in Central Asia(1967-2017)

This data set is the monthly runoff data of nijnii hydrological station, the main stream of the upper reaches of Amu Darya River in Central Asia from 1967 to 2017. The station is located on the main stream of the border between Tajikistan and Afghanistan. The data is from Tajikistan hydrometeorological Bureau. The data are processed according to the country's hydrological observation specifications and quality control process. The data period is 1967-2017. The hydrological station is located at 37.193121 ° n, 68.590218 ° e, 328m above sea level, and the unit of runoff is m3 / s. The data can be used for scientific research and water conservancy engineering services such as water resources assessment in Central Asia mountainous areas.

0 2021-12-22

The development of devices monitoring ecosystem energy and water flux: Two-wavelength scintillometer (2018, 2021)

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 &microwave 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.

0 2021-11-15

The development of devices monitoring ecosystem energy and water flux: Sap flow gauge (2019-2020)

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.

0 2021-11-11

The development of devices monitoring ecosystem energy and water flux: Mesoscale soil moisture measurement system (2020)

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.

0 2021-11-11

The development of devices monitoring ecosystem energy and water flux: Nonintrusive portable soil moisture meter (2020)

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.

0 2021-11-11

Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2020)

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.

0 2021-11-02

Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin: Huailai station-lysimeters (2020)

This dataset includes the observation data from 01 Jan. 2020 through 31 Dec. 2020, collected by lysimeters, which are located at 115.788E, 40.349N 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.

0 2021-11-02