Soil respiration is the second most important carbon flux, which is only lower than that of photosynthesis in terrestrial ecosystems. The production and transport of CO2 and its δ13C by soil biochemical processes are the limiting factors for the magnitude and process evaluation of soil respiration. According to the characteristics of CO2 gas generation and transportation in soil biochemical process, based on stable isotope infrared spectroscopy technology, the nonlinear on-line calibration technology, multi-channel double-cycle efficient gas circulation path, efficient gas circulation path with pre-reduced gas concentration, and variable temperature technology that can simulate the freezing and thawing process were independently developed. On account of the gas exchange process in soil and air interface, vertical migration process of CO2 in soil profile and the process of soil organic matter decomposition, we develop a comprehensive observation system for measuring the isotope composition of carbon dioxide during soil biochemistry processes. The observation systems were placed in the ecologically fragile areas and measured the concentration and flux of soil CO2 and its δ13C, which effectively solved the comprehensive monitoring problem in generation, migration and release of CO2 during soil biochemical process. (1) a multichannel and dual-cycle observation system for soil CO2 and δ13C fluxes at the soil-atmosphere interface: We developed an online calibration system for the nonlinear response of CO2 and its δ13C analyzer at multiple concentrations to ensure the accuracy and accuracy of the instrument. By controlling the circuit switch, the instrument can automatically switch and collect circular observations between multiple channels. By controlling the premixing of gas in the channel while waiting to collect an observation, the instrument can decrease the observation time required for every channel and increase the efficiency and frequency of observation collection. By premixing the gas in the channel to be monitored, the instrument can eliminate the “dead gas” disturbances in the observation results and improve the accuracy of the observations. Based on the simulation flux verification system test, the simulated flux of CO2 and δ13C is better than 0.32 μmol m-2 s-1 @ 10 μmol m-2 s-1 (CO2) and 0.52‰ @ 10 μmol m-2 s-1 (CO2), which is better than the core technical index requirements of the project. The average domestication rate of the equipments is more than 80%, which has been used in the automatic monitoring of forest, grassland and farmland ecosystems, realizing the independent innovation and upgrading of ecological monitoring technology in China, and can be extended to CERN, CFERN, CNERN and similar field stations in other related departments. It is helpful to greatly improve China's R&D capability, level and international influence on ecological monitoring and assessment, effectively support China's terrestrial ecosystem carbon sequestration rate and potential assessment and certification, and provide technical support for national ecological civilization construction, carbon peak, carbon neutrality and ecological security regulation.
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from August 31 to December 24, 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 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 (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). 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_30m, 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 IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
This is the soil temperature and moisture observation data set in the runoff fields of the east bank of Selincuo Lake. It can be used in Climatology, Environmental Change, Hydrologic Process in Cold Regions and other disciplinary areas. The data is observed from August 19, 2017 to September 8, 2017. It is measured by soil temperature and moisture probe （5TE）and a piece of data is recorded every 60 minutes. The original data is precise, with the soil moisture accurate to 0.01% and the soil temperature 0.01℃. The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The data is stored as an excel file.
The data set contains the meteorological element observation data of jingyangling station in the upper reaches of heihe hydrometeorological observation network on January 1, 2015 and December 31, 2017.The site is located in pass, jingyangling mountain, qilian county, qinghai province.The longitude and latitude of the observation point are 101.1160E, 37.8384N and 3750m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plates (3 pieces) are successively buried 6cm underground, 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: percent). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
The data set contains the data of the meteorological element gradient observation system of the upper reaches of the heihe hydrological and meteorological observation network's arou super station on January 1, 2015 and December 31, 2017.Site is located in qilian county, qinghai province, arou township grass daban village, the underlying surface is alpine grassland.The longitude and latitude of the observation point are 100.4643E,38.0473N, and the altitude is 3033m.The air temperature, relative humidity and wind speed sensors are installed at 1m, 2m, 5m, 10m, 15m and 25m, respectively. There are 6 floors in total, facing due north.Wind direction sensor is mounted at 10m, facing due north;The barometer is installed at 2m;The tilting rain gauge is installed on the 40m observation tower of the super station in aru.The four-component radiometer is installed at 5m, facing due south;Two infrared thermometers are mounted at 5m, facing due south, with the probe facing down vertically;The photosynthetic effective radiometer was installed at 5m, facing south, and the probe direction was vertical upward.Part of the soil sensor is buried 2m away from the south of the tower, and the soil heat flow plate (self-calibration) (3 pieces) are all buried 6cm underground.Mean soil temperature sensor (tcavr) was buried 2cm and 4cm underground.The soil temperature probe is buried at the surface 0cm and underground 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm. There are three duplicates in the two layers of 4cm and 10cm.The soil moisture sensor was buried in the ground at 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm respectively, and there were three replications in the two layers of 4cm and 10cm. Observation items include: wind speed (WS_1m, WS_2m, WS_5m, WS_10m, WS_15m, WS_25m) (unit: m/s), wind direction (WD_10m) (unit: degrees), air temperature and humidity (Ta_1m, Ta_2m, Ta_5m, Ta_10m, Ta_15m, Ta_25m and RH_1m, RH_2m, RH_5m, RH_10m, RH_5m) (unit: Celsius, percentage), air pressure (Press) (unit:Hundred mpa), precipitation (Rain) (unit: mm), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit: c), photosynthetic active radiation (PAR) (unit: second micromoles/m2), the average soil temperature (TCAV) (unit: c), soil heat flux (Gs_1, Gs_2, Gs_3) (unit:W/m2), soil moisture (Ms_2cm, Ms_4cm_1, Ms_4cm_2, Ms_4cm_3, Ms_6cm, Ms_10cm_1, Ms_10cm_2, Ms_10cm_3, Ms_15cm, Ms_20cm, Ms_30cm, Ms_60cm, Ms_80cm, Ms_120cm, Ms_160cm, Ms_280cm, Ms_320cm) (unit:Soil temperature (Ts_0cm, Ts_2cm, Ts_4cm_1, Ts_4cm_2, Ts_4cm_3, Ts_6cm, Ts_10cm_1, Ts_10cm_2, Ts_15cm, Ts_20cm, Ts_30cm, Ts_60cm, Ts_80cm, Ts_120cm, Ts_160cm, Ts_280cm, Ts_320cm) (unit:Degrees Celsius. Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.The data of soil temperature and humidity and soil heat flux were missing between September 9, 2015 and September 19, 2015 and between September 30 and October 20, 2015 due to power supply problems.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: June 10, 2015 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
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.
This data set includes 26 bnunet nodes in the 0.5 °× 0.5 ° observation matrix around Zhangye City in the middle reaches of Heihe River from September 2013 to March 2014. The configuration of 26 nodes is the same, including 3 layers of soil temperature probe with depth of 1cm, 5cm and 10cm and 1 layer of soil moisture probe with depth of 5cm. The observation frequency is 2 hours. This data set can provide spatiotemporal continuous observation data set for remote sensing authenticity test of surface heterogeneity and ecological hydrology research. The time is UTC + 8. Please refer to "bnunet data document. Docx" for details
This data set contains the observation data of Zhangye National Climate Observatory from 2008 to 2009. The station is located in Zhangye, Gansu Province, with longitude and latitude of 100 ° 17 ′ e, 39 ° 05 ′ N and altitude of 1456m. The observation items include: atmospheric wind temperature and humidity gradient observation (2cm, 4cm, 10cm, 20m and 30m), wind direction, air pressure, photosynthesis effective radiation, precipitation, radiation four components, surface temperature, multi-layer soil temperature (5cm, 10cm, 15cm, 20cm and 40cm), soil moisture (10cm, 20cm, 50cm, 100cm and 180cm) and soil heat flux (5cm, 10cm and 15cm). Please refer to the instruction document published with the data for specific header and other information.
Soil water content is the key factor affecting the transpiration water consumption of plants in desert riparian forest. In this project, the typical plant communities in the lower reaches of Heihe River are selected, with coordinates of 42 ° 02 ′ 00.07 ″ N and 101 ° 02 ′ 59.41 ″ E. through continuous measurement of soil water data in 2010-2012, the observation instrument is environscan (Australia, ICT), with observation depth of 10, 30, 50, 80 and 140cm, and observation frequency of 0.5h Understanding the mechanism of environmental regulation of transpiration water consumption of desert riparian forest in the lower reaches of Heihe River provides basic data support.
This data set includes four soil temperature and moisture instrument observation points in the source area of the Yarlung Zangbo River Basin, that is Xietongmen County, Angren County, Saga County and Zhongba County. The observation time is from August 23 to December 10, 2017. And the observation interval is 10 minutes. There are 4 layers of depth of observation,which is 10cm, 40cm, 80cm and 120cm.The specific observation location and time range are as follows. Saga Bridge From 12:50:00 August 31, 2017 to 17:20:00 December 10, 2017 Maquan River Bridge From 19:30:00 August 30, 2017 to 13:10:00 December 10, 2017 Duoxiongzangbu From 17:20:00 August 24, 2017 to 12:20:00 December 8, 2017 Pangdaya River Bridge From 11:30:00 August 23, 2017 to 9:10:00 December 4, 2017 The soil moisture data is accurate to 3 digits after decimal point. The soil temperature data is accurate to 1 digit after decimal point. Quality control includes eliminating the data when the sensor is not fully adapted to the soil environment and system errors caused by sensor failure. The data is stored as an excel file.