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.
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.
The dataset contains: I. document Dataset description file 二. Grid The Urumqi River Basin in Tianshan is divided into two sub regions: the upper reaches and the No. 1 glacier area. The data scale of the upstream area is 1:50000, and the grid size of the two kinds of precision digital elevation model is 2000 × 2000m and 100 × 100m respectively; the data scale of the source area is 1:5000, and the grid size of the digital elevation model is 5*5m. Digital elevation model of glacier in Headwater Area Digital elevation model of No.1 glacier in 1973、1980 and 1986; digital elevation model of No.2 glacier in 1963、1968、1973、1980 and 1986. 三, Map Thumbnails of various data types 四. rsimage TM, ETM,remote sensing image 五. Vector includes: (1) soil type map (Soil): geocode soil type (2) land resource evaluation map (Landeval): geocode, land type , subclass (3) land type map(Landtype): geocode, category, subclass (4) landuse map(Landuse): geocode, category, subclass (5) current situation of water resources utilization (wateruse): geocode, category, subclass (6) human activity(activity): geocode , category 2、 glaicer: No.1 glacier map (73, 80, 86 years), No.2 glacier map (62, 64, 73, 80years), including glacier, glacier boundary, contour data 3、 upstream sub area UP: (1) boundary (2) Subregional drainage system（River）(3) soil type map (Soil) (4) land resource evaluation map (Landeval) (5) land type map(Landtype) (6)landuse map(Landuse) (7) current situation of water resources utilization (wateruse) (8) human activity(activity) (9) Glacier distribution map(Glacier) Data projection: Project: reverse & Mercator False_easting: 500000.000000 False_northing: 0.000000 Central_meridian: 87.000000 Scale factor: 1.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_Krasovsky_1940
This data set comprises the plateau soil moisture and soil temperature observational data based on the Tibetan Plateau, and it is used to quantify the uncertainty of model products of coarse-resolution satellites, soil moisture and soil temperature. The observation data of soil temperature and moisture on the Tibetan Plateau (Tibet-Obs) are from in situ reference networks at four regional scales, which are the Nagqu network of cold and semiarid climate, the Maqu network of cold and humid climate, and the Ali network of cold and arid climate，and Pali network. These networks provided representative coverage of different climates and surface hydrometeorological conditions on the Tibetan Plateau. - Temporal resolution: 1hour - Spatial resolution: point measurement - Measurement accuracy: soil moisture, 0.00001; soil temperature, 0.1 °C; data set size: soil moisture and temperature measurements at nominal depths of 5, 10, 20, 40 - Unit: soil moisture, cm ^ 3 cm ^ -3; soil temperature, °C
The data set contains observation data of cosmic-ray instrument (crs) from January 1, 2017 to December 31, 2017. The site is located in the farmland of Daman Irrigation District, Zhangye, Gansu Province, and the underlying surface is cornfield. The latitude and longitude of the observation site is 100.3722E, 38.8555N, the altitude is 1556 meters. The bottom of the instrument probe is 0.5 meter from the ground, and the sampling frequency is 1 hour. The original observation items of the cosmic-ray instrument include: voltage Batt (V), temperature T (°C), relative humidity RH (%), air pressure P (hPa), fast neutron number N1C (number / hour), thermal neutron number N2C (number / hour), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s). The data was released after being processed and calculated. The data includes: Date Time, P (pressure hPa), N1C (fast neutrons one/hour), N1C_cor (pressure-corrected fast neutrons one/hour) and VWC ( soil water content %), it was processed mainly by the following steps: 1) Data Screening There are four criteria for data screening: (1) Eliminating data with a voltage less than or equal to 11.8 volts ; (2) Eliminating data with a relative humidity greater than or equal to 80%; (3) Eliminating data with a sampling time interval not within 60 ± 1 minute; (4) Eliminating data with fast neutrons that vary by more than 200 in one hour. In addition, missing data is supplemented with -6999. 2) Air Pressure Correction The original data is corrected by air pressure according to the fast neutron pressure correction formula mentioned in the instrument manual, and the corrected fast neutron number N1C_cor is obtained. 3) Instrument Calibration In the process of calculating soil moisture, it is necessary to calibrate the N0 in the calculation formula. N0 is the number of fast neutrons under the situation with low antecedent soil moisture . Usually, soil samples in the source area are used to obtain measured soil moisture (or obtained by relatively dense soil moisture wireless sensors) θm (Zreda et al. 2012) and the fast neutron correction data N in corresponding time periods, then NO can be obtained by reversing the formula. Here, the instrument is calibrated according to the Soilnet soil moisture data in the source region of the instrument, and the relationship between the soil volumetric water content θv and the fast neutron is established. The data of June 26-27, and July 16-17, respectively, which have obvious differences in dry and wet conditions, were selected. The data from June 26 to 27 showed low soil moisture content, so the average of the three values of 4 cm, 10 cm and 20 cm was used as the calibration data, and the variation range was 22% to 30%; meanwhile , the data from July 16 to 17 showed high soil moisture content, so the average of the two values of 4cm and 10 cm was used as the calibration data, and the variation range was 28% - 39%, and the final average N0 was 3597. 4) Soil Moisture Calculation According to the formula, the hourly soil water content data is calculated.
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
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.
According to the schedule of the project implementation plan and commissioned by the project team management committee, the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences organized a team of 6 people to carry out a field investigation in the Aral Sea from November 15 to November 26 in view of the desertification, salinization and vegetation construction of the surrounding land. The history, current situation, harnessing work, achievements and pressing problems of soil desertification and salinization around the Aral Sea were preliminarily understood. Sampling of vegetation and soil was carried out, and the technical idea to solve the problem was put forward, that is, planting Halophytes in brackish and saline groundwater to realize rapid vegetation construction in saline-alkali land. Through on-the-spot investigation, the investigation group believed that the implementation of vegetation construction in saline-alkali land should mainly focus on halophytes and local tree species. According to the distribution law of local halophytes and the characteristics of main constructive species in saline-alkali land, combined with the climatic conditions of the implementation site, seven halophytes, such as Salt-eared Tree, should be selected for planting demonstration. After the investigation, the investigation group put forward three specific suggestions on vegetation construction in saline-alkali land.