This dataset (version 1.5) is derived from the complementary-relationship method, with inputs of CMFD downward short- and long-wave radiation, air temperature, air pressure, GLASS albedo and broadband longwave emissivity, ERA5-land land surface temperature and humidity, and NCEP diffuse skylight ratio, etc. This dataset covers the period of 1982-2017, and the spatial coverage is Chinese land area. This dataset would be helpful for long-term hydrological cycle and climate change research. Land surface actual evapotranspiration (Ea)，unit: mm month-1. The spatial resolution is 0.1-degree; The temporal resolution is monthly; The data type is NetCDF; This evapotranspiration dataset is only for land surface.
2022-04-18
This data is conventional and satellite data of six hour resolution for the Great Lakes region of Central Asia. The conventional data include the observation of ground stations and sounding stations in the Great Lakes region of Central Asia and its surrounding areas (China, Kazakhstan, Kyrgyzstan, Turkmenistan, Tajikistan, Uzbekistan, Afghanistan, Russia, Iran, Pakistan, India, etc.), and the observation elements include temperature, pressure, wind speed and humidity, with the average number of stations in each time It is about 600, and the interval between stations is between 10-100km; the satellite data comes from the cloud guide wind retrieved by polar orbiting satellites (NOAA series and MetOp Series). All the data are from the global telecommunication system (GTS), and the observation data with poor quality are eliminated through quality control. The data can be applied to the data assimilation of the Great Lakes region in Central Asia, and also to the numerical simulation of the Great Lakes region in Central Asia.
2022-04-18
As the “water tower of Asia”, Tibetan Plateau (TP) are the resource of major rivers in Asia. Black carbon (BC) aerosol emitted from surrounding regions can be transported to the inner TP by atmospheric circulation and consequently deposited in snow, which can significantly influence precipitation and mass balance of glaciers. Five Aethalometers are used to mornitoring black carbon concentration at 5 stations on the Tibetan Plateau. It can provide basic dataset to study the effects of BC to the environment and climate over the Tibetan Plateau, as well as the pollutants transport.
2022-04-15
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
2021-08-05
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.
2021-03-10
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.
2021-01-26
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.
2021-01-26
The dataset of CMA operational meteorological stations observations in the Heihe river basin were provided by Gansu Meteorological Administration and Qinghai Meteorological Administration. It included: (1) Diurnal precipitation, sunshine, evaporation, the wind speed, the air temperature and air humidity (2, 8, 14 and 20 o'clock) in Mazongshan, Yumen touwnship, Dingxin, Jinta, Jiuquan, Gaotai, Linze, Sunan, Zhangye, Mingle, Shandan and Yongchang in Gansu province (2) the wind direction and speed, the temperature and the dew-point spread (8 and 20 o'clock; 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 and 50hpa) in Jiuquan, Zhangye and Mingqin in Gansu province and Golmud, Doulan and Xining in Qinghai province (3) the surface temperature, the dew point, the air pressure, the voltage transformation (3 hours and 24 hours), the weather phenomena (the present and the past), variable temperatures, visibility, cloudage, the wind direction and speed, precipitation within six hours and unusual weather in Jiuquan, Sunan, Jinta, Dingxin, Mingle, Zhangye, Gaotai, Shandan, Linze, Yongchang and Mingqin in Gansu province and Tuole, Yeniugao, Qilian, Menyuan, Xining, Gangcha and Huangyuan in Qinhai province.
2020-10-12
The dataset of LAS (Large Aperture Scintillometer: BLS450, made in Germany) observations was obtained at the A'rou freeze/thaw observation station from Mar. 11 to Jul. 11, 2008. The transmitter (E100°28′16.4″, N38°03′24.3″, 11.2m) and the receiver (E100°27′25.9″, N38°02′18.1″, 11.5m) were 2390m away from each other and the operating altitude was 9.5m. The observation item was the atmospheric refractive index structural parameters (Cn2). The transmitting frequency was 5HZ and the data were output per minute. The processed data were archived in a 30 minutes cycle. The data were named after WATER_LAS_A'rou_yyyymmdd-yyyymmdd.csv (yyyymmdd-yyyymmdd for observation time). The missing data were marked "None".
2020-10-09
The monthly average vegetation index data of Heihe River Basin is based on MODIS 1 km and 250 m NDVI products. From 250 m products, the grid value of Heihe River Basin is proposed as precision control, and the 1 km product is modified by HASM method. The monthly average vegetation index of Heihe River Basin from 2001 to 2011 was obtained by fusing multi-source NDVI data using HASM method. Resolution: 1km * 1km The average precipitation data set of Heihe River Basin adopts the data information of 21 meteorological conventional observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River basin provided by Heihe planning data management center. The daily precipitation data of each station from 1961 to 2010 is calculated. If the coefficient of variation is greater than 100%, the daily precipitation distribution trend can be obtained by using the geographic weighted regression to calculate the relationship between the station and the geographical terrain factors; if the coefficient of variation is less than or equal to 100%, the relationship between the station precipitation value and the geographical terrain factors (longitude, latitude, elevation) is calculated by ordinary least square regression, and the daily precipitation score is obtained HASM (high accuracy surface modeling method) was used to fit and modify the residual error after removing the trend. Finally, the trend surface results and residual correction results are added to get the annual average precipitation distribution of Heihe River Basin from 1961 to 2010. Time resolution: annual average precipitation from 1961 to 2010. Spatial resolution: 500M.
2020-10-09
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