Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 660

  • MODIS evapotranspiration data of 18 key nodes of Pan third pole (2000-2016)

    MODIS evapotranspiration data of 18 key nodes of Pan third pole (2000-2016)

    Evapotranspiration (ET) is the process which changes from liquid or solid to vapor returning to the atmosphere in hydrological cycles since precipitation arrives at the ground. It is usually the sum of evaporation of surface soil moisture and transpiration (T) in plants. It is the key parameter in the study of global change. At present, THE EVAPotranspiration data product of MODIS satellite is an important data source for monitoring the temporal and spatial changes of the surface, and surface evapotranspiration is an important part of water balance in the earth-gas interaction. Book which has high space-time resolution MODIS16 products as the foundation, global land evaporation in area along the whole area separated from 31 key nodes and Laos, Cambodia's railway, China and myanmar oil and gas pipeline and elegant high iron three key verification area ET cutting, estimation, get the key node area of 8 to 16 days ET products, time range is 2000-2016. Is mainly used in the areas related to all the way the surface of water and energy balance in the process of simulation and dynamic monitoring and management of regional water resources rationally, especially to the scientific allocation of water resources and realize the efficient utilization of water resources has important practical significance, to be able to have a purpose of the related research of area along the area to provide data support and reference.

    2020-08-06 101 0 View Details

  • The second glacier inventory dataset of China (version 1.0) (2006-2011)

    The second glacier inventory dataset of China (version 1.0) (2006-2011)

    China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.

    2020-07-29 58656 711 View Details

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of meteorological elements gradient of Alpine meadow and grassland ecosystem superstation, 2018)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of meteorological elements gradient of Alpine meadow and grassland ecosystem superstation, 2018)

    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.

    2020-07-25 1797 49 View Details

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Yulei station on Qinghai lake, 2018)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Yulei station on Qinghai lake, 2018)

    This dataset contains the flux measurements from the Qinghai Lake eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 2 to October 18 in 2018. The site (100° 29' 59.726'' E, 36° 35' 27.337'' N) was located on the Yulei Platform in Erlangjian scenic area, Qinghai Province. The elevation is 3209m. The EC was installed at a height of 16.1m, 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 (Gill&Li7500A) was about 0.17 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 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 3% 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. Data during October 13 to December 31, 2018 were absent due to the unavailable collecting condition in winter. 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.

    2020-07-25 1107 21 View Details

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Alpine meadow and grassland ecosystem Superstation, 2018)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Alpine meadow and grassland ecosystem Superstation, 2018)

    This dataset contains the flux measurements from the Alpine meadow and grassland ecosystem Superstation superstation eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from September 2 to December 18 in 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 EC was installed at a height of 4.5 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 (CSAT3A &EC150) was about 0.17 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 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 3% 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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.

    2020-07-25 1165 22 View Details

  • Qilian Mountains integrated observatory network: cold and arid research network of Lanzhou university (eddy covariance system of Guazhou station, 2018)

    Qilian Mountains integrated observatory network: cold and arid research network of Lanzhou university (eddy covariance system of Guazhou station, 2018)

    This dataset contains the flux measurements from the Guazhou station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from September 24 to December 31 in 2018. The site (95.673E, 41.405N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2016 m. The EC was installed at a height of 4.0 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 0.17 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 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 3% 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. Suspicious data were marked in red. 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 Liu et al. (2011) for data processing) in the Citation section.

    2020-07-25 1061 25 View Details

  • Monthly temperature grid data set of Qinghai Tibet Plateau (2000-2015)

    Monthly temperature grid data set of Qinghai Tibet Plateau (2000-2015)

    The Qinghai Tibet Plateau belongs to the plateau mountain climate. The temperature and its seasonal variation have been one of the hot spots in the global climate change research. The data includes the temperature data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation on the data of national weather station in Qinghai Tibet Plateau. The data can be used to analyze the temporal and spatial distribution of air temperature in the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the law of temperature change with time in the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.

    2020-07-23 143 0 View Details

  • Photosynthetically active radiation absorption coefficient dataset in Qinghai Tibet Plateau (2000-2015)

    Photosynthetically active radiation absorption coefficient dataset in Qinghai Tibet Plateau (2000-2015)

    Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.

    2020-07-23 200 0 View Details

  • Gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015(WorldPop1.0)

    Gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015(WorldPop1.0)

    Gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.

    2020-07-23 670 2 View Details

  • Spatial distribution map of ecological carrying capacity in One Belt And One Road area in 2015

    Spatial distribution map of ecological carrying capacity in One Belt And One Road area in 2015

    Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.

    2020-07-22 1261 36 View Details

  • Atmospheric forcing dataset along the Belt and Road (2000-2015)

    Atmospheric forcing dataset along the Belt and Road (2000-2015)

    The atmospheric forcing dataset for along the Belt and Road from 2000 to 2015 comes from CRUNCEP. CRUNCEP is an atmospheric forcing dataset used forcing the land surface models. Specifically, this long time series data set (including temperature, precipitation, temperature, etc.) is used to drive the Community Land Model (CLM) Land Model in the long term. The CRUNCEP is a combination of two existing datasets; the CRU TS3.2 0.5 X 0.5 monthly data covering the period 1901 to 2002 and the NCEP reanalysis 2.5 X 2.5 degree 6-hourly data covering the period 1948 to 2016. The CRUNCEP dataset has been used to force CLM for studies of vegetation growth, evapotranspiration, and gross primary production and for the TRENDY (trends in net land-atmosphere carbon exchange over the period 1980-2010) project, among many other use cases. The CRUNCEP data archived in this dataset is Version 7.

    2020-07-22 170 0 View Details

  • S receiver functions of the Northeastern Tibetan Plateau (2009-2016)

    S receiver functions of the Northeastern Tibetan Plateau (2009-2016)

    The dataset partially used in the study of paper 2018GC007986 includes S receiver functions derived from 48 permanent stations and 11 stations of a temporary HY array deployed in the northeastern Tibetan Plateau. The dataset as a zipped file contains one folder, two files including NETibet_SRF.QBN and NETibet_SRF.QHD. A spiking deconvolution in the time domain is used to calculate the P and S receiver functions, all the S receiver functions have been visually inspected to remove the bad traces that obviously different from the majority. The dataset is applied to explore the lithospheric structure and understand the mechanism of northeastern expansion and growth of NE Tibetan Plateau.

    2020-07-17 714 3 View Details

  • Regional water system and basin zoning data of 31 key nodes of Pan third pole (2018)

    Regional water system and basin zoning data of 31 key nodes of Pan third pole (2018)

    Inland water system and river basin regional dataset are the key hydrological parameters in the study of global change. Waterr distribution is of great significance to the study of the characteristics, morphological characteristics, changes, time distribution of various types of water bodies at the nodes, and the law of regional differentiation. The basic data is downloaded from DIVA-GIS, and is subset and resampled by administrative boundary dataset of all 31 key nodes as the research areas. The data concludes the distribution of lakes and reservoirs (planar River system) and rivers (linear River basin) . Finally, the data of water system and river basin in 31 key node regions are stored and obtained. This data set serves as the research basis for all hydrological remote sensing data and provides hydrological base data for the project. This data set can be updated in real time according to the government information and the changing trend of water system where node is located.

    2020-07-08 164 3 View Details

  • Pan third pole 31 key nodes regional administrative boundary dataset (2015)

    Pan third pole 31 key nodes regional administrative boundary dataset (2015)

    The Administrative boundary dataset is the base in the global change research, and it is important for the whole project.At present, DIVA-GIS is the basic source of administrative boundary. Whole national administrative boundary shapefiles were downloaded from DIVA-GIS. Based on the official administrative units (municipalities) as the basic units, the administrative units at the higher level (provincial level) where the municipalities are located are stored and reserved as the research objects.If the provincial unit area of the node has exceeded 10,000 square kilometers, the provincial unit will be retained as the research area. At the same time, if the provincial unit area of the node is small, then considering the political and economic impact of the provincial level and its surrounding areas, neighboring provincial units are also combined by merging and retaining to at least 10,000 square kilometers as the research object. Finally, the administrative region data of all 31 key node regions (Abbas, Alexander, Ankara, Astana, Bangkok, Chittagong, Colombo, Dhaka, Djibouti, Ekaterinburg, Gwadar, Hambantota, Karachi, Kolkata, Kuantan, Kyaukpyu, Maldives, Mandalay, Melaka, Minsk, Mumbai, Novosibirsk, Piraeus, Rayong, Sihanouk, Tashkent, Teheran, Valencia, Vientiane, Warsaw, Yangon) are produced. This data set serves as the research basis for all remote sensing data and provides baseline data for the project. This dataset can be updated in real time according to the official or government information of the node.

    2020-07-08 158 3 View Details

  • Meteorological observation data of Kunsha Glacier (2015-2017)

    Meteorological observation data of Kunsha Glacier (2015-2017)

    This data set includes the temperature, precipitation, relative humidity, wind speed, wind direction and other daily values in the observation point of Kunsha Glacier. The data is observed from October 3, 2015 to September 19, 2017. It is measured by automatic meteorological station (Onset Company) and a piece of data is recorded every 2 hours. The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The original data meets the accuracy requirements of China Meteorological Administration (CMA) and the World Meteorological Organization (WMO) for meteorological observation. Quality control includes eliminating the systematic error caused by the missing point data and sensor failure. The data is stored as an excel file.

    2020-06-24 939 13 View Details

  • Meteorological data of the integrated observation and research station of Ngari for desert environment (2009-2017)

    Meteorological data of the integrated observation and research station of Ngari for desert environment (2009-2017)

    The data set includes meteorological data from the Ngari Desert Observation and Research Station from 2009 to 2017. It includes the following basic meteorological parameters: temperature (1.5 m from the ground, once every half hour, unit: Celsius), relative humidity (1.5 m from the ground, once every half hour, unit: %), wind speed (1.5 m from the ground, once every half hour, unit: m/s), wind direction (1.5 m from the ground, once every half hour, unit: degrees), atmospheric pressure (1.5 m from the ground, once every half hour, unit: hPa), precipitation (once every 24 hours, unit: mm), water vapour pressure (unit: kPa), evaporation (unit: mm), downward shortwave radiation (unit: W/m2), upward shortwave radiation (unit: W/m2), downward longwave radiation (unit: W/m2), upward longwave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). The temporal resolution of the data is one day. The data were directly downloaded from the Ngari automatic weather station. The precipitation data represent daily precipitation measured by the automatic rain and snow gauge and corrected based on manual observations. The other observation data are the daily mean value of the measurements taken every half hour. Instrument models of different observations: temperature and humidity: HMP45C air temperature and humidity probe; precipitation: T200-B rain and snow gauge sensor; wind speed and direction: Vaisala 05013 wind speed and direction sensor; net radiation: Kipp Zonen NR01 net radiation sensor; atmospheric pressure: Vaisala PTB210 atmospheric pressure sensor; collector model: CR 1000; acquisition interval: 30 minutes. The data table is processed and quality controlled by a particular person based on observation records. Observations and data acquisition are carried out in strict accordance with the instrument operating specifications, and some data with obvious errors are removed when processing the data table.

    2020-06-24 1722 64 View Details

  • Monthly standard weather station dataset in Sanjiangyuan (1957-2015)

    Monthly standard weather station dataset in Sanjiangyuan (1957-2015)

    Monthly meteorological data of Sanjiangyuan includes 32 national standard meteorological stations. There are 26 variables: average local pressure, extreme maximum local pressure, date of extreme maximum local pressure, extreme minimum local pressure, date of extreme minimum local pressure, average temperature, extreme maximum temperature, date of extreme maximum temperature, extreme minimum temperature and date of extreme minimum temperature, average temperature anomaly, average maximum temperature, average minimum temperature, sunshine hours, percentage of sunshine, average relative humidity, minimum relative humidity, date of occurrence of minimum relative humidity, precipitation, days of daily precipitation >=0.1mm, maximum daily precipitation, date of maximum daily precipitation, percentage of precipitation anomaly, average wind speed, maximum wind speed, date of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed and occurrence date of maximum wind speed. The data format is txt, named by the site ID, and each file has 26 columns. The names and units of each column are explained in the SURF_CLI_CHN_MUL_MON_readme.txt file. site_id lat lon elv name_cn 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Wulan 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Qiapuqia 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wudaoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Togton He 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maduo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98.10 9200.00 Shiqu 56043 34.47 100.25 3719.00 Guoluo 56046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Maqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suo County 56116 31.42 95.60 3873.10 Dingqing 56125 32.20 96.48 3643.70 Nangqian 56128 31.22 96.60 3810.00 Leiwuqi 56137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Seda

    2020-06-24 1894 64 View Details

  • Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

    Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

    The files in this data set are named as: 1. Pressure of the station: SURF_CLI_CHN_MUL_DAY-PRS-10004-SITEID.TXT 2. Temperature: SURF_CLI_CHN_MUL_DAY-TEM-12001-SITEID.TXT 3. Relative humidity: SURF_CLI_CHN_MUL_DAY-RHU-13003-SITEID.TXT 4. Precipitation: SURF_CLI_CHN_MUL_DAY-PRE-13011-SITEID.TXT 5. Evaporation: SURF_CLI_CHN_MUL_DAY-EVP-13240-SITEID.TXT 6. Wind direction and wind speed: SURF_CLI_CHN_MUL_DAY-WIN-11002-SITEID.TXT 7. Sunshine: SURF_CLI_CHN_MUL_DAY-SSD-14032-SITEID.TXT 8.0cm Ground Temperature: SURF_CLI_CHN_MUL_DAY-GST-12030-0cm-SITEID.TXT Detailed format descriptions for each data file are given in the SURF_CLI_CHN_MUL_DAY_FORMAT.doc file. The meteorological site information contained in this data set is as follows: Site_id lat lon ELV name_En 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Uran 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Chabcha 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wu Daoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Toto River 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maddo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98 98.10 9200.00 Shiqu 56 043 34.47 100.25 3719.00 Golo 56 046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56 067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Marqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suoxian 56116 31.42 95.60 3873.10 Ding Qing 56125 32.20 96.48 3643.70 Xiangqian 56128. 31.22. 96.60. 3810.00 Leiwuqi 56 137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Saida

    2020-06-23 1971 63 View Details

  • Dataset of meteorological elements of Nagqu Station of Plateau Climate and Environment (2014-2017)

    Dataset of meteorological elements of Nagqu Station of Plateau Climate and Environment (2014-2017)

    This dataset is derived from the Nagqu Station of Plateau Climate and Environment (31.37N, 91.90E, 4509 a.s.l), Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. The ground is flat, with open surrounding terrain. An uneven growth of alpine steppe, with a height of 3–20 cm. The observation time of this dataset is from January 1, 2014 to December 31, 2017. The observation elements primarily included the wind speed, air temperature, air relative humidity, air pressure, downward shortwave radiation, precipitation, evaporation, latent heat flux and CO2 flux. The precipitation , evaporation and CO2 flux data are daily cumulative values, and the other variables are daily average values. The observed data are generally continuous, but some data are missing due to power supply failure, and the missing data in this dataset are marked as NAN.

    2020-06-23 1285 58 View Details

  • Basic datasets of the Tibetan Plateau in Chinese Cryospheric Information System

    Basic datasets of the Tibetan Plateau in Chinese Cryospheric Information System

    Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".

    2020-06-23 21019 32 View Details