Observational datasets of Pan-Third Pole

Brief Introduction: The high-cold regions in China include the Qinghai Tibetan Plateau, and the alpine regions of Gansu, Inner Mongolia and Xinjiang, with a total area of about 2.9 million square kilometers. Due to the complexity of topography and geomorphology, the worldwide researches more and more focus on the surface processes of the Qinghai Tibetan Plateau and its adjacent areas. The High-cold Region Observation and Research Network for Land Surface Processes & Environment of China (HORN) has gradually formed. It integrates 17 stations of Chinese Academy of Sciences, for long term observations and researches of land surface processes, including glaciers, permafrost, lades, alpine ecosystem in the high-cold regions of China. It provides a platform support for integrated researches of earth system, through condensation of scientific problems, integration of monitoring resources, improvement of observation capability and level, long-term continuous monitoring of surface processes and environmental changes in cold regions. It also provides data support for revealing the law of climate change and water resources formation and transformation in the headwaters of big rivers, exploring the changes of ecosystem structure and service function, grasping the mechanism of natural disasters such as ice and snow freezing and thawing, and promoting the sustainable development of regional economy and society, etc. A network integrated center is set up to organize research and carry out the specific implementation of network construction. It consists of an office, an observation technology service group and a data integration management group. The participating units of HORN should sign construction/research contracts in order to implement contract-based management, perform all tasks in the contracts and accept the examination and acceptance of the network organization. The network construction should give priority to scientific research, coordinated development, relatively balanced allocation of infrastructure and observation instruments, and free sharing of data within the network. For the principle of sharing and opening, the observatories of the network are open to the whole country. The network cooperates with relevant units through consultation, agreement or contract according to specific tasks and costs; the original observation data are gradually shared based on the principle of first the network, then the department and then the society. The network carries out planned and coordinated cooperation with foreign scientific research institutions and universities, which can improve the level of network observation and expand the content of observation through the cooperation. The HORN is managed by the Chinese Academy of Sciences in the allocation of funds and resources.

Number of Datasets: 75

  • Geocryological regionalization and classification map of the frozen soil in China (1:10,000,000) (2000)

    Geocryological regionalization and classification map of the frozen soil in China (1:10,000,000) (2000)

    These data are digitized for the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 million) (Guoqing Qiu et al., 2000; Youwu Zhou et al., 2000), adopting a geocryological regionalization and classification dual series system. The geocryological regionalization system and classification system are used on the same map to reflect the commonality and individuality of the formation and distribution of frozen soil at each level. The geocryological regionalization system consists of three regions of frozen soil: (1) the frozen soil region of eastern China; (2) the frozen soil region of northwestern China; and (3) the frozen soil region of southwestern China (Tibetan Plateau). Based on the three large regions, 16 regions and several subregions are further divided. In the division of the geocryological boundary in the frozen soil area, the boundary between major regions I and III mainly consults the results of Bingyuan Li (1987). The boundary between major regions II and III is the northern boundary of the Tibetan Plateau, which is the Kunlun Mountains-Altun Mountains-Northern Qilian Mountains and the piedmont line. The boundary between major regions I and II is in the area of Helan Mountain-Langshan Mountain. The boundary of the secondary region is divided by the geomorphological conditions in regions II and III. However, in region I, it is mainly divided by the ratio of the annual temperature range A to the annual mean temperature T, and the frozen depths of various regions are taken into consideration. The classification system is divided into 8 types based on the continuity of frozen soil, the time of existence of frozen soil and the seasonal frozen depth. The various classifications of boundaries are mainly taken from the "Map of Snow, Ice and Frozen Ground in China" (1:4 million) (Yafeng Shi et al., 1988) and consult some new materials, whereas the seasonal frozen soil boundary is mainly based on the weather station data. The definitions of each classification are as follows: (1) Large permafrost: the continuous coefficient is 90%-70%; (2) Large-island permafrost: the continuous coefficient is 70%-30%; (3) Sparse island-shaped permafrost: the continuous coefficient is <30%; (4) Permafrost in the mountains; (5) Medium-season seasonal frozen soil: the maximum seasonal frozen depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal frozen depth that can be reached is <1 m; (7) Short-term frozen soil: less than one month of storage time; and (8) Nonfrozen soil. According to the data, China's permafrost areas sum to approximately 2.19 × 106 km², accounting for 22.83% of China's territory. Among those areas, the mountain permafrost is found over 0.42×106 km2, which is 4.39% of the territory of China. The seasonal frozen soil area is approximately 4.76×106 km², accounting for 49.6% of China's territory, and the instantaneous frozen soil area is approximately 1.86×106 km², i.e., 19.33% of China's territory. For more information, please see the references (Youwu Zhou et al., 2000).

    2020-10-09 27488 498

  • Meteorological observation dataset of the standard meteorological station in the Irtysh River basin(1961-2015)

    Meteorological observation dataset of the standard meteorological station in the Irtysh River basin(1961-2015)

    The "Meteorological observation dataset of the standard meteorological station in the Irtysh River basin" contains the temperature and precipitation observation data at the monthly scale of the Habahe meteorological station, Jimunai meteorological station, Buerjin meteorological station, Fuhai meteorological station, Altay meteorological station and Fuyun meteorological station of the Irtysh river basin. The time scale of the data is month. The data set started in January 1961 (data of Fuyun station was missing from January to May 1961) and ended in December 2015. The special work of ground basic data re-examined the quality of historical informatization documents and revised the site documents with problems and differences. The data set does not revise the homogeneity of data, but segments the stations with obvious heterogeneity.

    2020-08-24 3280 244

  • Aerosol optical property dataset of the Tibetan Plateau by ground-based observation (2009-2016)

    Aerosol optical property dataset of the Tibetan Plateau by ground-based observation (2009-2016)

    The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.

    2020-08-17 3759 272

  • Dataset of black carbon concentration at Mt. Everest Station from May 2015 to May 2017

    Dataset of black carbon concentration at Mt. Everest Station from May 2015 to May 2017

    Black carbon(BC) is a carbonaceous aerosol that mainly emitted from the incomplete combustion of fossil fuels or biomass. As fine particles in the atmosphere with light-absorbing characteristic, BC can significantly reduce the surface albedo when deposits on snow and ice and accelerate the melting of glaciers and snow cover. New Aethalometer model AE-33 acquires the real-time BC concentration according to the light absorption and attenuation characteristics from the different wavelengths. In addition, AE-33 uses dual-spot measurements, which can compensate for the “spot loading effect” and obtain high-quality BC concentrations. By using the real-time observation data measured by AE-33 at Mt. Everest Station, we analyzed the seasonal and diurnal variations of BC and its sources and transport processes, and we also investigated the transport mechanisms of serious polluted episodes. That can provide basis for future works on assessment of climate effects caused by BC in this region.

    2020-08-15 2552 22

  • 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 8175 113

  • 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 4700 332

  • Meteorological data of surface environment and observation network in China's cold region (2014-2017)

    Meteorological data of surface environment and observation network in China's cold region (2014-2017)

    Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing. The data includes the daily meteorological observation data sets (air temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) of the Qinghai Tibet Plateau in 2014-2017 from 17 stations of China Alpine network. The data of the three river sources are missing.

    2020-07-21 6999 232

  • 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 5471 196

  • 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 25080 104

  • Automatic weather station dataset from Guoluo station (2017)

    Automatic weather station dataset from Guoluo station (2017)

    The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.

    2020-06-03 4705 332

  • Meteorological observation dataset of Shiquan River Source (2012-2015)

    Meteorological observation dataset of Shiquan River Source (2012-2015)

    This dataset includes the temperature, precipitation, relative humidity, wind speed, wind direction and other daily values in the observation point of Shiquan River Source. The data is observed from July 2, 2012 to August 5, 2014, and from September 30, 2015 to December 25, 2015. 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.

    2020-06-03 3738 263

  • Yulong snow mountain glacier No.1, 3046 m altitude  the daily average meteorological observation dataset (2014-2018)

    Yulong snow mountain glacier No.1, 3046 m altitude the daily average meteorological observation dataset (2014-2018)

    1.The data content: air temperature, relative humidity, precipitation, air pressure, wind speed, the average daily data of total radiation and vapor pressure. 2. Data sources and processing methods: campel mountain type automatic meteorological station observation by the United States, including air temperature and humidity sensor model HMP155A;Wind speed and direction finder models: 05103-45;Net radiation instrument: CNR four radiometer component;The atmospheric pressure sensor: CS106;The measuring cylinder: TE525MM.Automatic meteorological station every ten minutes automatic acquisition data, after complete automatic acquisition daily meteorological data then daily mean value were calculated statistics. 3. Data quality description: automatic continuous access to data. 4.Data application results and prospects: the weather stations of underlying surface type as the alpine meadow, meteorological data can provide basic data for GaoHan District land surface process simulation.

    2020-06-02 2393 18

  • Yulong snow mountain glacier No.1, 4 300 m altitude, 2014-2018, the daily average meteorological observation dataset

    Yulong snow mountain glacier No.1, 4 300 m altitude, 2014-2018, the daily average meteorological observation dataset

    1.The data content: air temperature, relative humidity, precipitation, air pressure, wind speed and vapor pressure. 2. Data sources and processing methods: campel mountain type automatic meteorological station observation by the United States, including air temperature and humidity sensor model HMP155A;Wind speed and direction finder models: 05103-45;The atmospheric pressure sensor: CS106;The measuring cylinder: TE525MM.Automatic meteorological station every ten minutes automatic acquisition data, after complete automatic acquisition daily meteorological data then daily mean value were calculated statistics. 3.Data quality description: automatic continuous access to data. 4.Data application results and prospects: the weather stations set in the upper of the glacier terminal, meteorological data can be used to simulate for predict the future climate change under the background of type Marine glacial changes in response to global climate change research provides data.

    2020-06-02 2480 19

  • Measurement data from 26 crustal displacement observation stations of Qilian mountain (2017-2018)

    Measurement data from 26 crustal displacement observation stations of Qilian mountain (2017-2018)

    High-frequency continuous GPS observation can effectively monitor the kinematics of crustal deformation. The Qilian Mountains region is an important constraint boundary of the northeastern margin of the Qinghai-Tibet Plateau. The study of this region can provide important implications for the dynamic process of the growth and uplift of the Tibetan Plateau and the internal deformation of the Tibetan Plateau. At the local level, it can be discussed whether there is creepage in the Haiyuan fault and the movement mode of the northeastern margin of the Qinghai-Tibet Plateau. The data comes from 26 fixed stations set up by the research group in the Qilian Mountain area. The site selection requirements are strict, and the high-frequency continuous GPS receiver is Provided by trimble, the data quality is good, the data can be applied not only to geodynamic research, but also to related earth science research such as meteorological precipitation.

    2020-05-30 3195 10

  • Regular meteorological element datasets for 22 observing sites in Sri Lanka (2008-2018)

    Regular meteorological element datasets for 22 observing sites in Sri Lanka (2008-2018)

    This data set includes the daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, and water vapor pressure observed from 22 international exchange stations in Sri Lanka from January 1, 2008 to October 1, 2018. The data was downloaded from the NCDC of NOAA. The data set processing method is that the original data is quality-controlled to form a continuous time series. It satisfies the accuracy of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO), and eliminates the systematic error caused by the failure of the tracking data and the sensor. The meteorological site information contained in this dataset is as follows: LATITUDE&emsp;LONGITUDE&emsp;ELEVATION&emsp;&ensp;COUNTRY&emsp;&emsp;STATION NAME +09.800&emsp;&emsp;+080.067&emsp;&emsp;&ensp;+0015.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;KANKASANTURAI +09.650&emsp;&emsp;+080.017&emsp;&emsp;&ensp;+0003.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;JAFFNA +09.267&emsp;&emsp;+080.817&emsp;&emsp;&ensp;+0002.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;MULLAITTIVU +08.983&emsp;&emsp;+079.917&emsp;&emsp;&ensp;+0003.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;MANNAR +08.750&emsp;&emsp;+080.500&emsp;&emsp;&ensp;+0098.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;VAVUNIYA +08.539&emsp;&emsp;+081.182&emsp;&emsp;&ensp;+0001.8&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;CHINA BAY +08.301&emsp;&emsp;+080.428&emsp;&emsp;&ensp;+0098.8&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;ANURADHAPURA +08.117&emsp;&emsp;+080.467&emsp;&emsp;&ensp;+0117.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;MAHA ILLUPPALLAMA +08.033&emsp;&emsp;+079.833&emsp;&emsp;&ensp;+0002.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;PUTTALAM +07.706&emsp;&emsp;+081.679&emsp;&emsp;&ensp;+0006.1&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;BATTICALOA +07.467&emsp;&emsp;+080.367&emsp;&emsp;&ensp;+0116.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;KURUNEGALA +07.333&emsp;&emsp;+080.633&emsp;&emsp;&ensp;+0477.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;KANDY +07.181&emsp;&emsp;+079.866&emsp;&emsp;&ensp;+0008.8&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;BANDARANAIKE INTL COLOMBO +06.900&emsp;&emsp;+079.867&emsp;&emsp;&ensp;+0007.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;COLOMBO +06.822&emsp;&emsp;+079.886&emsp;&emsp;&ensp;+0006.7&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;COLOMBO RATMALANA +06.967&emsp;&emsp;+080.767&emsp;&emsp;&ensp;+1880.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;NUWARA ELIYA +06.883&emsp;&emsp;+081.833&emsp;&emsp;&ensp;+0008.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;POTTUVIL +06.817&emsp;&emsp;+080.967&emsp;&emsp;&ensp;+1250.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;DIYATALAWA +06.983&emsp;&emsp;+081.050&emsp;&emsp;&ensp;+0667.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;BADULLA +06.683&emsp;&emsp;+080.400&emsp;&emsp;&ensp;+0088.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;RATNAPURA +06.033&emsp;&emsp;+080.217&emsp;&emsp;&ensp;+0013.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;GALLE +06.117&emsp;&emsp;+081.133&emsp;&emsp;&ensp;+0020.0&emsp;&emsp;&emsp;SRI LANKA&emsp;&ensp;HAMBANTOTA

    2020-05-14 2393 30

  • Surface meteorological observation data product of TP (1979-2016)

    Surface meteorological observation data product of TP (1979-2016)

    1)Data content (including elements and meanings): surface meteorological observation data product of TP in 1979-2016 2)Data source and processing method: In .tif format, can be opened and analysed in arcgis. 3)Data quality description: daily resolution 4)Data application results and prospects: Based on the long-term observation data of the 17 stations of HORN, establish a series of data series of meteorological, hydrological and ecological elements in the Pan-Earth region; Strengthen observation and sample and sample verification, and complete the inversion of meteorological elements, lake water quantity and water quality, aboveground vegetation biomass, glacier and frozen soil changes; based on Internet of Things technology, develop multi-station networked meteorological, hydrological, The ecological data management platform realizes real-time acquisition and remote control and sharing of networked data.

    2020-05-14 6390 429

  • The active layer moisture monitoring dataset of Qinghai-Tibet Plateau Beiluhe meteorological station (2017.1-2018.10)

    The active layer moisture monitoring dataset of Qinghai-Tibet Plateau Beiluhe meteorological station (2017.1-2018.10)

    The active layer is one of the main characteristics of permafrost. It melts in warm season and freezes in cold season, showing seasonal changes. The amount of water content in the active layer has certain influence on the temperature of the permafrost, thus affecting the stability of the permafrost.The data set is mainly composed of active layer moisture data. The monitoring station is located at 92°E, 34°N, with an elevation of 4600m. The monitoring site is flat, the vegetation type is alpine meadow, and the water probe used by Beilouhe Meteorological Station is CS615. The data set is used to monitor water at 5 depths below the surface, 10 cm, 20 cm, 40 cm, 80 cm and 160cm. The time interval of the data set is 1 day and is 30 minutes.Mean value of data once, data is stable and continuous during monitoring.By combining the data of soil heat flux and frozen soil temperature, the thermal change process and mechanism of active layer can be carried out.

    2020-04-23 1532 270

  • The meteorological data monitoring dataset of Qinghai-Tibet Plateau Beiluhe meteorological station (2014.1-2018.10)

    The meteorological data monitoring dataset of Qinghai-Tibet Plateau Beiluhe meteorological station (2014.1-2018.10)

    The meteorological data set of Beiluhe station mainly includes 7 meteorological elements such as atmospheric temperature, wind speed, wind direction, humidity, atmospheric pressure, solar radiation and daily rainfall of 2m. The monitoring station of the data set is located at 92 ° E, 35 ° N and 4600m above sea level. The terrain of the monitoring site is flat, and the vegetation type is alpine meadow. The measuring sensors are manufactured by Campell company, of which the measurement of high temperature and humidity is transmitted The sensor model is HMP45C, the wind speed and direction sensor model is 05103, the atmospheric pressure measurement sensor model is ptb-210, the solar radiation sensor model is nr01, the rain gauge sensor model is t-200b, the time interval of this data set is 1 day, which is obtained through the calculation of 30 minute data. During the monitoring period, the data is stable and continuous. Through the analysis of meteorological data, we can recognize Beilu river The change of local climate is not only helpful, but also an indispensable index in the study of frozen soil environment and engineering.

    2020-04-23 2622 309

  • Water temperature observation data at Nam Co Lake in  Tibet (2011-2014)

    Water temperature observation data at Nam Co Lake in Tibet (2011-2014)

    This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.

    2020-03-16 4385 71

  • Dataset of the synthetic monitoring station at the small cachment of Sumu Jaran Lakes

    Dataset of the synthetic monitoring station at the small cachment of Sumu Jaran Lakes

    This dataset contains data for comprehensive monitoring in the small watershed of Sumu Jaran in the Badain Jaran Desert from 2012 to 2013. The small watershed of Sumu Jaran is composed of two lakes, namely North Lake and South Lake of Sumu Jaran. The latitude and longitude range is: 39° 46' 18.24" to 39° 49' 17.25" north latitude, 102° 23' 40.53 " to 102° 26' 59.27" east longitude. The observation instruments are mainly arranged around the South Lake of Sumu Jaran, including scintillator (BLS450), automatic weather station (net radiation, rainfall, wind speed, wind direction, air humidity, pressure, E601 type evaporation dish), soil monitoring station (soil temperature, water content and tension pF-meter) and one groundwater monitoring hole. The data released this time are the monitoring results from September 2012 to December 2013. Post-monitoring data will be released in version 2.0. For the layout, coordinates, and type of the instrument, see the layout of the small watershed monitoring system.pdf, coordinates of the monitoring point.xls, and location and equipment of the monitoring point.tif.

    2020-03-07 9100 189