Brief Introduction: 高寒区一般指海拔较高、气候寒冷的地区。中国的高寒区包括整个青藏高原，以及甘肃、内蒙古和新疆的高山地区，总面积约为290万平方公里。高寒区形成了特殊的大气过程、水文过程及生态过程，其环境变化研究是地表过程研究的重要组成部分，是开展地球系统集成研究的重要切入口。高寒区广泛分布着冰川、冻土、积雪和湿地等，是我国大江大河的源头区和重要的生态屏障区，同时又是气候条件恶劣、生态环境脆弱、经济发展水平低的地区。 从阐明高寒区大气过程、水文过程、生态过程及其相互间的联系这一科学问题的角度考虑，中国科学院组织院内所属的17个野外站（点），并通过与其他系统的野外站联合组建“高寒区地表过程与环境监测研究网络”（简称“高寒网”），通过凝炼科学问题、整合监测资源、统一观测手段、完善观测能力、提高观测水平，实现对高寒区地表过程与环境变化的长期连续监测，为地球系统集成研究、关键区域对全球变化的影响与响应、定量化辨识人类活动在全球变化中的作用等研究提供平台支撑；为揭示大江大河源头区气候变化规律和水资源形成转化规律、合理开发利用水资源，探明生态系统结构与服务功能变化、构建生态屏障，掌握冰雪冻融等自然灾害发生机理、科学防灾减灾，以及促进区域经济社会可持续发展等提供数据支持。 “高寒网”以中国科学院所属17个野外站为主体，由中国科学院作为牵头单位，组织成立“高寒网”科学委员会，把握网络研究的重要科学问题，指导网络建设的计划和发展方向。设立网络综合中心，负责组织研究和开展网络建设的具体实施工作，下设办公室、观测技术服务组和数据集成管理组。网络实施合同式管理，各参建单位需签定建设/研究合同，履行合同规定的各项任务，接受网络组织部门的考核与验收。网络建设过程中，本着科研优先，协调发展的原则，相对均衡配置网络内部各观测台站的基础设施以及观测仪器，获得的数据在网络内部实现无偿共享；本着共享开放的原则，网络的各观测站对全国开放，根据具体工作任务和成本的需要，通过协商、协议或合同的形式与相关单位开展合作，获得的数据本着先网络，再部门，后社会的原则，使原始观测数据逐步共享；本着以我为主，扩大交流的原则，整个网络开展有计划、有协调地与国外科研单位和大学合作，通过合作提高网络观测水平与扩大观测内容，围绕提高网络整体观测和研究实力的目标开展工作。“高寒网”由中国科学院层面统筹进行经费资源配置。
Number of Datasets: 65
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.
2019-09-15 0 11 View Details
This data set includes the biomass and photosynthesis observational data of the highland spring barley experimental plot at the Lhasa Farm Experimental Station and the meteorological data observationally obtained at the Damxung Grass Experimental Station. The time range is 2006-2009. Biomass observation method: The sampling area of each sample is 25 cm*25 cm. Photosynthetic data observation: The instrument is a LiCor-6400. The biomass data are manually entered according to the record book. The photosynthetic data are automatically recorded by the instrument. The average wind speed, prevailing wind direction, temperature, atmospheric pressure and relative humidity in the daily values of meteorological data are averaged over half-hour data. The precipitation and total radiation data are automatically recorded by the observation system. The observation process of biomass data is in strict accordance with the agronomic method, and it can be applied to the estimation of agricultural productivity. In the process of photosynthetic data observation, the operation of the instrument and the selection of the observation object are strictly in accordance with professional requirements and can be used in photosynthetic parameter simulations estimating plant leaf and productivity. The Tibetan Plateau farmland ecosystem observation data includes: 1) aboveground biomass; 2) CO2 response photosynthetic data; 3) light-response photosynthetic data; and 4) daily meteorological data in Damxung Monitoring Point. Data collection locations: Lhasa Agricultural Ecology Experimental Station, Chinese Academy of Sciences, Longitude: 91°20’, Latitude: 29°41’, Altitude: 3688 m and Damxung Alpine Meadow Carbon Flux Observation Station, Longitude: 91°05′, Latitude: 30°25′, Altitude: 4333 m.
2019-09-15 0 7 View Details
This data set includes carbon flux data and soil moisture data obtained from the Swamp Meadow Carbon Flux Station in Dangxiong. The temporal coverage is from 2009 to 2010. The temporal resolution of carbon flux data is 4 hours, and it records data from 00:00 to 20:00; the temporal resolution of the soil moisture data is 1 day. All data were automatically recorded by the vorticity-related observing instruments and manually checked. The observation and collection of the data were performed in strict accordance with the instrument operating specifications. During the data observation process, the operation of the instrument and the selection of the observation object were strictly in accordance with professional requirements. The data were collected at Dangxiong Wetland Carbon Flux Observatory of Lhasa Agro-ecological Station of Chinese Academy of Sciences, longitude: 91°07’; latitude: 30°50’; and altitude: 4333 m. The data set can be used in simulations of plant leaf photosynthetic parameter and evaluations of productivity to study the water and carbon processes of wetland ecosystems and their responses to climate change.
2019-09-15 0 7 View Details
This data set includes biomass survey data observed from the carbon flux station in the Guoluo Army Ranch in Qinghai from 2005 to 2009. Carbon flux data observation method: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. The carbon flux data were automatically recorded by the instruments and manually checked. Observations and data collection were carried out in strict accordance with the instrument operating specifications and were published in relevant academic journals. During the data observation process, the operation of the instrument and the selection of the observational objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and production estimation. 1) Biological observational data of the Guoluo meadow ecosystem: Date, site number, vegetation type, plot number, aboveground biomass (g/m²), underground biomass (g/m²), total biomass (g/m²) 2) Carbon flux observational data of the Guoluo meadow ecosystem: Site number, date, vegetation type, soil type, water vapor flux (w/m²), carbon flux (mg/m²·S) The fixed point observation data are of high precision.
2019-09-15 0 14 View Details
This data set contains data on the concentrations of persistent organic pollutants (POPs) and total suspended particulate (TSP) in the atmosphere at a station in southeastern Tibet (Lulang). The samples were collected using an atmospheric active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head. The gaseous POPs and TSPs were collected. The sampling period for each sample was 2 weeks. The types of observed POPs include organochlorine pesticides (OCPs), polychorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). Only gaseous concentrations were detected for OCPs and PCBs, while both gaseous concentrations and particulate concentrations were detected for PAHs. All of the data contained in the data set are measurement data. The samples were collected in the field at the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The sampler was an atmospheric flow active sampler equipped with a tandem fibreglass membrane-polyurethane foam sampling head, in which the fibreglass membrane was used to collect TSPs and the polyurethane foam was used to adsorb gaseous pollutants in the atmosphere. During the sampling period, the sampler was run every other day for approximately 24 hours each time, and each sample was collected for 2 weeks. The atmospheric volume collected for each sample was 500-700 cubic metres. Both gaseous and particulate POP samples were prepared and analysed in the Key Laboratory of Tibetan Environment Changes and Land Surface Processes, CAS. The sample preparation steps included Soxhlet extraction, silica-alumina column purification, removal of macromolecular impurities by a GPC column, concentration to a defined volume, etc. The analytical test instrument was a gas chromatography/ion trap mass spectrometer (Finnigan-TRACE GC/PolarisQ) produced by Thermo Fisher Scientific. The column used to separate OCPs and PCBs was a CP-Sil 8CB capillary column (50 m × 0.25 mm × 0.25 μm), and the column used to separate PAHs was a DB-5MS capillary column (60 m x 0.25 mm x 0.25 μm). The total suspended particulate concentration in the atmosphere was determined by the gravimetric method, and the accuracy of the weighing balance was 1/100,000 g. The field samples were subjected to strict quality control with laboratory blanks and field blanks. The detection limit of a given compound was 3 times the standard deviation of the concentration of the corresponding compound in the field blank; if the compound was not detected in the field blank, the detection limit of the method was determined by the lowest concentration of the working curve. For a sample, concentrations above the detection limit of the method are corrected by subtracting the detection limit; concentrations below the detection limit of the method but higher than 1/2 times the detection limit are corrected by subtracting half the method detection limit; and concentrations below 1/2 times the detection limit are considered undetected. The recovery rate of PAH laboratory samples was between 65-120%, and that of OCPs was between 70-130%; the sample concentrations were not corrected by the recovery rate. In the table, undetected data are marked as BDL; data marked in black italics are data corrected by subtracting 1/2 the method detection limit.
2019-09-15 0 5 View Details
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.
2019-09-15 0 48 View Details
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.
2019-09-15 0 5 View Details
This data set includes daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, water vapour pressure and other elements obtained from the Integrated Observation and Research Station of the Westerly Environment in Muztagh Ata from 18 May 2003 to 31 December 2016. The data are obtained by an automatic meteorological station (Vaisala) that recorded one measurement every 30 minutes. The data set was processed as a continuous time series after the original data were quality controlled. This data set satisfies the accuracy requirements of the meteorological observations of the National Weather Service and the World Meteorological Organization (WMO), and the systematic errors caused by the tracking data and sensor failure have been eliminated. The data set has mainly been applied in the fields of glaciology, climatology, environmental change research, cold zone hydrological process research and frozen soil science. Furthermore, this data set is mainly used by professionals engaged in scientific research and training in atmospheric physics, atmospheric environment, climate, glaciers, frozen soil and other disciplines.
2019-09-15 0 15 View Details
The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
2019-09-15 0 26 View Details
This data set is the concentrations of atmospheric and water POPs in Nam Co, including time series of gas phase OCP, PCBs, PAHs and particulate PAHs in the atmosphere; dissolve and particulate POPs in water. An active air sampler (AAS) was deployed on the roof of Nam Co Monitoring and Research Station for Multisphere Interactions (NCMORS) and the air monitoring was conducted for two consecutive years from September 2012 to September 2014. The flow rate of AAS was 60 L min-1 and the air samples were collected every 2 weeks with a volume of approximately 600 m3 for each sample. The air stream passes first through glass fiber filters (GFFs 0.7 μm, Whatman) to collect the total suspended particulates (TSP) and then through polyurethane foam (PUF, 7.5×6 cm diameter) to retain the POPs in gas phase. Fifteen sites around the Nam Co Lake (surface lake water, 0–1 m depth) were selected to obtain the spatial distribution of POPs in lake water. The water samples (200 L) were filtered with GFFs to obtain the total suspended particulate matter (SPM), and then pumped through an XAD-2 resin column to collect the dissolved phase compounds. All the samples were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently, and were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs and PCBs and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Field blanks and procedural blanks were prepared. The recoveries ranged from 64% to 112% for OCPs, and 65% to 92% forPAHs. The reported concentrations were not corrected for recoveries.
2019-09-15 0 0 View Details
This dataset contains the monthly hydrological characteristic values of the Jiangzi hydrological station on the Nianchu River in the Yarlung Zangbo River for many years (e.g., coefficient of variation, deviation factor, and nonuniform coefficient). It can be used to study the hydrological characteristics of the Yarlung Zangbo River. The original data are the national hydrological station data, and the quality requirements are the same as the national standards. This data sheet has four fields. Field 1: Time, month Field 2: Coefficient of variation Field 3: Deviation factor Field 4: Nonuniform coefficient
2019-09-15 0 5 View Details
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Sidalong Station from October 24 to December 31, 2018. The site (38.430°E, 99.931°N) was located on a forest in the Kangle Sunan, which is near Zhangye city, Gansu Province. The elevation is 3059 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (0.5, 3, 13, 24, and 48 m), wind speed and direction profile (windsonic; 0.5, 3, 13, 24, and 48 m), air pressure (1.5 m), rain gauge (24 m), infrared temperature sensors (4 m and 24m, vertically downward), photosynthetically active radiation (4 m and 24m), soil heat flux (-0.05 m and -0.1m), soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m and -0.6mr), four-component radiometer (24 m, towards south), sunshine duration sensor(24 m, towards south). The observations included the following: air temperature and humidity (Ta_0.5 m, Ta_3 m, Ta_13 m, Ta_24 m, and Ta_48 m; RH_0.5 m, RH_3 m, RH_13 m, RH_24 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_0.5 m, Ws_3 m, Ws_13 m, Ws_24 m, and Ws_48 m) (m/s), wind direction (WD_0.5 m, WD_3 m, WD_13 m, WD_24 m, and WD_48 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_A, IRT_B) (℃), photosynthetically active radiation (PAR_A, PAR_B) (μmol/ (s m^2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, and Ts_60 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, and Ms_60 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, and SWP_60cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, and Ec_60cm)(μs/cm), sun time (h). 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 soil water potential in the area is so low that it has exceeded the sensor measurements. (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: 2018-6-10 10:30.
2019-09-15 0 9 View Details
The daily lake level observation data of lake Namco obtained from the Integrated Observation and Research Station of Multisphere in Namco in summers during 2007 to 2016. Every winter, the water gauge is destroyed by the lake ice, and it is reinstalled every summer. Taking the observational data (beginning with 0 cm) of the beginning of every year as a reference, an observational sequence is generated every year. The data set was processed by forming a continuous time series after the raw data were quality-controlled to meet the needs of lake hydrology research. Water level, unit: cm.
2019-09-15 0 8 View Details
This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²
2019-09-15 0 8 View Details
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
2019-09-15 0 5 View Details
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 LONGITUDE ELEVATION  COUNTRY  STATION NAME +09.800  +080.067   +0015.0   SRI LANKA  KANKASANTURAI +09.650  +080.017   +0003.0   SRI LANKA  JAFFNA +09.267  +080.817   +0002.0   SRI LANKA  MULLAITTIVU +08.983  +079.917   +0003.0   SRI LANKA  MANNAR +08.750  +080.500   +0098.0   SRI LANKA  VAVUNIYA +08.539  +081.182   +0001.8   SRI LANKA  CHINA BAY +08.301  +080.428   +0098.8   SRI LANKA  ANURADHAPURA +08.117  +080.467   +0117.0   SRI LANKA  MAHA ILLUPPALLAMA +08.033  +079.833   +0002.0   SRI LANKA  PUTTALAM +07.706  +081.679   +0006.1   SRI LANKA  BATTICALOA +07.467  +080.367   +0116.0   SRI LANKA  KURUNEGALA +07.333  +080.633   +0477.0   SRI LANKA  KANDY +07.181  +079.866   +0008.8   SRI LANKA  BANDARANAIKE INTL COLOMBO +06.900  +079.867   +0007.0   SRI LANKA  COLOMBO +06.822  +079.886   +0006.7   SRI LANKA  COLOMBO RATMALANA +06.967  +080.767   +1880.0   SRI LANKA  NUWARA ELIYA +06.883  +081.833   +0008.0   SRI LANKA  POTTUVIL +06.817  +080.967   +1250.0   SRI LANKA  DIYATALAWA +06.983  +081.050   +0667.0   SRI LANKA  BADULLA +06.683  +080.400   +0088.0   SRI LANKA  RATNAPURA +06.033  +080.217   +0013.0   SRI LANKA  GALLE +06.117  +081.133   +0020.0   SRI LANKA  HAMBANTOTA
2019-09-15 0 1 View Details
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of Yulei station on Qinghai lake from January 1 to October 12, 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 installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 12 and 12.5 m above the water surface, towards north), wind speed and direction profile (windsonic; 14 m above the water surface, towards north) , rain gauge (TE525M; 10m above the water surface in the eastern part of the Yulei platform ), four-component radiometer (NR01; 10 m above the water surface, towards south), one infrared temperature sensors (SI-111; 10 m above the water surface, towards south, vertically downward), photosynthetically active radiation (LI190SB; 10 m above the water surface, towards south), water temperature profile (109, -0.2, -0.5, -1.0, -2.0, and -3.0 m). The observations included the following: air temperature and humidity (Ta_12 m, Ta_12.5 m; RH_12 m, RH_12.5 m) (℃ and %, respectively), wind speed (Ws_14 m) (m/s), wind direction (WD_14 m) (°) , 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), water temperature (Tw_20cm、Tw_50cm、Tw_100cm、Tw_200cm、Tw_300cm) (℃). 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 other data in addition to the four-component radiation data during January 1 to October 12 were missing because the malfunction of datalogger. 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: 2018-1-1 10:30. Moreover, suspicious data were marked in red.
2019-09-15 0 10 View Details
This data set comprises the observed runoff data of the glacial hydrological stations in the Namco Basin in Tibet from 2006 to 2008. It contains monthly mean runoff data from four regions: the Niyaqu river, Qugaqie river, Zhadang river, and Angqu river. The data were used to study the regional hydrology and water resources. Measurement instrument: propeller flow velocity meter (LS1206B), Hobo water level meter. Spatial location: Niyaqu, East Namco (the road near the lake outlet): 90.2969E, 31.0342N, elevation: 4730 m; Qugaqie, South Namco (road into the lake outlet): 90.6361E, 30.8175N, elevation: 4780 m; End of the Zhadang Glacier: 90.7261E, 30.6878N, elevation: 5400 m; Angqu (bridge near Deqing Town): 90.2839E, 30.6525N, elevation: 4780 m.
2019-09-14 0 11 View Details