Brief Introduction: 泛第三极是从第三极向西、向北扩展,涵盖青藏高原、帕米尔、兴都库什、伊朗高原、高加索、喀尔巴阡等山脉的欧亚高地及其环境影响区,面积2000多万平方公里,和30多亿人的生存环境有关。泛第三极地区与“一带一路”核心区高度重合。深入研究泛第三极地区环境变化规律、机制与未来变化趋势,解决重点地区、重点国家和重点工程的资源环境问题,将为环境变化和人类活动最强烈的丝绸之路经济带可持续发展提供科学依据,为打造绿色、健康、智力、和平的“一带一路”提供决策支持。 中国科学院A类战略性先导科技专项“泛第三极环境变化与绿色丝绸之路建设”(以下简称“丝路环境专项”)于9月30日在北京。本专项将遵循习近平总书记对第二次青藏高原综合科学考察研究的重要指示精神和新时代青藏高原生态文明建设理念的系列重要讲话指示精神,与第二次青藏高原综合科学考察研究和三极环境与气候变化国际大科学计划有机结合,聚焦水、生态、人类活动,着力解决环境变化机理、资源环境承载力、灾害风险、绿色发展途径等方面的问题。围绕专项的两大统领科学问题,在科学贡献层面,预期在泛第三极环境变化与西风-季风相互作用和水资源变化及广域联动、泛第三极环境变化对关键物种和典型生态系统影响的预警体系与适应模式、人类文明发展与泛第三极环境相互作用及其适应策略等方面产出重大成果,推动从高极到三极的全球环境研究新前沿和三极环境与气候变化国际大科学计划的实施;在国家需求层面,预期在绿色丝绸之路建设的路线图、绿色丝绸之路建设的技术示范、优化青藏高原生态安全屏障体系的科学方案等方面产出重大成果,推动青藏高原可持续发展、推进国家生态文明建设、促进全球生态环境保护。

Number of Datasets: 534

  • Precipitation observation data on Taerma Township (2016-2017)

    This is the precipitation observation data of the observation point in Taerma Township. It can be used in Glaciology, Climatology, Environmental Change, Hydrologic Process in Cold Regions and other disciplinary areas. The data is observed from September 15, 2016 to August 17, 2017. It is measured by automatic rain gauge and a piece of data is recorded every 60 minutes. 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.

    2019-09-15 0 1 View Details

  • Time-lapse observation dataset of soil temperature and humidity on the Tibetan Plateau (2008-2016)

    This data set comprises the plateau soil moisture and soil temperature observational data based on the Tibetan Plateau, and it is used to quantify the uncertainty of model products of coarse-resolution satellites, soil moisture and soil temperature. The observation data of soil temperature and moisture on the Tibetan Plateau (Tibet-Obs) are from in situ reference networks at four regional scales, which are the Nagqu network of cold and semiarid climate, the Maqu network of cold and humid climate, and the Ali network of cold and arid climate,and Pali network. These networks provided representative coverage of different climates and surface hydrometeorological conditions on the Tibetan Plateau. - Temporal resolution: 1hour - Spatial resolution: point measurement - Measurement accuracy: soil moisture, 0.00001; soil temperature, 0.1 °C; data set size: soil moisture and temperature measurements at nominal depths of 5, 10, 20, 40 - Unit: soil moisture, cm ^ 3 cm ^ -3; soil temperature, °C

    2019-09-15 0 19 View Details

  • Data on rural electronic power, irrigated area, and fertilizer application in the Tibetan Autonomous Region (1992-2016)

    The data set contains time series data on rural electronic power, irrigated area, and fertilizer application in Tibet from 1992 to 2016. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. The table contains 7 fields. Field 1: Districts and counties Field 2: Year Field 3: Number of township hydropower stations Field 4: Capacity of township hydropower stations, unit: 10,000 kilowatt Field 5: Electricity consumption in rural areas, unit: 10,000 kW/h Field 6: Fertilizer application amount, unit: ton Field 7: Effective irrigation area, unit: 1000 hectares

    2019-09-15 0 5 View Details

  • A new map of permafrost distribution on the Tibetan Plateau (2017)

    The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs)、The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution , validated this map using various ground-based data sets. The properties of frozen soil include: Seasonally frozen ground、Permafrost、Unfrozen ground. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

    2019-09-15 0 36 View Details

  • Remote sensing monitoring dataset of land use status in six provinces in western China for many years (1970s, 1980s, 1995, 2000, 2005, 2010, 2015)

    The remote sensing monitoring database of land use status in China is a multi-temporal land use status database covering the land area of China, which has been established after many years of accumulation under the support of the National Science and Technology Support Plan and the Key Direction Project of the Knowledge Innovation Project of the Chinese Academy of Sciences. It is the most accurate remote sensing monitoring data product of land use in China at present, which has played an important role in the national land resources survey, hydrology and ecological research.   This data set covers the six western provinces in China: Xinjiang, Tibet, Qinghai, Yunnan, Sichuan and Gansu. Based on Landsat TM/ETM remote sensing images in the late 1970s、1980s、1995、2000、2005、2010、2015, 1KM raster data are generated by using the professional software and manual visual interpretation on the basis of vector data.   The land use types include six primary land types which are cultivated land, forest land, grassland, water area, residential land and unused land, and 25 secondary types.

    2019-09-15 0 37 View Details

  • Water quality dataset of Gaacuo Lake (2017)

    This is the water quality data of the vertical profile of the observation point in Gaacuo Lake. The data is observed on July1, 2017. The data is stored as an excel file.

    2019-09-15 0 5 View Details

  • Observational farmland ecosystem data in Lhasa on the Tibetan Plateau (2006-2009)

    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

  • Main per capita social and economic indicators in the Tibetan Autonomous Region (1965-2016)

    The data set describes the per capita main economic indicators in Tibet from 1965 to 2016, including time series data on the regional GDP, gross agricultural output value, gross industrial output value, grain production, total retail sales of consumer goods, savings account balances, per capita net income of farmers and herdsmen, and per capita net income of employees in the district. The data were derived from the Tibet Society and Economics Statistical Yearbook and Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. Table 1: The table of the main per capita economic indicators contains 9 fields. Field 1: Year of the data Field 2: Regional GDP, unit: yuan Field 3: Gross agricultural output value, unit: yuan Field 4: Gross industrial output, unit: yuan Field 5: Grain production, unit: kilograms Field 6: Total retail sales of consumer goods, unit: yuan Field 7: Savings account balance, unit: yuan Field 8: Per capita net income of farmers and herdsmen, unit: yuan Field 9: Per capita net income of employees in the district, unit: yuan Table 2: The table of per capita indicators in each county contains 6 fields. Field 1: Districts and counties Field 2: Year of the data Field 3: Per capita grain possession, unit: kilograms Field 4: Per capita GDP, unit: yuan Field 5: Per capita local fiscal revenue, unit: yuan Field 6: Savings account balance of urban and rural residents, unit: 10,000 yuan

    2019-09-15 0 4 View Details

  • Precipitation observation data of the Qiang-Tang Plateau (2017)

    This is the rain gauge precipitation observation data along Bange-Ali line in the Qiang-tang Plateau. It can be used to evaluate and improve the quality of satellite precipitation products, thereby improving the estimation accuracy of precipitation in the Qiangtang Plateau. The data is observed from June 19, 2016 to September 24, 2017. And a piece of data is recorded every 60 minutes. 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.

    2019-09-15 0 5 View Details

  • The monthly mean air temperature in the Qilian Mountains on the Qinghai-Tibetan Plateau (1980-2013)

    This dataset includes the monthly air temperature at 2 m in the Qilian Mountain area on the Qinghai-Tibetan Plateau during 1980 to 2013. The dataset was obtained from the ERA-interim reanalysis product. The ERA-interim system includes a 4-dimensional variational analysis (4D-Var). The quality of the data has been improved using the bias correction of satellite data. The spatial resolution of the dataset is 0.125°. The dataset includes the grid data of the air temperature in the Qilian Mountain area during the past 30 years, and provides a basic data for the studies such as climatic change, ecosystem succession, and earth system models.

    2019-09-15 0 10 View Details

  • Water index in the Qilian Mountain Area (1980-2015)

    This dataset contains the ground surface water (including liquid water, glacier and perennial snow) area in Qilian Mountain Area from 1980 to 2015. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected between 1978 and 2018 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 8 seasons, the dataset has a temporal resolution of 5 years and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the variation of water distribution within the Qilian Mountain in the past 35 years, and can be used for quantitative estimation of water resource.

    2019-09-15 0 9 View Details

  • Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Jingyangling station, 2018)

    This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the Jingyangling station from January 1 to December 31, 2018. The site (101.116° E, 37.838° N) was located on a cold meadow surface in the Jingyangling, Qilian County, Qinghai Province. The elevation is 3750 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (5 m, north), wind speed and direction (10 m, north), air pressure (in the tamper box on the ground), rain gauge (10 m), four-component radiometer (6 m, south), two infrared temperature sensors (6 m, south, vertically downward), soil heat flux (3 duplicates, -0.06 m), soil temperature profile (0, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil moisture profile (-0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 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_0 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), and soil moisture (Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). 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. Due to the snow cover the solar panel causing insufficient power supply, data during December 13-21 were missing; due to the sensor malfunction, there were some NAN invalid values during May 29 to June 22 and July 16 to August 19 of the wind speed and direction; incorrect data of longwave radiation during December 13 to 31; incorrect data of 4 cm depth soil moisture during January 1 to 3 and April 1 to May 20; (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: 2018-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    2019-09-15 0 11 View Details

  • Water quality slope data of Co Ngoin Lake (2017)

    These are the water quality vertical slope data of Co Ngoin Lake obtained during the River and Lake Source Investigation from June to July in 2017. The main water quality observation data include dissolved oxygen, electrical conductivity, PH, water temperature and others.

    2019-09-15 0 3 View Details

  • The observation dataset of the Dangxiong Wetland ecosystem on the Tibetan Plateau (2009-2010)

    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

  • MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)

    Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.

    2019-09-15 0 18 View Details

  • The active layer depth distribution map of the Qinghai-Tibet engineering corridor (1980-2015)

    Based on the existing natural hole data of 15 active layer depth monitoring sites in the Qinghai-Tibet Engineering Corridor, the active layer depth distribution map of the Qinghai-Tibet Engineering Corridor was simulated using the GIPL2.0 frozen soil model. The model required synthesis of a temperature data set of time series. The temperature data were divided into two phases according to the time spans, which were 1980-2009 and 2010-2015. The data of the first phase were from the Chinese meteorological driving data set (http://dam. Itpcas.ac.cn/rs/?q=data#CMFD_0.1), and the data of the second phase was the application of MODIS surface temperature products (MOD11A1/A2 and MYD11A1/A2) with a spatial resolution of 1 km. In addition, the soil type data required by the model came from the China Soil Database (V1.1) and have a resolution of 1 km. At the same time, the topography was also considered. The research area was classified into 88 types based on the measured soil thermophysical parameters and land cover types, and then the simulation was performed. The simulation results were compared with the field measured data. The results showed that they were highly consistent, and the correlation coefficient reached 0.75. In alpine areas, the average depth of the active layer is below 2.0 m. However, in the river valleys, the average depth of the active layer is above 4.0 m. In the high plain area, the depth of the active layer is usually between 3.0 m and 4.0 m.

    2019-09-15 0 10 View Details

  • Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (automatic weather station of Heihe remote sensing station, 2018)

    This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the observation system of Heihe remote sensing station from January 1 to December 31, 2018. The site (100.4756° E, 38.8270° N) was located on artificial grassland in Dangzhai Town of Zhangye, Gansu Province. The elevation is 1560 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (1.5 m, north), wind speed and direction (10 m, north), air pressure (2 m), rain gauge (0.7 m), four-component radiometer (1.5 m, south), two infrared temperature sensors (1.5 m, south, vertically downward), soil heat flux (3 duplicates, -0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6 m), and two photosynthetically active radiation (1.5 m, south, one vertically downward and one vertically upward). The observations included the following: air temperature and humidity (Ta_1.5, RH_1.5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 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_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm) (℃),on the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_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: 2018-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    2019-09-15 0 9 View Details

  • Natural places names dataset at 1:250,000 in Sanjiangyuan Region (2015)

    This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, water system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example NAME Name Ramsay Laboniwa PINYIN Chinese Pinyin Lamusailabaoniwa CLASS Toponymic Classification Code HB

    2019-09-15 0 9 View Details

  • Basic geographic dataset of resources and environment in Central and Western Asia Region

    Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.

    2019-09-15 0 0 View Details

  • Flow discharge observation data of Selincuo Lake’s inflow river - Boquzangbu (2017)

    This is the flow discharge observation data observed in the estuary of Boquzangbu, which is Selincuo Lake’s inflow river. It is measured by Flow Tracker portable hydrological flow rate meter. It can be used in Hydrologic Process in Cold Regions and other disciplinary areas. The data is observed on August 16, 2017. The observation includes time, location, depth of water, water flow rate, and water flow discharge. The data is stored as an excel file.

    2019-09-15 0 0 View Details