Brief Introduction: Pan-third-polar environmental change and green silk road construction
Number of Datasets: 652
This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
2020-03-13 731 9 View Details
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.
2019-09-15 373 17 View Details
Soil data is important both on a global scale and on a local scale, and due to the lack of reliable soil data, land degradation assessments, environmental impact studies, and sustainable land management interventions have received significant bottlenecks . Affected by the urgent need for soil information data around the world, especially in the context of the Climate Change Convention, the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO) and the Kyoto Protocol for Soil Carbon Measurement and FAO/International The Global Agroecological Assessment Study (GAEZ v3.0) jointly established the Harmonized World Soil Database version 1.2 (HWSD V1.2). Among them, the data source in China is the second national land in 1995. Investigate 1:1,000,000 soil data provided by Nanjing Soil. The resolution is 30 seconds (about 0.083 degrees, 1km). The soil classification system used is mainly FAO-90. The core soil system unit unique verification identifier: MU_GLOBAL-HWSD database soil mapping unit identifier, connected to the GIS layer. MU_SOURCE1 and MU_SOURCE2 source database drawing unit identifiers SEQ-soil unit sequence in the composition of the soil mapping unit; The soil classification system utilizes the FAO-7 classification system or the FAO-90 classification system (SU_SYM74 resp. SU_SYM90) or FAO-85 (SU_SYM85). The main fields of the soil property sheet include: ID (database ID) MU_GLOBAL (Soil Unit Identifier) (Global) SU_SYMBOL soil drawing unit SU_SYM74 (FAO74 classification); SU_SYM85 (FAO85 classification); SU_SYM90 (name of soil in the FAO90 soil classification system); SU_CODE soil charting unit code SU_CODE74 soil unit name SU_CODE85 soil unit name SU_CODE90 soil unit name DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS(19.5); AWC_CLASS (effective soil water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification to the bottom of the soil); SWR: String (soil moisture content); ADD_PROP: Real (specific soil type in the soil unit related to agricultural use); T_TEXTURE (top soil texture); T_GRAVEL: Real (top gravel volume percentage); (unit: %vol.) T_SAND: Real (top sand content); (unit: % wt.) T_SILT: Real (surface layer sand content); (unit: % wt.) T_CLAY: Real (top clay content); (unit: % wt.) T_USDA_TEX: Real (top layer USDA soil texture classification); (unit: name) T_REF_BULK: Real (top soil bulk density); (unit: kg/dm3.) T_OC: Real (top organic carbon content); (unit: % weight) T_PH_H2O: Real (top pH) (unit: -log(H+)) T_CEC_CLAY: Real (cation exchange capacity of the top adhesive layer soil); (unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of top soil) (unit: cmol/kg) T_BS: Real (top level basic saturation); (unit: %) T_TEB: Real (top exchangeable base); (unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top sulfate content); (unit: % weight) T_ESP: Real (top exchangeable sodium salt); (unit: %) T_ECE: Real (top conductivity). (Unit: dS/m) S_GRAVEL: Real (bottom crushed stone volume percentage); (unit: %vol.) S_SAND: Real (bottom sand content); (unit: % wt.) S_SILT: Real (bottom sludge content); (unit: % wt.) S_CLAY: Real (bottom clay content); (unit: % wt.) S_USDA_TEX: Real (bottom USDA soil texture classification); (unit: name) S_REF_BULK: Real (bottom soil bulk density); (unit: kg/dm3.) S_OC: Real (underlying organic carbon content); (unit: % weight) S_PH_H2O: Real (bottom pH) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying adhesive layer soil); (unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of the bottom soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation); (unit: %) S_TEB: Real (underlying exchangeable base); (unit: cmol/kg) S_CACO3: Real (bottom carbonate or lime content) (unit: % weight) S_CASO4: Real (bottom sulfate content); (unit: % weight) S_ESP: Real (underlying exchangeable sodium salt); (unit: %) S_ECE: Real (underlying conductivity). (Unit: dS/m) The database is divided into two layers, with the top layer (T) soil thickness (0-30 cm) and the bottom layer (S) soil thickness (30-100 cm). For other attribute values, please refer to the HWSD1.2_documentation documentation.pdf, The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description and HWSD.mdb.
2019-09-13 104 7 View Details
This data set contains oxygen isotope data from 1010 to 2005. It is used to study environmental changes in the Xixiabangma area of the Tibetan Plateau. The ice core oxygen isotope is measured by instrument. This data set is obtained from laboratory measurements. The data are obtained immediately after the completion of the instrument or experiment. The samples and data are collected in strict accordance with relevant operating procedures at all stages and comply with the laboratory operating standards. This data contains two fields: Field 1: The time AD. Field 2: The oxygen isotope ‰.
2019-09-12 380 6 View Details
The data set records the merchandise exports and imports of 1960-2017 countries along 65 countries along the belt and road. Merchandise imports show the c.i.f. value of goods received from the rest of the world valued in current U.S. dollars.Merchandise exports show the f.o.b. value of goods offered to the rest of the world valued in current U.S. dollars.Data sources:The data on total imports of goods (merchandise) are from the World Trade Organization (WTO), which obtains data from national statistical offices and the IMF's International Financial Statistics, supplemented by the Comtrade database and publications or databases of regional organizations, specialized agencies, economic groups, and private sources (such as Eurostat, the Food and Agriculture Organization, and country reports of the Economist Intelligence Unit). The data set contains 2 tables: Merchandise imports (current US$),Merchandise exports (current US$).
2019-09-12 332 1 View Details
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
2019-09-15 757 15 View Details
China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.
2019-09-11 56291 618 View Details
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. 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.
2019-11-19 263 4 View Details
Monthly meteorological data of Sanjiangyuan includes 32 national standard meteorological stations. There are 26 variables: average local pressure, extreme maximum local pressure, date of extreme maximum local pressure, extreme minimum local pressure, date of extreme minimum local pressure, average temperature, extreme maximum temperature, date of extreme maximum temperature, extreme minimum temperature and date of extreme minimum temperature, average temperature anomaly, average maximum temperature, average minimum temperature, sunshine hours, percentage of sunshine, average relative humidity, minimum relative humidity, date of occurrence of minimum relative humidity, precipitation, days of daily precipitation >=0.1mm, maximum daily precipitation, date of maximum daily precipitation, percentage of precipitation anomaly, average wind speed, maximum wind speed, date of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed and occurrence date of maximum wind speed. The data format is txt, named by the site ID, and each file has 26 columns. The names and units of each column are explained in the SURF_CLI_CHN_MUL_MON_readme.txt file. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters
2019-09-15 615 33 View Details
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 1340 87 View Details
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.
2019-11-18 434 4 View Details
This data set collected the age data of ophiolite along the Bangong-Nujiang on the Tibetan Plateau in the literature published before October,2014. The data were analyzed by Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICPMS), Sensitive High-Resolution Ion Micro Probe (SHRIMP), Isotope Dilution Thermal Ionization Mass spectrometry (ID TIMS), Secondary Ion Mass spectrometer (SIMS), etc. The data were obtained according to laboratory standards and the quality meets laboratory requirements. Data fields: Area Location Rock type Sample No. Target mineral" Method Age Reference Interpretation Notes
2019-09-12 348 2 View Details
Based on the Tibetan Plateau wetland pattern in the 1970s interpreted using the Mire Map of China compiled by the scientific expeditions and the Tibetan Plateau wetland pattern in the 2000s interpreted using Landsat TM (resolution: 30 m) satellite image data, The Mire Map of China in the 1970s was interpreted. Visual interpretation of Landsat TM images from 2006 to 2009: a) Based on the natural zoning of the whole district, the interpretation keys of different wetland types were established with reference to the data obtained by different physical geography units and actual surveys. b) Based on the established interpretation keys, wetlands with an area greater than 10 square kilometers were primarily extracted by artificial visual interpretation method (excluding permanent, seasonal rivers and riverbeds). c) According to the interpretation results in combination with the topographic map (resolution: 90 m) of the study area and the actual situation of the wetland plaque investigation within the study area, the plaque modification and supplementation were artificially carried out. The data of the 1970s were obtained by interpretation of the Mire Map of China compiled by the Tibetan Plateau scientific expeditions of the Changchun Institute of Geography. The wetland data of the 2000s was derived from Landsat TM (resolution: 30 m) satellite image data. The data are of good quality.
2019-09-14 368 19 View Details
The Tibetan plateau (TP), called as “the third pole of the earth” is the water tower of Asia not only feed tens of millions of people, but also maintain fragile ecosystems in arid region of northwestern China. Temporal-spatially complete representations of land surface temperature are required for many purposes in environmental science, especially in third pole where the traditional ground measurement is difficult and therefore the data is sparse. The cloud-free datasets of daily mean land surface temperature (LST) and mean annual land surface temperature (MAST) during 2004 to 2016 were released and derived from the quartic daily MODIS (the Moderate Resolution Imaging Spectroradiometer) Terra/Aqua LST products with a resolution of 1 km using a pragmatic data processing algorithm (Ran et al., 2015; 2017a). The comparison between radiance-based LST measurement and the estimated LST shows good agreement in the daily and inter-annual variability, with a correlation of 0.95 and 0.99 and bias of -1.73℃ (±3.38℃) and -2.07℃ (±1.05℃) for daily-mean-LST and MAST, respectively (Ran et al., 2017c). The systematic error is mainly source from the defined of daily mean LST, which is represented by the arithmetic average of the daytime and nighttime LSTs. The random error is mainly source from the uncertainty of the original MODIS LST values, especially for the daytime LST products. Trend validation using air temperatures from 94 weather stations indicate that the warming trends derived from time series MAST data is comparable with that derived from CMA data. The dataset is potential useful for various studies, including climatology, hydrology, meteorology, ecology, agriculture, public health, and environmental monitoring in the third pole and around regions.
2019-09-12 767 3 View Details
The data set contains land cover data sets from the Yellow River Source, the Yangtze River Source, and the Lancang River from 1992 to 2015. A total of 22 land cover classifications based on the UN Land Cover Classification System were included. NOAA AVHRR, SPOT, ENVISAT, PROBA-V and other vegetation classification products were integrated. In China, (1) first, combined with the 1:100,000 vegetation classification (2007) of China, quality correction and control were performed, and (2) the vegetation classification of China emphasized the combination with climate zones, when correcting CCI-LC, climate divisions and the corresponding vegetation types were combined, and the data label was comprehensively revised.
2019-09-13 926 28 View Details
Based on the average NDVI (spatial resolution 250m) of MODIS during the growing season from 2000 to 2018, the trend of NDVI was calculated by using Mann-Kendall trend detection method. Three parks of Three River Source National Park are calculated (CJYQ: Yangtze River Park; HHYYQ: Yellow River Park; LCJYQ: Lancang River Park). CJYQ_NDVI_trend_2000_2018_ok.tif: Changjiang Source Park NDVI trend. CJYQ_NDVI_trend_2000_2018_ok_significant.tif: Changjiang Source Park NDVI change trend, excluding the area that is not significant (p > 0.05). CJYYQ_gs_avg_NDVI_2000.tif: The average NDVI of the Yangtze River Source Park in 2000 growing season. Unit NDVI changes every year.
2019-05-28 853 36 View Details
Based on our field works in the Qinghai-Tibet Plateau (QTP) from 2008 to 2018, and combining data from previous studies, we obtained genetic data of all widely distributed amphibian and reptile species in QTP. Meanwhile, our data covered the whole ranges of all the species. To answer the question: ‘How climatic changes influenced animals of QTP?’, we rebuild the demographic history and analyzed how Quaternary climatic changes impacted animals. Then, we identified the locations of refugia. After constructing spatial pattern of genetic diversity, we identified genetic hotspots which needs more conservation effects. These results are important in biodiversity conservation in QTP.
2019-11-05 44 0 View Details
The data set contains three tables: the crop yield in each county, the per capita production of industrial and agricultural products, and the main crop yield. It recorded the sequence data of major crops, grain, oilseed, fruits, meat and eggs, major industrial products, aluminum, crude oil, steel, cement and power generation in Qinghai from 1978 to 2015. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. Table 1: The table of statistics of enterprises in each county contains 5 fields. Field 1: Districts and counties Field 2: Year Field 3: The number of industrial enterprises above state designated scale Field 4: Total output value of industrial enterprises above state designated scale Field 5: Urban completed investment in fixed assets Unit: 10 thousand yuan Table 2: The table of overview of the foreign investment use contains 4 fields. Field 1: Year Interpretation: year of the data Field 2: Foreign investment project Interpretation: The number of foreign investment projects Field 3: Foreign investment amount Interpretation: Foreign investment amount Unit： 10,000 dollars Field 4: Actually used amount Interpretation: Actually used foreign investment amount Unit： 10,000 dollars
2019-04-20 341 4 View Details
The data set contains data on workers in primary, secondary, and tertiary industries in Qinghai over time. The data were derived from the Qinghai Society and Economics Statistical Yearbook and Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. The table contains 8 fields. Field 1: Year of the data Field 2: The total number of workers, unit: 10,000 Field 3: Number of workers in primary industries, unit: 10,000 Field 4: Number of workers in secondary industries, unit: 10,000 Field 5: Number of workers in tertiary industries, unit: 10,000 Field 6: Proportion of the primary industry workers, unit: % Field 7: Proportion of the secondary industry workers, unit: % Field 8: Proportion of the tertiary industry workers, unit: %
2019-09-15 352 5 View Details
This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the observation system of Meteorological elements gradient of Huazhaizi desert steppe station from January 1 to December 31, 2018. The site (100.3201°E, 38.7659°N) was located on a desert steppe surface in the Huazhaizi, which is near Zhangye city, Gansu Province. The elevation is 1731 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 and 10 m, north), wind speed and direction profile (windsonic; 5 and 10 m, north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), four-component radiometer (CNR1; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0 m), soil moisture profile (ML3; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0 m). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m; RH_5 m and RH_10 m) (℃ and %, respectively), wind speed (Ws_5 m and Ws_10 m) (m/s), wind direction (WD_5 m and 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_60 cm, Ts_100 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, Ms_100 cm) (%). 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 malfunction of soil moisture sensor, data during 1.1-1.7, 8.22-8.31, and 9.4-9.12 were missing; (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.
2019-09-15 692 45 View Details