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

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

Number of Datasets: 1202

  • Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

    Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

    The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.

    2022-05-17 4016 57

  • Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, Row number: 308 *166,each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.

    2022-05-05 15196 71

  • The spatial dataset of climate on the Tibetan Plateau (1961-2020)

    The spatial dataset of climate on the Tibetan Plateau (1961-2020)

    The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00,t1960-90_2.e00,t1960-90_3.e00,t1960-90_4.e00,t1960-90_5.e00, t1960-90_6.e00,t1960-90_7.e00,t1960-90_8.e00,t1960-90_9.e00,t1960-90_10.e00, t1960-90_11.e00,t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00,t1991-20_2.e00,t1991-20_3.e00,t1991-20_4.e00,t1991-20_5.e00, t1991-20_6.e00,t1991-20_7.e00,t1991-20_8.e00,t1991-20_9.e00,t1991-20_10.e00, t1991-20_11.e00,t1991-20_12.e00, Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00,p1960-90_2.e00,p1960-90_3.e00,p1960-90_4.e00,p1960-90_5.e00, p1960-90_6.e00,p1960-90_7.e00,p1960-90_8.e00,p1960-90_9.e00,p1960-90_10.e00, p1960-90_11.e00,p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00,p1991-20_2.e00,p1991-20_3.e00,p1991-20_4.e00,p1991-20_5.e00, p1991-20_6.e00,p1991-20_7.e00,p1991-20_8.e00,p1991-20_9.e00,p1991-20_10.e00, p1991-20_11.e00,p1991-20_12.e00, The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.

    2022-05-05 5880 559

  • Frozen land temperature monitoring dataset of  Tibet Plateau Beibeihe meteorological station (2017-2018)

    Frozen land temperature monitoring dataset of Tibet Plateau Beibeihe meteorological station (2017-2018)

    Frozen soil refers to a soil or rock mass with a temperature lower than or equal to 0 ° C and containing ice. It is particularly sensitive to temperature and its physical and mechanical properties change significantly with temperature. The frost heaving deformation and melt settlement deformation of frozen soil are the most common frozen soil disasters. Their occurrence is mainly caused by the change of the inherent temperature of frozen soil due to the frozen soil engineering activities. Therefore, the protection of frozen soil is mainly to protect the temperature of frozen soil. , to maintain it in the closest state before the engineering activities. The main method for obtaining the temperature of the frozen land is to embed the temperature measuring cable. Through the data acquisition function of the CR3000, the resistance value of the temperature measuring cable is obtained at different times, and the temperature value is calculated by the correspondence between the calibration coefficient and the resistance value. According to the sensitive characteristics of frozen soil to temperature, the change of ground temperature can reflect the change of climate, and can also analyze the influence mechanism and degree of human activities on the stability of frozen soil in combination with other factors, so as to guide the later engineering activities. Upgrading and upgrading of frozen soil protection measures.

    2022-04-29 4164 325

  • Temperature and precipitation data at meteorological stations in five Central Asian countries (1980-2015)

    Temperature and precipitation data at meteorological stations in five Central Asian countries (1980-2015)

    The data set covers 599 meteorological stations in five Central Asian countries, including the following elements: * daily maximum temperature, * daily minimum temperature, * observed temperature, * Precipitation (i.e. rain, melting snow), covering the following dates: 1980-1986; 1996-2005; 2010; 2014; 2015 The data comes from ghcn-d, a data set containing global land area daily observation data, which integrates climate records. The data is a direct measurement of surface temperature, without interpolation or model assumptions, and contains many long-term site records. The disadvantage is uneven space coverage. Due to changes in observation time, site location, and the type of thermometer used, the records contain many heterogeneity. For more information about this dataset, see https://www.ncdc.noaa.gov/ghcnd-data-access

    2022-04-26 6638 248

  • The NPP spatio-temporal dataset of the Tibetan Plateau (1982-2006)

    The NPP spatio-temporal dataset of the Tibetan Plateau (1982-2006)

    This data set contains the results of the calculation of Net Primary Productivity (NPP) on the Tibetan Plateau based on ecological models and remote sensing data from 1982 to 2006. Ecosystem NPP of the Tibetan Plateau was generated based on the remote sensing Advanced Very High Resolution Radiometer (AVHRR) data and the Carnegie-Ames-Stanford Approach (CASA) model(1982-2006), the soil carbon content was generated based on the second soil census data, and the biomass carbon data were generated based on the High Resolution Biosphere Model (HRBM) model. Forest ecosystem NPP of the Tibetan Plateau (1982-2006): npp_forest82.e00,npp_forest83.e00,npp_forest84.e00,npp_forest85.e00,npp_forest86.e00, npp_forest87.e00,npp_forest88.e00,npp_forest89.e00,npp_forest90.e00,npp_forest91.e00, npp_forest92.e00,npp_forest93.e00,npp_forest94.e00,npp_forest95.e00,npp_forest96.e00, npp_forest97.e00,npp_forest98.e00,npp_forest99.e00,npp_forest00.e00,npp_forest01.e00, npp_forest02.e00,npp_forest03.e00,npp_forest04.e00,npp_forest05.e00,npp_forest06.e00 Grassland ecosystem NPP of the Tibetan Plateau(1982-2006): npp_grass82.e00,npp_grass83.e00,npp_grass84.e00,npp_grass85.e00,npp_grass86.e00, npp_grass87.e00,npp_grass88.e00,npp_grass89.e00,npp_grass90.e00,npp_grass91.e00, npp_grass92.e00,npp_grass93.e00,npp_grass94.e00,npp_grass95.e00,npp_grass96.e00, npp_grass97.e00,npp_grass98.e00,npp_grass99.e00,npp_grass00.e00,npp_grass01.e00,npp_grass02.e00,npp_grass03.e00,npp_grass04.e00,npp_grass05.e00,npp_grass06.e00. Biomass carbon and soil carbon of the Tibetan Plateau: Biomass.e00,Socd.e00. The soil carbon content data (Socd) are generated based on data of the second soil census of China and Soil Map of China (1:1,000,000) by soil subclass interpolation. The NPP data are generated from the CASA model and AVHRR data simulation: Potter CS, Randerson JT, Field CB et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811–841. The biomass carbon data are generated via HRBM model simulation: McGuire AD, Sitch S, et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochem. Cycles, 2001, 15 (1), 183-206. The raw data are mainly remote sensing data and field observation data with high accuracy; the verification and adjustment of the measured data in the field during the production were undertaken to maintain the error of the simulation results and the field measured data within the acceptable range as much as possible; the verification results of the NPP data and the field measured data show that the error remains within 15%. The spatial resolution is 0.05°×0.05° (longitude×latitude).

    2022-04-21 3732 299

  • Ice core δ18O and accumulations dataset (1900-2011)

    Ice core δ18O and accumulations dataset (1900-2011)

    Among many indicators reflecting changes in climate and environment, the stable isotope index of ice core is an indispensable parameter in ice core record research, and it is one of the most reliable means and the most effective way to restore past climate change. Meanwhile, ice core accumulation is a direct record of precipitation on the glacier, and high-resolution ice core records ensure continuity of precipitation records. Therefore, ice core records provide an effective means of restoring changes in precipitation. Stable isotopes from ice cores drilled throughout the TP have been used to reconstruct climate histories extending back several thousands of years. This dataset provides data support for studying climate change on the Tibetan Plateau.

    2022-04-19 1778 148

  • Land use and land cover in Central Asia (1990-2015)

    Land use and land cover in Central Asia (1990-2015)

    The data defines LC classes using a set of classifiers. The system was designed as a hierarchical classification, which allows adjusting the thematic detail of the legend to the amount of information available to describe each LC class, whilst following a standardized classification approach. As the CCI-LC maps are designed to be globally consistent, their legend is determined by the level of information that is available and that makes sense at the scale of the entire world. The “level 1” legend – also called “global” legend – presented in Table 3-1 meets this requirement. This legend counts 22 classes and each class is associated with a ten values code (i.e. class codes of 10, 20, 30, etc.). The CCI-LC maps are also described by a more detailed legend, called “level 2” or “regional”. This level 2 legend makes use of more accurate and regional information – where available – to define more LCCS classifiers and so to reach a higher level of detail in the legend. This regional legend has therefore more classes which are listed in Appendix 1. The regional classes are associated with nonten values (i.e. class codes such as 11, 12, etc.). They are not present all over the world since they were not properly discriminated at the global scale.

    2022-04-19 2837 146

  • Long-term sequence dataset of lake area on the Tibetan Plateau (1970-2013)

    Long-term sequence dataset of lake area on the Tibetan Plateau (1970-2013)

    The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.

    2022-04-19 15845 925

  • Land use data set in Central Asia l(1970, 2005, 2015)

    Land use data set in Central Asia l(1970, 2005, 2015)

    In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.

    2022-04-19 2576 62

  • High-resolution extreme climate change dataset (air temperature and precipitation) during 10 years under 1.5-2.0℃ global warming

    High-resolution extreme climate change dataset (air temperature and precipitation) during 10 years under 1.5-2.0℃ global warming

    This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.

    2022-04-19 2093 1

  • Global Historical Tide Gauge Dataset (1913-2017)

    Global Historical Tide Gauge Dataset (1913-2017)

    The UHSLC offers tide gauge data with two levels of quality-control (QC). Fast Delivery (FD) data are released within 1-2 months of data collection and receive only basic QC focused on large level shifts and obvious outliers. The GLOSS/CLIVAR (formerly known as the WOCE) "fast" sea level data is distributed as hourly, daily, and monthly values. This project is supported by the NOAA Climate and Global Change program, and is one of the activities of the University of Hawaii Sea Level Center. Each file is given a name "h###.dat" where "h" denotes hourly sea level data and "###" denotes the station number. A file exists for every station with hourly data. The UHSLC datasets are GLOSS data streams (read more here). There are many tide gauge records in the UHSLC database, but the backbone is the GLOSS Core Network (GCN) – a global set of ~300 tide gauge stations that serve as the foundation of the global in situ sea level network. The network is designed to provide evenly distributed sampling of global coastal sea level variation at a variety of time-scales.

    2022-04-19 5270 558

  • Data of annual lake area in the endorheic basin of the Qinghai-Tibet Plateau from 1986 to 2019

    Data of annual lake area in the endorheic basin of the Qinghai-Tibet Plateau from 1986 to 2019

    This data provides the annual lake area of ​​582 lakes with an area greater than 1 km2 in the enorheic basin of the Qinghai-Tibet Plateau from 1986 to 2019. First, based on JRC and SRTM DEM data, 582 lakes are identified in the area that are larger than 1 km2. All Landsat 5/7/8 remote sensing images covering a lake are used to make annual composite images. NDWI index and Ostu algorithm were used to dynamically segment lakes, and the size of each lake from 1986 to 2019 is then calculated. This study is based on the Landsat satellite remote sensing images, and using Google Earth Engine allowed us to process all Landsat images available to create the most complete annual lake area data set of more than 1 km2 in the Qinghai-Tibet Plateau area; A set of lake area automatic extraction algorithms were developed to calculate of the area of ​​a lake for many years; This data is of great significance for the analysis of lake area dynamics and water balance in the Qinghai-Tibet Plateau region, as well as the study of the climate change of the Qinghai-Tibet Plateau lake.

    2022-04-19 4145 1

  • Data on natural disasters in 65 countries along the along the Belt and Road (1900-2018)

    Data on natural disasters in 65 countries along the along the Belt and Road (1900-2018)

    "Disaster data for countries along the belt and road, mainly from the global disaster database.The records information of disaster database are from the United Nations, government and non-governmental organizations, research institutions and the media. It's documented in detail such as the country where the disaster occurred, the type of disaster, the date of the disaster, the number of deaths and the estimated economic losses. This study extracts the natural disaster records of the countries along the One Belt And One Road line one by one from the database, and finally forms the disaster database of 9 major disasters of the 65 countries. The natural disaster records collected can be roughly divided into nine categories, including: floods, landslides, extreme temperatures, storms, droughts, forest fires, earthquakes, mass movements and volcanic activities. From 1900 to 2018, a total of 5,479 disaster records were recorded in countries along the One Belt And One Road. From 2000 to 2015, there were 2,673 disaster records. On this basis, the natural disasters of the countries along the belt and road are investigated from four aspects, including disaster frequency, death toll, disaster-affected population and economic loss assessment. Overall, since 1900, a total of 5479 natural disasters have occurred in countries along the One Belt And One Road, resulting in about 19 million deaths and economic losses of about 950 billion us dollars. Among them, the most frequent occurrence is flood and storm; the biggest economic losses are floods and earthquakes; the most affected people are flood and drought; drought and flooding are the leading causes of death

    2022-04-19 1474 191

  • Temporal and spatial matching pattern data and maps of water and soil resources on Tibetan Plateau (resolution 1km) (2008-2015)

    Temporal and spatial matching pattern data and maps of water and soil resources on Tibetan Plateau (resolution 1km) (2008-2015)

    The Tibetan Plateau in China covers six provinces including Tibet, Qinghai, Xinjiang, Yunnan, Gansu and Sichuan, including Tibet and Qinghai, as well as parts of Xinjiang, Yunnan, Gansu and Sichuan. The research on water and soil resources matching aims to reveal the equilibrium and abundance of water resources and land resources in a certain regional scale. The higher the level of consistency between regional water resources and the allocation of cultivated land resources, the higher the matching degree, and the superior the basic conditions of agricultural production. The general agricultural water resource measurement method based on the unit area of cultivated land is used to reflect the quantitative relationship between the water supply of agricultural production in the study area and the spatial suitability of cultivated land resources. The Excel file of the data set contains the generalized agricultural soil and water resource matching coefficient data of the Tibetan Plateau municipal administrative region in China from 2008 to 2015, the vector data is the boundary data of the Tibetan Plateau municipal administrative region in China in 2004, and the raster data pixel value is the generalized agricultural soil and water resource matching coefficient of the year in the region.

    2022-04-19 2939 153

  • FVC dataset of remote sensing for ecological assets assessment in Qinghai-Tibet Plateau

    FVC dataset of remote sensing for ecological assets assessment in Qinghai-Tibet Plateau

    The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. Based on pixel dichotomy model, the vegetation coverage model is developed by using multi-scale remote sensing images, combining with high precision remote sensing parameters such as vegetation community type and distribution characteristics, and the mixed pixel decomposition method is used to construct the vegetation coverage model. All data could be used only after the permission of the data distributor.

    2022-04-19 2989 211

  • The sequence data of livestock number at county level on the Tibetan Plateau (1970-2006)

    The sequence data of livestock number at county level on the Tibetan Plateau (1970-2006)

    This data set contains sequence data of the number variation of livestock in the major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study the social and economic changes of the Tibetan Plateau. The table has ten fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Large livestock (10,000) Interpretation: The number of large livestock such as cattle, horses, mules, donkeys, and camels. Field 6: Cattle herd (10,000) Interpretation: Number of cattle Field 7: Equine animals(10,000) Interpretation: The number of equine animals such as horses, mules and donkeys. Field 8: Horses (10,000) Interpretation: The number of horses Field 9: Sheep (10,000) Interpretation: The number of sheep Field 10: Data Sources Interpretation: Source of Data The data come from the statistical yearbook and county annals. Some are listed as follows. [1] Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009 [2] Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009 [3] Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009 [4] Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009 [5] Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009 [6] Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008. [7] County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993 [8] Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994 [9] Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993 [10] Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996 [11] Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995 [12] Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003 [13] Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993 [14] Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005 [15] Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004 [16] Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [17] Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006 [18] Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996 [19] County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993 [20] Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995 [21] Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. 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  • Dataset of Soil  Erosion (water) Intensity with 300m resoluton in Tibetan Plateau (1992, 2005, 2015)

    Dataset of Soil Erosion (water) Intensity with 300m resoluton in Tibetan Plateau (1992, 2005, 2015)

    1)The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015, and the grid resolution is 300m. 2) China soil erosion prediction model (CSLE) was used to calculate the soil erosion amount of more than 4,000 investigation units on the Qinghai-Tibet Plateau. Soil erosion was interpolated according to land use on Qinghai-Tibet Plateau. According to the soil erosion classification standard, the soil erosion intensity map of Qinghai-Tibet Plateau was obtained. 3) By comparing the differences of three-stage soil erosion intensity data, it conforms to the actual change law and the data quality is good. 4) The data of soil erosion intensity are of great significance to the study of soil erosion in the Qinghai-Tibet Plateau and the sustainable development of local ecosystems. In the attribute table, "Value" represents the erosion intensity level, from 1 to 6, the value represents slight, mild, moderate, intense, extremely intense and severe. "BL" represents the percentage of echa erosion intensity in the total area.

    2022-04-19 4732 283

  • The map of fractional vegetation cover in the Yellow River source region of Tibet Plateau (2015)

    The map of fractional vegetation cover in the Yellow River source region of Tibet Plateau (2015)

    This dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.

    2022-04-19 4010 127

  • Data of soil organic matter in Qinghai-Tibet Plateau (1979-1985)

    Data of soil organic matter in Qinghai-Tibet Plateau (1979-1985)

    The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau

    2022-04-19 3014 87