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: 1357

  • Human development resilience dataset for countries along the "Belt and Road" (2000-2020)

    Human development resilience dataset for countries along the "Belt and Road" (2000-2020)

    The Human Development Index (HDI) was developed by the United Nations Development Programme (UNDP) in the Human Development Report 1990 to measure the level of economic and social development of the United Nations member countries. The HDI is a composite indicator based on three basic variables: life expectancy, educational attainment and quality of life, and is calculated according to a certain methodology. "The One Belt One Road (OBOR) human development resilience dataset is a comprehensive indicator of human development resilience in each country. "The human development resilience dataset for countries along the Belt and Road is a comprehensive diagnosis based on sensitivity and adaptability analysis using year-by-year data of the Human Development Index for countries along the Belt and Road from 2000 to 2020. The Human Development Resilience Indicator (HDRI) data was prepared based on sensitivity and adaptation analysis. Please refer to the documentation for the methodology of preparing the dataset. "The Human Development Resilience Dataset for countries along the Belt and Road is an important reference for analysing and comparing the current state of human development resilience in each country.

    2022-05-05 227 70

  • 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 15556 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 7299 723

  • Vegetation cover resilience data set for countries along the Belt and Road (2000-2020)

    Vegetation cover resilience data set for countries along the Belt and Road (2000-2020)

    The resilience of vegetation cover in countries along the Belt and Road reflects the level of resilience of vegetation cover in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of vegetation cover in the countries along the Belt and Road. The vegetation cover status resilience data products were prepared with reference to the MODIS MOD13A3 dataset from 2000 to 2020, with a spatial resolution of 1 KM and a temporal resolution of 1 year. Using the year-by-year NDVI data of the countries along the Belt and Road from 2000 to 2020, based on the consideration of year-by-year changes, and through comprehensive diagnosis based on sensitivity and adaptability analysis, the data were prepared The resilience products of vegetation cover status were generated. The data set on the resilience of vegetation cover in countries along the Belt and Road is an important reference for analysing and comparing the current resilience of vegetation cover in each country.

    2022-04-29 199 54

  • A dataset of water supply resilience in countries along the "Belt and Road" (2000-2019)

    A dataset of water supply resilience in countries along the "Belt and Road" (2000-2019)

    The water resource supply resilience of countries along the “Belt and Road” reflects the level of water supply resilience of countries along the route. The higher the data value, the stronger the resilience of water supply in countries along the route. Preparation of data products for water supply resilience of countries along the “Belt and Road”, using the annual precipitation, surface runoff and underground net data produced by FLDAS (Famine Early Warning System Network Land Data Assimilation System) based on the Noah land surface model from 2000 to 2019 The flow simulation data set, on the basis of considering the year-to-year changes, based on sensitivity and adaptability analysis, and through comprehensive diagnosis, prepared and generated water resource supply resilience products. The data set of water supply resilience of countries along the “Belt and Road” has important reference significance for analyzing and comparing the current status of water resources supply resilience in various countries.

    2022-04-29 284 90

  • Ecosystem productivity resilience dataset for countries along the Belt and Road (2000-2015)

    Ecosystem productivity resilience dataset for countries along the Belt and Road (2000-2015)

    Ecosystem productivity resilience along the Belt and Road reflects the level of ecosystem productivity resilience in the countries along the Belt and Road, with higher values indicating stronger ecosystem productivity resilience. The ecosystem productivity resilience data products were prepared with reference to the global medium-resolution vegetation gross primary productivity dataset from 2000 to 2015, with a spatial resolution of 0.05° and a temporal resolution of 1 year, using the year-by-year data of gross primary productivity of vegetation from 2000 to 2015 for the countries along the Belt and Road. The data set was used to generate ecosystem productivity resilience products based on sensitivity and adaptation analyses, using year-by-year data on total primary productivity of vegetation in the countries along the Belt and Road from 2000 to 2015, and a comprehensive diagnosis based on year-by-year changes. "The ecosystem productivity resilience dataset for the countries along the Belt and Road is an important reference for analysing and comparing the current state of ecosystem productivity resilience in each country.

    2022-04-29 140 46

  • 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 5110 435

  • Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2020,SMHiRes,V2)

    Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2020,SMHiRes,V2)

    This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2020. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include GLASS Albedo, MUSES LAI/FVC, Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2 and Lat/Lon information.

    2022-04-26 306 70

  • 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 8148 273

  • 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 4292 379

  • 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 3078 218

  • 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 3154 68

  • 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 2544 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 6125 733

  • 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 4693 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 1823 286

  • 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 3269 217

  • 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 3469 267

  • 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]. Gansu: Gansu Cultural Publishing House, 1995 [22] Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006 [23] Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997 [24] Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995 [25] County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998 [26] Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006 [27] Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996 [28] Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. Gansu: Gansu Cultural Publishing House, 1999 [29] Zhuoni County Annals Editorial Committee. Zhuoni County Annals [J]. Gansu: Gansu Nationality Publishing House, 1994 [30] Diebu County Annals Editorial Committee. Diebu County Annals [J]. Gansu: Lanzhou University Press, 1998 [31] Pengxian County Annals Editorial Committee. Pengxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1989 [32] Guanxian County Annals Editorial Committee. Guanxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1991 [33] Wenjiang County Annals Editorial Committee. Wenjiang County Annals [J]. Sichuan: Sichuan People's Publishing House, 1990 [34] Shifang County Annals Editorial Committee. Shifang County Annals [J]. Sichuan: Sichuan University Press, 1988 [35] Tianquan County Annals Editorial Committee. Tianquan County Annals [J]. Sichuan: Sichuan Science and Technology Press, 1997 [36] Shimian County Annals Editorial Committee. Shimian County Annals [J]. Sichuan: Sichuan Cishu Publishing House, 1999 [37] Lushan County Annals Editorial Committee. Lushan County Annals [J]. Sichuan: Fangzhi Publishing House, 2000 [38] Hongyuan County Annals Editorial Committee. Hongyuan County Annals [J]. Sichuan: Sichuan People's Publishing House, 1996 [39] Wenchuan County Annals Editorial Committee. Wenchuan County Annals [J]. Sichuan: Bayu Shushe, 2007 [40] Derong County Annals Editorial Committee. Derong County Annals [J]. Sichuan: Sichuan University, 2000 [41] Baiyu County Annals Editorial Committee. Baiyu County Annals [J]. Sichuan: Sichuan University Press, 1996 [42] Batang County Annals Editorial Committee. Batang County Annals [J]. Sichuan: Sichuan Nationality Publishing House, 1993 [43] Jiulong County Annals Editorial Committee. Jiulong County Annals(Sequel) (1986-2000) [J]. Sichuan: Sichuan Science and Technology Press, 2007 [44] County Annals Editorial Committee of Derung-Nu Autonomous County Gongshan. County Annals of Derung-Nu Autonomous County Gongshan [J]. Beijing: Nationality Publishing House, 2006 [45] Lushui County Annals Editorial Committee. Lushui County Annals [J]. Yunnan: Yunnan People's Publishing House, 1995 [46] Deqin County Annals Editorial Committee. Deqin County Annals [J]. Yunnan: Yunnan Nationality Publishing House, 1997 [47] Yutian County Annals Editorial Committee. Yutian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [48] Cele County Annals Editorial Committee. Cele County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2005 [49] Hetian County Annals Editorial Committee. Hetian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [50] Qiemo County Local Chronicles Editorial Committee. Qiemo County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [51] Shache County Annals Editorial Committee. Shache County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [52] Yecheng County Annals Editorial Committee. Yecheng County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1999 [53] Akto County Local Chronicles Editorial Committee. Akto County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [54] Wuqia County Local Chronicles Editorial Committee. Wuqia County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1995

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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 5207 376