Global 0.05° near-surface freeze-thaw states data set (2002-2018)
  • 2019-10-21
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The near-surface freeze-thaw affects the water and energy exchanges mode and efficiency between the land and atmosphere. The transition of the freeze/thaw state affects the pattern of runoff concentration, which has an important impact on regional and global water cycle. Based on the remote sensing data of AMSR-E/2 passive microwave sensors and MODIS optical sensor, this data set uses the discriminant function algorithm and its downscaling method to produce a global mapping of near-surface freeze-thaw states with higher spatial resolution. This product covers the time period from 2002 to 2018 (daily), and spatial coverage is global scale (spatial resolution of 0.05°). It can be used to analyze the start/end time of global near-surface freeze/thaw states, the duration of freezing/thawing and their changing trends, and provide data support for studying the mechanism of water cycle and energy exchanges in the context of global change.

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River lake ice range / coverage data set v1.0
  • 2019-10-21
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There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.

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River lake ice phenology data in QPT (2002-2018) v1.0
  • 2019-10-21
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River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.

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Spatial distribution map of ecological carrying capacity in One Belt And One Road area in 2015
  • 2019-09-30
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Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.

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Typical case dataset of major global flood disasters (2018.01-2018.12)
  • 2019-09-30
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The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .

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Global drought intensity and major meteorological factors anomaly dataset (2018)
  • 2019-09-30
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This dataset mainly includes the spatial distribution of global SPEI in 1218 in 2018, the global drought intensity in 2018, and the anomalies of precipitation, land surface temperature, 0-10 cm soil moisture and the past 10 years (2009-2018); The flat index method, the maximum value synthesis method and the trend analysis method calculate the global drought intensity and the main meteorological factor anomaly data for 2018. The data time scale is 2018-01-01 to 2018-12-31, and the spatial resolution is 0.5 degree. The data can provide a scientific reference for the analysis of global drought distribution and drought assessment in 2018.

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Dataset of major global forest fire disasters (2018.01-2019.06)
  • 2019-09-29
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This data set contains 2018 global forest fire case data for the whole year and 2019, including the forest fire in California in November 2018, the forest fire in Attica, Greece in July 2018, and the forest fire in Shanxi Province in March 2019. Case data. Specific data include: fire intensity data of the monitoring range and data of vegetation index changes before and after the disaster. The data set is mainly used to describe the occurrence, development, impact and recovery of major global forest fire events in the first half of 2018-2019. The data mainly comes from NASA official website and EM-DAT database, it was processed by statistical and spatial analysis methods using EXCEL and ArcGIS tools. The data source is reliable, the processing method is scientific and rigorous, and it can be effectively applied to global (forest fire) disaster case analysis research.

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Long-term sequence dataset of China snow depth (1979-2018)
  • 2019-09-19
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This data set is an upgraded version of the “Long-term Sequence Data Set of China Snow Depth". The source data of the dataset differ from those of the previous version. Because AMSR-E stopped running in 2011, snow depth from 2008 to 2018 is extracted using the brightness temperature of the SSMI/S sensor. This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2018, with a spatial resolution of 0.25 degrees. The original data used to invert the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2018) processed by the National Snow and Ice Data Center (NSIDC). Because the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data. The time consistency of the brightness temperature data is improved by cross-calibrating the brightness temperatures of different sensors. The snow depth inversion is then performed using the algorithm specifically modified for China by Dr. Tao Che based on the Chang algorithm. For the specific inversion method, please refer to the data specification, “Long-term Sequence Data Set of China Snow Depth (1979-2018) Introduction. doc". The data set is a latitude and longitude projection, with one file each day, the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data file.

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Land cover of core countries of the Belt and Road in 2015 (Version 1.0)
  • 2019-09-16
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Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).

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