Slope data of economic corridors in Silk Road can reflect the degree of steepness of the surface units of the six major economic corridors, the unit is degree (°). The spatial resolution of the data is 0.016 degrees, which is about 1.8km. The longitude range is 12.09°E-180°, and the latitude range is 10.99°S-90°N. The source is derived from the Global Relief Model built by the National Oceanic and Atmospheric Administration of the United States (NOAA). The range is cut by the border of the Silk Road. This data is one of the basic data necessary to assess the risks of natural disasters (including debris flows, landslides, flash floods, etc.) in the six economic corridors. The application frequency will be high and the prospects will be broad.
This data set contains information on natural disasters in Tibet of nearly 50 years, including the time, place and the consequences of natural disasters such as drought, snows disasters, frost hazards, hail, floods, gales, and lightning disasters. Tibet is located on the southwest border of China and is the main body of the Tibetan Plateau. Due to the influence of the westerly winds, weather and strong warm and wet air currents from the Indian Ocean, the dry and wet seasons are obvious. In addition, the mountains and forests are numerous, and the terrain is complex in Tibet, which makes Tibet among those regions in China having the highest frequencies of natural disasters. The main meteorological disasters that cause significant damage to the production of agriculture and animal husbandry in Tibet are snows disasters, frost hazards, hail, floods and gales. According to incomplete statistics, the average annual disaster area from 1982 to 2000 was 28,440 hectares, of which the disaster area in 1983 was the largest, 203,700 hectares, followed by 1995 with a disaster area of 133,300 hectares. From the proportions of various disaster areas in the total area affected by the disasters, the proportion under drought is the largest, reaching 38%, followed by that under diseases and insect pests, which was 25%. Tibet is sparsely populated, and the ecological environment is very fragile. Traditional farming and animal husbandry production basically relies on people. Various meteorological disasters have caused heavy losses to the lives and property of the Tibetan people. Snow disasters topped the list of various meteorological disasters in Tibet. Tibet is one of the five largest pastoral areas in the country, and livestock is the most important source of production and livelihood for herdsmen. Snow disasters often cause large numbers of livestock death, significant property losses to herdsmen and threat to their lives. The data are extracted from the Tibet Volume of Chinese Meteorological Disaster Dictionary, with manual entry, summarizing and proofreading.
1) The data includes the soil erosion modulus of 22 watersheds with a resolution of 2.5 m in the year of 2019 in the Xinjiang Uygur Autonomous Region. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 22 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 22 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
This data set collects the wave tide level data of the southern sea area of Sri Lanka from September 2013 to October 2014. Sri Lanka is located in the core node of the "maritime Silk Road", which is the necessary node of our oil transportation lifeline. The wave observation data of this sea area is of great significance to understand the wave characteristics of this sea area and ensure the navigation safety of cargo ships and sea convoys. The data is obtained by the pressure sensor deployed on the seabed, and the data reliability is ensured by the quality control segments such as the removal of abnormal values. This data is of great significance to the analysis of marine disaster assessment, ship passing safety assessment and the study of wave characteristics in the sea area.
Global Tropical cyclone (TC) best track data already exist as individual storm tracks at other agencies. The intent of the IBTrACS project is to overcome data availability issues. This was achieved by working directly with all the Regional Specialized Meteorological Centers and other international centers and individuals to create a global best track dataset, merging storm information from multiple centers into one product and archiving the data for public use. The World Meteorological Organization Tropical Cyclone Programme has endorsed IBTrACS as an official archiving and distribution resource for tropical cyclone best track data. The IBTrACS project: contains the most complete global set of historical tropical cyclones available, combines information from numerous tropical cyclone datasets, simplifies inter-agency comparisons by providing storm data from multiple sources in one place, provides data in popular formats to facilitate analysis and checks the quality of storm inventories, positions, pressures, and wind speeds, passing the information on to the user. The primary intent of IBTrACS is to support scientific research efforts.
Data from EM-DAT. EM-DAT is a global database on natural and technological disasters, containing essential core data on the occurrence and effects of more than 21,000 disasters in the world, from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium.The main objective of the database is to serve the purposes of humanitarian action at national and international levels. The initiative aims to rationalise decision making for disaster preparedness, as well as provide an objective base for vulnerability assessment and priority setting.The database is made up of information from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies. Priority is given to data from UN agencies, governments, and the International Federation of Red Cross and Red Crescent Societies. This prioritization is not only a reflection of the quality or value of the data, it also reflects the fact that most reporting sources do not cover all disasters or have political limitations that could affect the figures. The entries are constantly reviewed for inconsistencies, redundancy, and incompleteness. CRED consolidates and updates data on a daily basis. A further check is made at monthly intervals, and revisions are made at the end of each calendar year.
A gridded ocean temperature dataset with complete global ocean coverage is a highly valuable resource for the understanding of climate change and climate variability. The Institute of Atmospheric Physics (IAP) provides a new objective analysis of historical ocean subsurface temperature since 1990 for the upper 2000m through several innovative steps. The first was to use an updated set of past observations that had been newly corrected for biases (e.g., in XBTs). The XBT bias was corrected by CH14 scheme, which is recommended by the XBT community. The second was to use co-variability between values at different places in the ocean and background information from a number of climate models that included a comprehensive ocean model. The third was to extend the influence of each observation over larger areas, recognizing the relative homogeneity of the vast open expanses of the southern oceans. Then the observations were also used to provide finer scale detail. Finally, the new analysis was carefully evaluated by using the knowledge of recent well-observed ocean states, but subsampled using the sparse distribution of observations in the more distant past to show that the method produces unbiased historical reconstruction. The ocean wind data set is constructed using RSS Version-7 microwave radiometer wind speed data. The input microwave data are processed by Remote Sensing Systems with funding from the NASA MEaSUREs Program and from the NASA Earth Science Physical Oceanography Program. This wind speed product is intended for climate study as the input data have been carefully intercalibrated and consistently processed. Each netCDF file contains: 1) monthly means of wind speed, grid size 360x180xnumber of all months since Jan 1988(increases over time) 2) a 12-month set of climatology wind speed, grid size 360x180, the climatology is an average calculated over the 20-year period 1988-2007 3) monthly anomalies of wind speed derived by subtracting the above climatology maps from the monthly means, grid size 360x180x#months since Jan 1988 (increases over time) 4) a wind speed trend map, grid size 360x180, the trend is calculated from 1988-01-01 to the latest complete calendar year 5) a time-latitude plot (a minimum of 10% of latitude cells is required for valid data), grid size 180x#months since Jan 1988 (increases over time).
The extreme drought damage historical events data of the 34 key areas along One Belt One Road were collected from Internet. First, a Web crawler was coded by python language. Using several key words about extreme drought damage, web pages were then collected by Google and Baidu search engine. Last, important information about the extreme drought events (e.g., place, time, affected area, affected population, count of death) were extracted from web pages. This data can be used for risk assessment of extreme drought in the 34 key areas along One Belt One Road.
The global typhoon path data set contains the data of 29 typhoon path points in the Northwest Pacific in 2018, including time, longitude and latitude, central air pressure, wind speed and wind force, future direction, future speed, wind force level and other indicators; the data comes from the typhoon network of the Central Meteorological Station (http://typhone.nmc.cn/web.html), using Python to grab the typhoon path data published on the web page, In addition, the captured Excel data table is sorted into ShapeFile form, and each path point is given wind power level according to the wind power rating standard of typhoon; It can be applied to the analysis of the characteristics and influence of the movement of typhoon path points, wind speed and wind force.
The data includes the path data of tropical cyclone "iday" in the southern hemisphere in March 2019, and the data of flood affected area in southern Africa caused by it. It is an important data source supplement for major global tropical cyclone disasters in 2019. The track data of the tropical cyclone is collected from the monitoring data of the National Satellite Meteorological Center, and the longitude and latitude coordinates are obtained by using ArcGIS software; the flooded range data of the southern Africa flood is extracted by the Institute of remote sensing of the Chinese Academy of Sciences Based on the high-resolution three satellite image. The data can be used for the path analysis, affected situation analysis and disaster damage assessment of tropical cyclone "Yidai".