On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of 34 key nodes, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using handred meters grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, distance of offshore line, etc. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method. Finally, the storm surge danger index is normalized to 0-1, which can be used to evaluate the danger level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks.
GE Yong, LI Qiangzi, DONG Wen
Data set of surface inundation caused by historical extreme precipitation evaluated the surface inundation range of One Belt And One Road key areas under extreme precipitation, providing a basis and reference for the decision-making of local government departments, so as to give early warning before the occurrence of extreme precipitation and reduce the loss of life and property caused by extreme precipitation.This data set to the extreme precipitation threshold set "and" the extreme precipitation recognition "as the foundation, to confirm the extreme precipitation time node and the area, and then to NASA's web site to download the submerged range products corresponding to the time and region, combining ArcGIS spatial analysis was used to connect the above data, build the data sets of historical extreme precipitation caused surface submerged range for 34 key nodes. The data mainly includes 34 key nodes (Vientiane, China-Myanmar oil and gas pipeline, China-Laos Thai-Cambodia railway, Alexandria, Yangon, Kwantan, Kolkata, Warsaw, Karachi, Yekaterinburg, Yekaterinburg and other regions).
WU Hua
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative moisture index is one of the indicators that characterize soil drought. It is the ratio of soil relative humidity to field water holding capacity, which can directly reflect the availability of water for crops.The soil moisture data is obtained from the SMAP remote sensing soil moisture data product through the downscaling method, and the field water holding capacity data comes from the Hamonized World Soil Database (HWSD). For detailed calculation formulas and methods, please refer to: "National Standard for Agricultural Drought Grades of China" No.: GB/T 32136-2015. The data covers 34 key node areas along the Belt and Road.
WU Hua
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The relative humidity index is the difference between the amount of precipitation in a certain period of time and the potential evapotranspiration over the same period divided by the potential evapotranspiration.
GE Yong, WU Hua
Apparent temperature refers to the degree of heat and cold that the human body feels, which is affected by temperature, wind speed and humidity. The spatial scope of the data covers 34 key nodes in the pan-third pole region (Vientiane, Yangon, Kolkata, Warsaw, Karachi, Yekaterinburg, Chittagong, Tashkent, etc.). The spatial resolution is 100m, and the temporal resolution is year. Processing process: Based on the monitoring data of the meteorological station, calculate the apperant temperature based on the Humidex index, and then use the temperature correction method based on elevation correction to obtain 1km gridded data of the entire area, and downscale it to 100m. The heat wave risk dataset mainly uses intensity as the evaluation index. The spatial range and spatial resolution are consistent with the somatosensory temperature data set, and the temporal resolution is years. The criterion for judging the heat wave is: the weather process in which the somatosensory temperature exceeds 29℃ for three consecutive days is judged to be a high-temperature heat wave.
YANG Fei, WU Xilin, YIN Cong
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The relative moisture index is the difference between the precipitation in a certain period of time and the potential evapotranspiration in the same period and then divided by the potential evapotranspiration in the same period.The precipitation data comes from the downscaling of the TRMM/GPM satellite precipitation data, and the potential evapotranspiration is estimated using the Thornthwaite method. For detailed algorithm, please refer to "National Standard for Meteorological Drought of China" (GB/T 20481-2017). The data only covers 34 key node areas along the Belt and Road.
WU Hua
The basic data source of this dataset is from the website of the National Oceanic and Atmospheric Administration (NOAA). NOAA satellites are meteorological observation satellites. Provide meteorological environment information including temperature, precipitation, dew point, wind speed, etc. This dataset mainly covers key nodes in the pan-third pole Southeast Asia and Middle East regions. The main steps of data processing are as follows: First, according to the definition of high temperature heat waves in China's national standard "GB / T 29457-2012", based on basic meteorological data, determine the occurrence of high temperature heat waves, and then statistically obtain the frequency of high temperature heat waves. The time and occurrence intensity are collated to obtain the historical high temperature heat wave disaster event data set. This data set is helpful for clarifying the occurrence of extreme high temperature disasters in each study area, and provides reference materials and a strong basis for judging the intensity of high temperature heat waves in each area.
GE Yong, LIU Qingsheng
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.
GE Yong, LI Qiangzi, DONG Wen
Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).
WU Hua
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.
LUO Yao
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.
YANG Qinke
The China-Pakistan Economic Corridor, north from Kashgar of China and south to the Gwadar seaport of Pakistan, with a total length of 000 km, is the key to linking the north and south Silk Road. Due to the complex geology, landform, climate, hydrology conditions, landslides and debris flows are very active in this area. Through the combination of field investigation and image interpretation, the symbols of typical landslide and debris flow images were established. Based on interactive interpretation and field investigation verification, the spatial distribution of landslides and debris flows within the scope of CPEC was identified, which provides important data support for risk analysis of landslide and debris flow disasters in CPEC and disaster prevention and reduction.
ZOU Qiang
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.
TIAN Feng, WU Jianjun, ZHOU Hongmin
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. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
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.
CHEN Yiting, YANG Hua, WU Jianjun, ZHOU Hongmin
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.
YANG Yuqing, GONG Adu, WU Jianjun, ZHOU Hongmin
This data set is used to analyze the global activity level of strong earthquakes (Mw 5) in the past 30 years, and to present it spatially. It can be used to obtain the distribution areas of strong earthquakes with high frequency and activity level in recent years. By comparing the distribution of strong earthquakes in 2018 with that in 1989-2018, the distribution characteristics of global strong earthquakes in 2018 are obtained. The original data of strong earthquakes are from USGS, and the local density is calculated as frequency information. The magnitudes of all earthquake cases are interpolated globally, and then the frequency and magnitude are multiplied as the activity level of strong earthquakes. The data set is in TIff format with a spatial resolution of about 80 km. The data set can provide a reference for the analysis of strong earthquake activity level on the global scale, and is helpful for the analysis of global earthquake risk and the construction of earthquake prevention and disaster reduction system.
Chen Jin, Tang Hong, WU Jianjun, ZHOU Hongmin
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".
CHEN Yiting, YANG Hua, WU Jianjun, ZHOU Hongmin
1) The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015m the grid resolution is 300m.2) The data of soil erosion intensity are obtained by using the Chinese soil erosion prediction model (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors, engineering measures factors and tillage measures factors are obtained from the first water conservancy census data; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 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 intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better carrying out the development policy of the area along the way.
ZHANG Wenbo
1) The dataset includes the raster data of soil erosion intensity in Pan-Third Pole 65 countries.2) The data of soil erosion intensity are obtained by using the Chinese soil loss equation (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors are calculated by 250 m soil grid data; engineering measure factors are calculated based on vegetation cover, land use and rainfall erosivity ratio; tillage measure factors haven't been considered yet, and the default value is 1; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 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 intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
ZHANG Wenbo
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