The historical storm surge events data of the 34 key areas along One Belt One Road were first collected from Internet and then re-processed. First, a Web crawler was coded by python language. Using several key words about storm surge, web pages were then collected by Google and Baidu search engine. Last, important information about the storm surge 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 storm surge in the 34 key areas along One Belt One Road.
GE Yong, LING Feng
1) The data includes the soil erosion modulus of 18 watersheds with a resolution of 5 m in the year of 2017 in Thailand. 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 18 watersheds of Thailand 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 18 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 intensity is of great significance for studying the present situation of soil erosion in Pan third polar region and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
YANG Qinke
One belt, one road, one belt, one road, one belt, one road, is the key city to solve the extreme drought climate events in 34 key nodes (important cities, major projects, ports and industrial parks). The risk assessment of extreme drought is carried out. The research supports the green "one belt and one road" construction of the spatial route map, and serves the green "one belt and one road" construction. Design. The vulnerability of drought disaster risk assessment for each node, on the one hand, depends on the sensitivity of different land cover types to drought disasters; on the other hand, it reflects the health of the ecological environment, determines the region's ability to bear and recover from drought disasters, which shows that the surface features under different land cover types are adversely affected by drought disasters The tendency to be loud. Using the 2015 land cover data of the "2018 silk road environment special project" source data, the vulnerability characteristics of different land cover types are measured by factor analysis method, and the weight of land vulnerability is assigned. The extreme drought vulnerability index with 100 m resolution of each node is obtained, which can provide reference for the construction planning, operation management and environmental problems of China's overseas parks, ports and major projects One belt, one road, one is the first and third, the other is the first and third.
WU Hua, ZHANG Dan, CHEN Baozhang
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
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
The data set records the comparison of direct economic losses caused by geological disasters in Qinghai Province from 2011 to 2018. The data is collected from the Department of ecological environment of Qinghai Province, and the data set contains 8 data tables, which are: direct economic losses caused by sudden geological disasters in 2011, direct economic losses caused by sudden geological disasters in 2012, comparison chart of direct economic losses caused by sudden geological disasters in 2013 and comparison chart of direct economic losses caused by geological disasters in 2014 The statistical table of direct economic losses caused by sudden geological disasters in Qinghai Province in 2015, the statistical table of direct economic losses caused by sudden geological disasters in Qinghai Province in 2016, the comparison of direct economic losses caused by sudden geological disasters in Qinghai Province in 2017, and the comparison chart of direct economic losses caused by sudden geological disasters in Qinghai Province in 2018 have the same data table structure. Each data table has two fields, such as the comparison chart of direct economic losses caused by sudden geological disasters in Qinghai Province in 2013 Field 1: disaster type Field 2: direct economic loss
Department of Ecology and Environment of Qinghai Province
Based on the global surface water data (wod) from 1984 to 2018, the extreme precipitation frequency index and extreme precipitation intensity index were selected. Combined with the spatial analysis method in ArcGIS, the risk level of flood disaster in 34 key nodes under extreme precipitation conditions was constructed and evaluated. One belt, one road, 34 key nodes, is evaluated for the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events, which provides a basis for local government departments to make decisions and early warning before floods occur, so that we can gain valuable time for disaster prevention and mitigation measures to reduce the lives of the people brought by floods. Loss of property.
GE Yong, LI Qiangzi, LI Yi
This data set is based on the spatial distribution data set of extreme precipitation disaster risk (2019) and vulnerability spatial distribution data set (2019) in Yangon deep water port area, combined with GDP and population distribution data of Yangon deep water port area, and through the definition of "risk = exposure × vulnerability × risk", the risk of extreme precipitation disaster in Yangon deepwater port area is calculated. The data set can provide a reference for the local disaster prevention and reduction work. By analyzing the distribution and causes of high risk, we can put forward engineering measures or non engineering measures to achieve the purpose of disaster reduction and prevention, and reduce the loss of people's lives and property caused by extreme precipitation disasters.
LI Yi
The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei, YIN Cong
The evaluation area of the data set is the central urban area of Yangon deepwater port. The data set is based on the extreme precipitation disaster risk spatial distribution data set (2019) and its evaluation index system. The data set considers both precipitation risk and terrain risk. Among them, precipitation risk index includes extreme precipitation intensity index and extreme precipitation frequency index, both of which are obtained from GPM precipitation data. Terrain risk mainly considers elevation index. Finally, the risk assessment results of extreme precipitation disaster are obtained. The probability and intensity of extreme precipitation disaster in high risk area are higher than those in low risk area.
GE Yong, LI Qiangzi, LI Yi
One belt, one road, one belt, one road, one belt, one road, is the key city to solve the extreme drought climate events in 34 key nodes (important cities, major projects, ports and industrial parks). The risk assessment of extreme drought is carried out. The research supports the green "one belt and one road" construction of the spatial route map, and serves the green "one belt and one road" construction. Design. For the risk assessment of drought disaster in each node, the hazard of disaster causing factors refers to the change characteristics and abnormal degree of the main meteorological factors causing drought disaster, such as the abnormal reduction of natural precipitation, the increase of evaporation or the abnormal high temperature. It is generally believed that the risk of drought disaster increases with the increase of the risk of disaster causing factors. Based on the spatialized satellite and reanalysis data of temperature, precipitation and soil available water content, the Palmer drought index of key node area was calculated to characterize the risk of extreme drought disaster factors in each node. One belt, one road and the other major projects should be built for the construction of the overseas parks, ports, major projects, and the scientific basis and Countermeasures for dealing with the drought disasters.
WU Hua, ZHANG Dan, CHEN Baozhang
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
The area of the data set is the central urban area of Yangon deep water port. The data set is based on the spatial distribution data set of extreme precipitation disaster vulnerability (2019) and refers to its evaluation index system. When evaluating the vulnerability of extreme precipitation disaster in Yangon deepwater port area, the disaster reduction ability and sensitivity index are considered. The disaster reduction ability is negatively correlated with vulnerability, and the sensitivity is positively correlated with vulnerability. Disaster reduction capacity considers the density of impervious surface, road network and emergency rescue facilities; sensitivity considers the local land cover types, including farmland, urban and road crisscross. When extreme precipitation disaster occurs, high vulnerability areas will suffer more serious losses, and the reconstruction is more difficult.
GE Yong, LI Qiangzi, LI Yi
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 the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).
DONG Wen
One belt, one road, one belt, one road, one belt, one road, is the key city to solve the extreme drought climate events. 34 key nodes (important cities, major projects, ports and industrial parks) are selected to carry out extreme drought risk assessment. Construction. The data one belt, one road area, and 34 extreme nodes in the "one area" area were evaluated by the extreme drought risk assessment index system. The time resolution and spatial resolution were 300 months. In order to facilitate the analysis of extreme drought risk index, the slope of the linear regression equation of monthly drought risk index at each pixel scale from 2014 to 2015 is calculated, which is used to represent the temporal variation characteristics of extreme drought (greater than 0 means drought aggravation, less than 0 means drought alleviation). At the same time, it can also reflect the spatial difference of extreme drought on the regional scale because it calculates the temporal change rate of each pixel.
WU Hua, ZHANG Dan, CHEN Baozhang
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 30 m in the year of 2017 in Qinhai. 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 11 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 11 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.
ZHANG Wenbo
One belt, one road level, is set up. The data set is based on the 100 meter risk assessment data set and the 100m level vulnerability assessment dataset. The risk assessment data set of 34 nodes and 100 meters in the key area of the whole area is calculated based on the international definition of risk, risk (R) = hazard (H) * vulnerability (V). The data set assessed one belt, one road, the extreme precipitation risk under extreme precipitation events, and provided the basis for local government departments' decision-making. At the same time, it could make early warning before the flood disaster, so that we could gain valuable time to take measures to prevent and reduce disasters and reduce the loss of lives and property of people caused by floods.
GE Yong, LI Qiangzi, LI Yi
The historical extreme precipitation events data of the 34 key areas along One Belt One Road were first collected from Internet and then re-processed. First, a Web crawler was coded by python language. Using several key words about extreme precipitation, web pages were then collected by Google and Baidu search engine. Last, important information about the extreme precipitation 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 precipitation in the 34 key areas along One Belt One Road.
GE Yong, LING Feng
1) The data is the layout of sample survey units in 65 countries of Pan-Third Polar region and western China. 2)Sample survey units were set in the pan-third pole region (70 °N-10 °S, 180 °E-180 ° W) . No samplings points were selected in the region with latitude >70 °. In the region wiht latitude of 60 ° -70 ° , sample survey units were selected in cells of 0.5 ° latitude ×1 ° Longitude (about 55km×55km-55km×38km). In the area with latitude of 40°-60°, sample survey units were selected in cells of 0.5 ° latitude×0.75 ° longitude (about 55km×63km-55km×42km). In the area with latitude <40°, sample survey units were selected in cells of 0.5 ° latitude × 0.5 ° longitude. In the Qinghai-Tibet Plateau, sample survey units were selected in cells of 0.25 ° latitude × 0.25 ° longitude. Thesample survey units deployed in the first national water conservancy survey for soil and water conservation were used in current project in five provinces including Xinjiang, Qinghai, Gansu, Sichuan and Yunnan in western China. The total number of sample survey units is 29,651, of which, 4052 are in the Qinghai-Tibet Plateau, 8771 in the western China, and 16,828 in countries outside of China. 3) The selected sample survey units is well distributed and the data quality is good.4) the layout map of sample survey units is of great significance for the study of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the area along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
WEI Xin
The data set records the Geological Environment Bulletin of Qinghai Province from 2011 to 2019. The data set contains 9 PDF data files, which are collected from the Department of natural resources of Qinghai Province. Qinghai provincial government order No. 72 "geological environment protection, social and environmental protection for the people of Qinghai Province" is the basis for the comprehensive protection of the geological environment, According to the geological environment survey and monitoring data, the provincial natural resources department publishes the annual Geological Environment Bulletin and publishes the annual geological environment status of our province to the public. The main contents of the Geological Environment Bulletin of Qinghai province include: the distribution characteristics, causes, harm degree and prevention and control of geological disasters in the whole province; the development and utilization of groundwater resources and dynamic changes, groundwater pollution; the protection and restoration of mine geological environment. The Geological Environment Bulletin of Qinghai Province is jointly compiled by the geological exploration management office of Qinghai Provincial Department of natural resources and the geological environment monitoring station of Qinghai Province.
Department of Natural Resources of Qinghai Province
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