Hengduan Mountain is located in the western part of Sichuan Basin, the northwestern part of Yunnan-Guizhou Plateau and the eastern part of Qinghai-Tibet Plateau. The Sichuan-Tibet Railway spans 14 large rivers and 21 snow-covered mountains over 4000 meters. The area is affected by many factors, such as complex geological structure, strong plate movement, diverse geomorphology, weathering and fragmentation of rock strata, major engineering disturbance, and climate change. As a result, earthquakes, debris flow, collapse, landslide, glacial lake outburst, mountain torrent, snow disaster and drought and other disasters in this region are highly frequent and frequent, showing obvious space-time extension, with short disaster period, high intensity and wide spread range. This data set is a collection of unmanned aerial vehicle remote sensing images and field photos of our second scientific expedition to the Qinghai-Tibet Plateau in the above areas, which is of great significance to support the strategic needs of disaster prevention and mitigation, engineering safety protection and regional development on the Qinghai-Tibet Plateau.
ZHANG Qiang, ZHOU Qiang, WU Wenhuan, ZHAO Jiaqi, YUAN Ruyue
Based on China's daily ground meteorological elements data set, national geographic basic data, demographic data, and 30M resolution DEM data, statistical yearbook data, historical disaster records, and other related data, using multi-methods like PCA, random forests to calculate hazard and vulnerability indicators, based on extreme precipitation，high temperature, flood, snow hazard, collapse and landslide hazards, to build comprehensive disaster risk index, and process them with normalization. Among them, we consider all the above disaster types in Hengduan Mountain area, and flood, snow disaster, collapse and landslide disaster in sichuan-tibet railway. The natural disasters hazard map, vulnerability map and comprehensive risk map of Hengduan Mountains (Sichuan-Tibet Railway) are included in the dataset.
ZHANG Qiang, ZHOU Qiang, WU Wenhuan, ZHAO Jiaqi, YUAN Ruyue
This data includes two standards: the data resource construction specification and the metadata specification for the scientific investigation of geological and geographical environment and disaster risk in the Qinghai Tibet Plateau. According to the opinions of the general office of the CPC Central Committee and the general office of the State Council on strengthening the development and utilization of information resources, the archives law of the people's Republic of China, the measures for the management of scientific data, and the outline for the construction of the platform for the basic conditions of science and technology, and in combination with the characteristics of the contents and achievements of task 9 scientific investigation, In order to facilitate the collection and sharing of scientific research data, realize simple and efficient management of complex project achievement data, and better protect the intellectual property rights of data resource producers, the metadata content standard framework and resource construction specification of task 9 of the second comprehensive scientific investigation on the Qinghai Tibet Plateau are formulated. In order to better serve the project itself, we should ensure the standardization and standardization of the data of each subject.
YANG Yaping
The data set includes the distribution data of mud flow terraces along the Sichuan Tibet railway and the distribution data of debris and loose particles along the Sichuan Tibet railway. The distribution data of mud flow terraces along the Sichuan Tibet railway is based on the data of Gaofen No.2 in recent years in China. The distribution map of freeze-thaw mud flow Terraces along the Sichuan Tibet railway is produced by deep learning classification method combined with manual visual interpretation and correction. The largest single mudflow terrace is 1030043 m2, which is located in Kangding City, about 12km away from Xinduqiao station of Sichuan Tibet railway. The smallest single mudflow terrace is 1102 m2, which is located in Naidong District, about 3.3km away from Jiacun station of Sichuan Tibet railway. The average area of mudflow terrace along the line is 45013 m2. Mudflow terraces along the line are mainly distributed in Kangding City, Chaya county and SANGRI county. Based on the remote sensing image data of gaofen-2 in the study area, the distribution data of clastic particles along the Sichuan Tibet railway are interpreted. The slope particles are widely developed in Litang Linzhi section of Sichuan Tibet railway. According to the flow characteristics and structural model, they are divided into active type and in-situ weathering type. At present, a total of 2308 slope granular diseases have been identified in the study area, covering an area of 1283.21km2, with an average area of 0.56km2. The minimum area in the figure above is 600m2, which is mainly distributed between 3700m and 5500m above sea level, with an average altitude of 4767.78m. About 95% of the slope particles in the study area have an area less than 2.0 × 104m2, with an average area of 55.5 square meters × 104m2, with the largest area of 9148 × 104m2； The slope granular materials are mainly distributed between the elevation of 4500-5400m, accounting for 87.9% of the total slope granular materials. The slope granular materials with the elevation of 5000-5400m account for 47.7%, with an average elevation of 4945m. The single slope granular material with the lowest elevation has an elevation of 3241m; The slope gradient of granular materials in the study area is mainly between 30-70 ° Among them, accounting for 89.5% of the total number of slope granular. The data set is used to formulate the operation specification of digital processing. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. It provides a basis for the study of the development law of freeze-thaw mudflow and paleoclimate and the geographical distribution characteristics of granular materials on the slope of Sichuan Tibet engineering corridor.
JIANG Liming, JIANG Liming, JIANG Liming, HUANG Ronggang, WANG Huini
The main content of the data set is the survey data set of slope and pavement engineering diseases along G317 and G318 national highways, which is obtained through field survey. The survey time is from January 9 to January 19, 2020, and from August 10 to September 2, 2020. The respondents were G317 (Nagqu Ganzi) of North Sichuan Tibet line and G318 (Lhasa Xinduqiao) of South Sichuan Tibet line. The types of diseases investigated mainly include slope diseases and disasters induced by freezing and thawing (rockfall, dangerous rock mass and debris slope), pavement crack diseases, loose diseases, pit diseases, subgrade deformation diseases and salivary flow ice diseases in winter. Using the method of manual investigation, observe the damage of various diseases, and record the number (SCOPE), damage degree and location of various types of damage according to the requirements. The data set can provide a basis for a comprehensive understanding of the freeze-thaw diseases of the main highway projects in Sichuan Tibet engineering corridor and related research.
NIU Fujun
The cataloguing data and distribution map of debris flow dammed lake burst flood disaster chain, which can be observed in literature and satellite images, have been sorted out. In the data, debris flow can be divided into two types: General debris flow and glacier debris flow. The data mainly through literature investigation combined with remote sensing identification to determine the location and type of disaster chain, and then sorted into tables and generated vector data. The data were generated from the investigation literature and remote sensing visual interpretation. It is difficult to evaluate the integrity of data because it is impossible to judge the exact time of many disasters. The number of disaster points is field scientific research area code + River Basin name initial code + disaster chain type code + four digit sequence number. See Excel data file for details.
ZHOU Liqin, TANG Chenxiao
The data set records the statistical data of fire accidents in Qinghai Province from 1998 to 2010, which are divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 13 tables Fire accident 2001.xls Fire accident 2006.xls Fire accident 2007.xls Fire accident 2008.xls Fire accident 2009.xls Fire accident 2010.xls Fire accident 1998.xls Fire accident 1999.xls Fire accident 2000.xls Fire accident 2002.xls Fire accident 2004.xls Fire accident 2006.xls Fire accident 2003.xls The data table structure is the same. For example, there are six fields in the data table of fire accidents in 2001 Field 1: Category Field 2: number of fires Field 3: number of deaths Field 4: number of injured persons Field 5: loss converted into RMB 10000 Field 6: cause of fire
Qinghai Provincial Bureau of Statistics
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
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 data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". 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 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 data set records the main distribution of sudden geological disasters in Qinghai Province from 2011 to 2018. The data are collected from the Department of ecological environment of Qinghai Province. The data set contains seven tables, which are: the main distribution of sudden geological disasters in 2011, 2012, 2014, 2015 and 2016 Distribution statistics table, 2017 Qinghai Province sudden geological disasters distribution table, 2018 Qinghai Province sudden geological disasters distribution table, the data table structure is the same. Each data table has five fields, such as the statistical table of the main distribution of sudden geological disasters in Qinghai Province in 2016 Field 1: county (city) Field 2: landslide Field 3: collapse Field 4: debris flow Field 5: loess collapsibility
Department of Ecology and Environment of Qinghai Province
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
The data set records the frequency statistics of typical geological disasters in Qinghai Province from 2011 to 2016. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains six data tables, which are: the frequency of sudden geological disasters in 2011, 2012, 2013, 2014 and 2015 Statistical table, 2016 Qinghai Province sudden geological disasters frequency statistical table, data table structure is the same. There are two fields in each data table, such as the occurrence frequency of sudden geological disasters in 2011: Field 1: Location Field 2: frequency ratio
Department of Ecology and Environment of Qinghai Province
The data set records the typical geological disasters in Qinghai Province from 2011 to 2018. The data set includes 10 data tables, which are: typical geological disasters in 2011, 2012, 2013, 2013, distribution, 2014, etc The data structure of typical geological disasters in 2018 is the same. Each data table has five fields, such as the typical geological disasters in 2011: Field 1: Location Field 2: disaster type Field 3: time of occurrence Field 4: scale Field 5: hazards and losses
ZHAO Hu
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
There are 428 large and medium-sized landslides in the Bangladesh China India Myanmar economic corridor. The number of landslides in Myanmar is the largest, reaching 304, accounting for 71% of the total landslides, followed by China and India. The number of landslides is 71 and 52, accounting for 17% and 12% of the total landslides, respectively. There is only one landslide in Bangladesh. According to the material composition of landslide, it can be divided into rock landslide and soil landslide. There are 343 rock landslides in this area, accounting for 80% of the total number of landslides, and 85 soil landslides, accounting for 20% of the total number of landslides. Rock landslides are mainly distributed in the north of China, India and Myanmar, while soil landslides are mainly distributed in the middle and south of Myanmar. A total of 1569 debris flows were interpreted in the Bangladesh China India Myanmar corridor, including 574 gully debris flows and 995 slope debris flows. In the eastern part of the study area, debris flows are mainly distributed on both sides of Lancang River, Nujiang River, Mojiang River and Honghe River, and they are distributed in the north-south direction along these rivers. In the central part of the study area, debris flows are distributed in the ruokai mountain area. Compared with the gully type debris flow, the scale and harm of slope debris flow are much smaller. In this study, the correlation analysis of debris flow is mainly aimed at the gully type debris flow.
ZOU Qiang
The China Mongolia Russia economic corridor starts from China in the East, passes through Mongolia in the west to Russia, and crosses the Mongolian Plateau, West Siberian plain and Eastern European Plain. There are great differences in natural environment and complex geological conditions in the region. Driven by regional differences in structure, earthquake, meteorology, hydrology and ecology, landslides are widely distributed in China Mongolia Russia economic corridor. Based on remote sensing images, the landslide and debris flow disasters in China Mongolia Russia economic corridor are interpreted. Statistics show that there are 396 landslide disasters in China Mongolia Russia economic corridor, and the landslide disaster area is between 0.0006km2 ~ 8.57km2. The watershed area within 100km on both sides of the railway line, with a total area of 1.43 × 106km2, has identified 1336 debris flow gullies in the China Mongolia Russia economic corridor.
ZOU Qiang
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
Gwadar deepwater port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from the Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and the Red Sea in the West. It is a port with a strategic position far away from Muscat, the capital of Oman. This data set is an extreme drought risk assessment data set. From the four aspects of extreme drought risk, exposure, vulnerability, and stability, the Palmer drought index, elevation, water system, land use, population density, GDP density, inter field water capacity, and other data are used to comprehensively assess the extreme drought risk of the region. The spatial resolution of the data is 30 meters and the time is 2015.
WU Hua
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 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
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. 34 key nodes (important cities, major projects, ports and industrial parks) are selected to carry out extreme drought risk assessment. Construction. In one belt, one road area is divided into 34 zones with 1km resolution. The data are based on the linear regression slope of 2011-2015 years' multi period drought risk as the "extreme drought state change". The scientific basis for the drought disaster in China's overseas parks, ports, major construction projects, operation management, environmental problems, and prevention and control is provided. One belt, one road, the third pole area, is to promote and ensure the smooth implementation of the regional development strategy.
WU Hua, ZHANG Dan, CHEN Baozhang
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 Hambantota, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using ten meters grid as evaluation unit. Based on statistical method, the tide level of every 20 years, 50 years and 100 years is estimated. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method, which can be used to evaluate the danger level of storm surge in each assessment unit. The data set includes 20-year, 50-year and 100-year hazard assessment results of the port area of Hambantota.
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
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
One belt, one road, 34 key nodes, is used to assess the risk of flooding in the key areas of the "one belt" Road area under extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters and reduce the lives of the people. Loss of property. The data set takes one belt, one road, 34 key nodes, and the ratio of cultivated land to land, the proportion of urban land, the proportion of interlaced zone, the density of road network and the impervious surface. Based on the spatial analysis method in ArcGIS, the weights of each index are assigned. The vulnerability of 34 key nodes under extreme precipitation conditions is evaluated, and the vulnerability is determined by natural breakpoint method. Sex is divided into five levels, which represent no vulnerability, low vulnerability, medium vulnerability, high vulnerability and extremely high vulnerability.
GE Yong, LI Qiangzi, LI Yi
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
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
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 disaster risk and vulnerability of storm surge in each unit are extracted and calculated using10 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit. The data set includes 20-year, 50-year, and 100-year corresponding risks.
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.
This data set contains the statistical information of natural disasters in Qinghai Tibet Plateau in the past 50 years (1950-2002), including drought, snow disaster, frost disaster, hail, flood, wind disaster, lightning disaster, cold wave and strong cooling, low temperature and freezing damage, gale sandstorm, insect disaster, rodent damage and other meteorological disasters. Qinghai and Tibet are the main parts of the Qinghai Tibet Plateau. The Qinghai Tibet Plateau is one of the Centers for the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the international scientific and technological circles to study climate and ecological environment changes. Its complex terrain conditions, high altitude and severe climate conditions determine that the ecological environment is very fragile, It has become the most frequent area of natural disasters in China. The data were extracted from "China Meteorological Disaster Canon · Qinghai volume" and "China Meteorological Disaster Canon · Tibet Volume", which were manually input, summarized and proofread.
Statistical Bureau Statistical Bureau
The accuracy of tropical cyclone (tropical storm) track forecasting improved by nearly 50% for lead times of 24–72 h since 1990s. Over the same period forecasting of tropical cyclone intensity showed only limited improvement. Given the limited prediction skill of models of tropical cyclone intensity based on environmental properties, there have been a wealth of studies of the role of internal dynamical processes of tropical cyclones, which are largely linked to precipitation properties and convective processes. The release of latent heat by convection in the inner core of a tropical cyclone is considered crucial to tropical cyclone intensification. 16-year satellite-based precipitation, and clouds top infrared brightness temperature were used to explore the relationship between precipitation, convective cloud, and tropical cyclone intensity change. The 6-hourly TC centers were linearly interpolated to give the hourly and half hourly tropical cyclone center positions, to match the temporal resolution of the precipitation and clouds top infrared brightness temperature. More precipitation is found as storms intensify, while tropical cyclone 24 h future intensity change is closely connected with very deep convective clouds with IR BT < 208 K. Intensifying tropical cyclones follow the occurrence of colder clouds with IR BT < 208 K with greater areal extents. As an indicator of very deep convective clouds, IR BT < 208 K is suggested to be a good predictor of tropical cyclone intensity change（Ruan&Wu，2018，GRL）. The properties of the satellite-based precipitation, and clouds top infrared brightness temperature are therefore suggested to be important measurements to study tropical cyclone intensity, intensity change and their underlying mechanisms. The high resolution of the satellite-based precipitation (3h), and cloud top infrared brightness temperature (half hour) datasets also makes them possible to be used to study tropical cyclone variability associated with diurnal cycle.
WU Qiaoyan
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
This data set is a collection of statistics on major geological disasters in the Himalayas, the study area starts in Zada County, Guer County of The Ali region in the west, the east side is bounded by the Yalu-Zanbu River, the northern boundary is the Yalu-Zanbu River break, south to the vast Himalayan region of the national boundary. The Himalayas are located in the southwest of China, the southwest of the Qinghai-Tibet Plateau, the world's largest, highest and youngest mountain range, the world's highest peak Mount Qomolangma is located here. Here the geological structure is complex, seismic activity is frequent, the new tectonic movement is strong, the internal and external dynamic geological action is very active, is one of the most serious geological disasters in China. The original data of the data set is digitized from the report of the Remote Sensing Survey of Major Geological Disasters in the Himalayas, and the total number of disaster statistics is more than 540, including three types of disasters: landslides, mudslides and glacial final lake collapses. This data set provides basic data for the study of disaster reduction and prevention in the Himalayas region of Tibet, and is of reference value for research in related fields.
TONG Liqiang
The pan third pole historical extreme precipitation data set includes 2000-2018 extreme precipitation identification data. One belt, one road, was used to assess the rainfall in the important area along the GPM IMERG Final Run (GPM) daily rainfall. The extreme precipitation threshold of 34 important nodes was evaluated by percentile method. The daily precipitation period was identified by the calculated threshold, and the surface inundation area was produced on the basis of extreme precipitation. The data range mainly includes 34 key nodes of Pan third pole (Vientiane, Alexandria, Yangon, Calcutta, Warsaw, Karachi, yekajerinburg, Chittagong, Djibouti, etc.) The data set can provide the basis for local government decision-making, so as to correctly identify extreme precipitation and reduce the loss of life and property caused by extreme precipitation.
HE Yufeng
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 percentage of precipitation anomaly is the percentage of the precipitation between a certain period of time and the average climate precipitation of the same period divided by the average climate precipitation of the same period.Based on the daily rainfall data of GPM IMERG Final Run(GPM), this data set calculates the precipitation of the corresponding region, adopts the evaluation index of precipitation anomaly percentage grade, and analyzes the distribution characteristics of drought of different grades. The data area is 34 key nodes of the pan-third pole (Abbas, Astana, Colombo, Gwadar, Mamba, Tehran, Vientiane, etc.).
WU Hua
We compiled the Seismotectonic Map of Western Asia using the ArcGIS platform through data collecting and digitization. The seismotectonic map of Western Asia covers Iran and its surrounding countries and regions. Based on the “Major active faults of Iran” map, the seismotectonic map is replenished with massive published data and depicts the location and nature of the seisogenic faults or active faults and the epicenter of earthquakes with M ≥ 5 from 1960 to 2019. The map can not only be used in the research of active faults and seismic risks in Western Asia, but also will be applied to the seismic safety evaluation for infrastructure construction.
LIU Zhicheng
The Pan-Third Polar region has strong seismic activity, which is driven by the subduction and collision of the Indian plate, the Arab plate and the Eurasian plate. 18806 earthquakes with Magnitude 5 or larger have occurred in Pan-Third Polar region (north latitude 0-56 degrees and east longitude 43-139 degrees) since 1960. Among them, 4 earthquakes with Magnitude 8 or larger, 187 earthquakes with Magnitude 7.0-7.9， 1625 earthquakes with Magnitude 6.0-6.9 and 16990 earthquakes with Magnitude 5.0-5.9 have occurred. Earthquakes occurred mainly in the foothills of the India-Myanmar Mountains, the Himalaya Mountains, the Sulaiman Mountains, where the India Plate collided with the Eurasian plate, and the Zagros Mountains where the Arab plate collided with the Eurasian plate.
WANG Ji
We compiled the Seismic Zonation Map of Western Asia using the ArcGIS platform through data collecting and digitization. The Seismic Zonation map of Western Asia covers Iran and its surrounding countries and regions. Based on the “Major active faults of Iran” map, the map is replenished with massive published data and depicts the location and nature of the seisogenic faults or active faults and the epicenter of earthquakes with M ≥ 5 from 1960 to 2019. The zonation map shows the mean values of peak ground acceleration (PGA) with 10% probability of being exceeded in 50 years. The two maps can not only be used in the research of active faults and seismic risks in Western Asia, but also will be applied to the seismic safety evaluation for infrastructure construction.
LIU Zhicheng
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).
GE Yong, LI Qiangzi, DONG Wen
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.
GE Yong, LING Feng
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.
GE Yong, LI Qiangzi, DONG Wen
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 disaster risk and vulnerability of storm surge in each unit are extracted and calculated using100 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit.At the same time, the data set includes the corresponding risk index, exposure index and vulnerability assessment results.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).
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
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 humidity index is one of the indicators to characterize soil drought and can directly reflect the status of crops' available water.
GE Yong, WU Hua
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.
DONG Wen, University of hawaii sealevel center （UHSLC）
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
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