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
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
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
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
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
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
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
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
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
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.
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.
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.
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
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
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
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
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
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