The Slope Length and Stepness Factor (LS) dataset of Pan-third pole 20 country is calculated based on the free accessed 1 arc second resolution SRTM digital elevation data (Shuttle Radar Topography Mission, SRTM; the website is http://srtm.csi.cgiar.org）. After the pre-processing such as pseudo edge removal, filtering and noise removal, the LS factor with 7.5 arc second resolution was calculated with the LS factor algorithm in CSLE model and the LS calculation tool (LS_tool) developed in this project. The LS factor data of Pan-third pole 20 countries is the fundamental data for soil erosion rate calculation based on CSLE, and it also the fuandatmental data for analyzing the erosion topographic characteristics of Pan third pole 20 countries (such as macro distribution and micro pattern of elevation, slope and slope) . The dataset if of great importance for the analysis of geomorphic characteristics and geological disaster characteristics in this area.
1)The datase includes a 30-year (1986-2015) average rainfall erosivity raster data for 20 countries in key regions, with a spatial resolution of 300 meters. 2）The 0.5°×0.5° grid daily rainfall data generated by the Climate Prediction Center (CPC) based on global site data was used to calculate the rainfall erosivity R factor of 20 countries in key regions. 3）The daily rainfall data of 2358 weather stations nationwide from China Meteorological Administration from 1986 to 2015 was used to calculate the R value, and the R value calculated by establishing the CPC data source was rechecked and verified. It is found that the R value calculated by the CPC data system was low, and then it was revised, and the final data obtained was of good quality. 4）Rainfall erosivity R factor can be used as the driving factor of the CSLE model, and the data is of great significance for the simulation of soil erosion in 20 countries in key regions and the analysis of its spatial pattern.
1)The dataset includes the grid data of vegetation coverage and biological measure factor B of 20 countries in key regions, with a spatial resolution of 300 meters. 2）The basic data source is the MODIS MOD13Q1 product from 2014 to 2016 with a spatial resolution of 250 m. Based on this, a 24-half month average vegetation coverage raster data during a 3 year period was calculated, and then the soil loss ratio was calculated according to the land type. The, the 24- half months rainfall erosivity was further weighted and averaged to obtain a grid map of vegetation coverage and biological measures B factor. 3）MOD13Q1 remote sensing vegetation data was processed by cloud removal. The calculated B factor was statistically analyzed by landuse types and rationality analyzed. The final data quality is good. 4）The factor B of vegetation coverage and biological measures reflects the impact of surface land use/vegetation coverage on soil erosion, and is of great significance for soil erosion simulation and spatial pattern analysis in 20 key regions.
According to Ya'an Qamdo, Qamdo Nyingchi, Nyingchi Lhasa and other sections, carry out field investigation on debris flow within 10km along the new Sichuan Tibet railway line and Sichuan Tibet highway, fill in debris flow questionnaire and take photos. Based on the investigated debris flow data, the basic data are provided for the pregnant disaster background characteristics and distribution law of Sichuan Tibet traffic corridor. At the same time, the hazard modes of debris flow and the hazard modes to highway, railway and other traffic lines are investigated in detail; Furthermore, debris flow risk, vulnerability and risk assessment shall be carried out along the new Sichuan Tibet railway line at different scales such as regional scale, key sections and typical disasters, so as to provide support for the route selection of Sichuan Tibet railway.
The distribution data of debris flow in Sichuan Tibet transportation corridor includes two layers, one is the point layer, which mainly marks the location of debris flow gully, the other is the area layer, which is the drainage area of debris flow gully. The source of the data is the combination of remote sensing identification and ground investigation. Firstly, the remote sensing image is used to interpret the location of the debris flow gully in the region, and then the ground investigation of the debris flow gully is carried out along the Sichuan Tibet railway and Sichuan Tibet highway. The remote sensing interpretation data is verified, and finally the more reliable debris flow distribution data is obtained. The data can be used to analyze the distribution of debris flow in Sichuan Tibet transportation corridor, multi-scale debris flow risk assessment and risk assessment.
1) Data content: ① indoor static tension video, infrared monitoring video and static tension analysis data chart of giant NPR anchor cable; ② Indoor dynamic impact video of giant NPR anchor cable; 2) Data sources: the static tension process, infrared monitoring and dynamic impact process of indoor giant NPR anchor cable were recorded, and the static tension data were imported into Origin Software for data processing and analysis; 4) Through the indoor static tension and dynamic impact tests on the giant NPR anchor cable, the supernormal mechanical properties of the giant NPR anchor cable are obtained, which can provide supporting materials for the prevention and control of slope disasters in fault zone, early warning monitoring and cross fault tunnel prevention.
As a typical representative of mountainous areas in western China, Hengduan Mountain Area has become one of the areas with frequent and most serious geological disasters, which has brought great threats to rural settlements located in mountainous areas. Therefore, the vulnerability of village disasters and comprehensive risk prevention capability have gradually become an important topic of disaster prevention and disaster mitigation in rural areas. This data is from a random questionnaire survey conducted in Xiamachang Village, Meixing Town, Xiaojin County, Dashiban Village, Huiping Town, Mianning County, Sichuan Province, and Qina Village, Qina Town, Yongsheng County, Yunnan Province, from August to September, 2020. And the interviewees are mainly adults who is familiar with family situations. The design of the questionnaire is based on the principles of scientific nature, applicability, feasibility, typicality and concreteness. And Questionnaire on Disaster Risk Prevention Ability and Social Vulnerability of Villages in the Hengduan Mountain Area is designed for individual villages in the Hengduan Mountain Area. In order to ensure the reliability and validity of the questionnaire, some questionnaire was pre-investigated before the formal investigation, and there were some modification and improvement about the problem founded. Also, before the formal questionnaire survey, the investigators were given an explanation of the questionnaire and the training of the survey skills. 171 questionnaires were completed in this survey. After eliminating 20 invalid questionnaires, 151 valid questionnaires were obtained, including 50 from Xiamachang Village, 39 from Dashiban Village and 62 from Qina Village, respectively. The effective rate of questionnaires was 88.3%.
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.
On the basis of literature and satellite image recognition, this data set has carried out a more detailed field scientific investigation on Sichuan Tibet railway, Sichuan Tibet transportation corridor and the upper reaches of Jinsha River, cataloguing and photographing the observed debris flow disaster chain, landslide disaster chain, typical fault structure points, glacial debris flow disaster chain and large-scale collapse disaster chain; Fill in the survey data form of disaster points in the field scientific examination, sort out and fill in the log files of scientific examination, and complete the distribution map of various types of disaster points. The photos are clear, the contents of the disaster questionnaire are detailed, and the scientific examination log is complete. The field survey photos and data have important reference significance for the future field survey of disaster chain and the comparative study of its future development trend.
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.