Data content: A large database of 1230 worldwide dam cases Data source: through literature search, classification, consolidation and compilation. Data quality description: classify and sort out the historical cases of weir plug dam from two aspects: qualitative description and quantitative description. The qualitative description includes the country, the name of the dam, the formation time, the type of landslide, the inducing factors, the type of dam body, the mechanism of collapse, etc; Quantitative description includes landslide volume, dam volume, dam height, dam length, dam width, barrier lake length, barrier lake volume, barrier dam life, breach depth, breach top width, breach bottom width, breach time, peak flow, casualties, etc.
Data content: empirical formula calculation data of final bottom elevation of dam breach Data source: a large database containing 1230 dam cases around the world based on literature retrieval. Collection method: processing and fitting through Excel data processing software. Data quality description: in order to solve the problem of assigning the final bottom elevation of the dam breach, based on the collected data of dam height and breach depth in the dam database, combined with the classification method of overtopping breach dam body erosion proposed by briaud in 2008, the dams were divided into three types: high, medium and low erosion degrees. Then the dam height and breach depth of the dam plug dam with different erosion degrees were regressed, The empirical formula for the depths of dam breaches with different erosion degrees were also fitted, and then the final bottom elevations of dam breaches were determined.
This data combines the direct economic loss risk assessment results of earthquake and geological disasters. According to the obtained loss assessment results, the study area is divided into nine categories according to the risk level, which are seismic geological low-risk area, geological medium seismic low-risk area, seismic medium geological low-risk area, seismic geological medium risk area, geological high epicenter risk area and seismic high quality low-risk area, Geological high seismic low risk area, seismic high quality low risk area and seismic geological high risk area. The data results of this multi disaster direct economic loss risk assessment provide a basis for the spatial distribution of direct economic losses in the Asian water tower area and the surrounding areas of the Himalayas in the future.
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%.