Log and image are unique and important primary data of field research, and also an important part of scientific data. In order to further standardize the collection, collation, warehousing and exchange of expedition logs and image data of the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau, and ensure the operability, organization and standardization of the warehousing of expedition logs and image data, this technical specification is formulated. This specification provides procedures and methods for the collection and collation of investigation logs and image data, including work preparation, field investigation, data collation and other requirements, in order to better serve the storage of investigation data. This specification applies to the collation and storage of log and image data of field investigations organized by the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau, and other relevant data formed by field investigations can also be carried out by reference to this technical specification.
YANG Yaping
This data set contains information on natural disasters in Qinghai over nearly 50 years, including the times, places and the consequences of natural disasters such as droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms, pest plagues, rats, and geological disasters. Qinghai Province is located in the northeastern part of the Tibetan Plateau and has a total area of 720,000 square kilometers. Numerous rivers, glaciers and lakes lie in the province. Because two mother rivers of the Chinese nation, the Yangtze River and the Yellow River, and the famous international river—the Lancang River—originated here, it is known as the "Chinese Water Tower"; there are 335,000 square meters of available grasslands in the province, and the natural pasture area ranks fourth in the country after those of Inner Mongolia, Tibet and Xinjiang. There are various types of grasslands, abundant grassland resources, and 113 families, 564 genera and 2100 species of vascular plants, which grow and develop under the unique climatic condition of the Tibetan Plateau and strongly represent the characteristics of the plateau ecological environment. As the main part of the Tibetan Plateau, Qinghai Province is one of the centers of the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the study of climate and ecological environment in the international field of sciences and technology. The terrain and land-forms in Qinghai are complex, with interlaced mountains, valleys and basins, widely distributed snow and glaciers, the Gobi and other deserts and grassland. Complex terrain conditions, high altitudes and harsh climatic conditions make Qinghai a province with frequent meteorological disasters. The main meteorological disasters include droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms. The data are extracted from the Qinghai Volume of Chinese Meteorological Disaster Dictionary, with manual entry, summarizing and proofreading.
Qinghai Provincial Bureau of Statistics
This data set collates and collects various geological hazard points, topographic relief, landslide, elevation, land use and other influencing factors, with a resolution of 90m. The above factor layers and sample data are used to obtain the risk grade map with random forest. Data sets / atlas are mainly generated by: raw data (investigation, collection and purchase, etc.), processing data (calculation and simulation). The data source is downloaded from the open source website with an accuracy of 90m. The data is downloaded from the open source website and calculated in spider with their own random forest code. The training set is 80% and the test set is 20%. Open it with a computer that can run ArcGIS.
YANG Wentao
Based on the concept of Height Above Nearest Drainage ( HAND ) derived from the international digital elevation model, the HAND model was used to identify the flooded area, and the spatial distribution of flood hazard level in the flood area of the study area was established. Flood hazard in the study area is divided into 1-5 grades, of which 5 represent very high risk, 4 represent high risk, 3 represent medium risk, 2 represent low risk, 1 represent very low risk.
CHEN Bo
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
Freezing-thawing disaster is the frost heaving and thawing settling caused by the change of thermal and mechanical stability of frozen soil, as well as the geological disasters caused by it, such as frost heaving hillock, ice cone, thermal thawing slump, thermal thawing subsidence, thawing mud flow, etc. In order to reveal the regional risk characteristics of freezing-thawing disasters around The Himalayas and in Asia's water tower region, it is very important to carry out the risk assessment of the factors causing the freezing-thawing disasters around the Himalayas and Asia's water tower region.The risk assessment of the risk factors of freezing-thawing disaster is mainly based on the climate, geography, environment and other factors of the evaluation area, and the geological conditions of the area are considered as the main factors of the risk assessment, and the risk assessment of the risk factors is graded.
ZHANG Guoming
This data set includes 1:1 million historical mountain flood disaster data in the Himalayas, 1:1 million mountain flood prevention and control area distribution data in the Himalayas, 1:1 million mountain flood zoning distribution data in the Himalayas, and 1:1 million key prevention and control area distribution data in the Himalayas. All data are based on the results of national mountain flood disaster investigation and evaluation, and obtain the information of historical mountain flood disaster occurrence time, location, disaster type, cause, longitude, latitude, quantity, distribution and number of victims in the study area, as well as the distribution data of mountain flood zoning, prevention and control area and key prevention and control areas in the study area, so as to form the distribution data set of historical mountain flood disaster in the Himalayas.
WANG Zhonggen
The photos contain the disaster spots and work photos of the scientific research every day (Juue 15th, 2021-July 24 6th, 2021), and the questionnaire of each disaster spot (including landslide, collapse, debris flow, etc.). The disaster spots recorded every day are marked on the map, converted into KMZ format, and the distribution of disaster spots in the scientific research area is analyzed on GIS. The distribution of disaster points shows that rainfall-induced landslide, debirs flow and rockfall, flash flood disaster points are major located along along the eastern route and the intensity are dense in there. In addition, the transportation infrastructure and popultion are relative densely distributed along the earstern route, may be resulte in high comprehensively disaster risk.In the western route, there are major distributed sand disaster, also mass movement disasters such as landslde and rockfall. The above pictures, vedios, disaster point map and route map are recorded. The above data are intuitive data for researching scientific expeditions, also are the key input data and examine data. In addition, they are fundamental significance for objectively judging the types and distribution of disasters in the scientific expedition area, as well as disaster prevention and mitigation measures.
ZHANG Zhengtao
To fully implement the measures for the administration of the scientific data for the "government budget funding for formation of the scientific data shall, in accordance with the open as normal, not open for exception principle, by the competent department to organize the formulation of scientific data resources directory, the directory should be timely access to the national data sharing and data exchange platform, open to society and relevant departments to share, In the spirit of unimpeded military-civilian sharing channels for scientific data, and in accordance with the relevant requirements of relevant exchange standards and specifications, this code is now established for the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau. The main drafting unit of this code: Institute of Geographic Sciences and Natural Resources Research, CAS. Main draftsman of this specification: project group 9 of the second Comprehensive Scientific investigation and research Mission of qinghai-Tibet Plateau.
YANG Yaping
This data includes: 30m mountain flood comprehensive risk data, 30m mountain flood risk data, 30m mountain flood disaster bearing body data and 30m mountain flood vulnerability distribution data in the Himalayas. Based on the results of national investigation and evaluation of mountain flood disasters, the distribution of comprehensive risk indicators of mountain flood disasters in the study area, the distribution of mountain flood risk indicators in each administrative village, the distribution of mountain flood disaster bearing body indicators and the distribution of mountain flood vulnerability indicators are obtained, forming the comprehensive risk distribution data of mountain flood disasters in the Himalayas. This data is helpful to analyze the spatial variation characteristics and distribution law of mountain flood disaster. The zoning of mountain flood disaster risk plays a guiding role in the flood control management and deployment of flood control emergency departments.
WANG Zhonggen
Flood risk assessment data along Sichuan Tibet railway, including natural indicators, risk, vulnerability and risk assessment data. Data source: obtained from the earth big data science and Engineering website; Calculated and obtained according to DEM downloaded by USGS. Processing method: the maximum 24h precipitation with five-year return period is obtained by calculating the frequency according to the annual maximum 24h precipitation sequence in the assessment area; The river network index is obtained by cutting and processing the level 6 water network of Haihe River version in the assessment area; The risk is obtained by calculating the maximum 24h precipitation once in five years and the assignment of river network index; Vulnerability is obtained by weighting the data of population density, transportation cost and total GDP; Risk data is calculated based on risk and vulnerability weighting. Formulate digital processing operation specifications. In the process of processing, the operators are required to strictly abide by the operation specifications, and a special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation all meet the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping, and the quality is excellent and reliable.
WANG Zhonggen
1) In recent years, with the global climate change, coupled with the internal dynamic disturbance and strong tectonic uplift, mountain disasters and floods in the Qinghai Tibet Plateau occur frequently, which poses a great threat to rural settlements in mountainous areas. Village disaster vulnerability and comprehensive risk prevention ability have gradually become an important topic of rural disaster prevention and reduction. 2) This data comes from a random questionnaire survey conducted from June to September 2021 in tuomai village, Lang Town, Lang County, Nyingchi City, Bangna village, Linzhi Town, Bayi District, xuewaka village, Gu township, Bomi County, Beibeng village, Beibeng Township, Motuo County, Xueni village, zhuwagen Town, Chayu County, Ranwu village, Ranwu Town, Basu County, Qamdo city and Zhuba village, Baima Town, Basu county, And the respondents are mainly adults familiar with family conditions. 3) Based on the principles of scientificity, applicability, feasibility, typicality and specificity, the questionnaire is designed for the individual villages around the Himalayas on the Qinghai Tibet Plateau. In order to ensure the reliability and validity of the design content of the questionnaire, a pre survey was conducted before the formal survey to further modify and improve the questionnaire. Before the formal start of the questionnaire survey, the investigators were explained the contents of the questionnaire and trained in survey skills. 4) A total of 231 questionnaires were completed, including 35 in tuomai village, 24 in Bangna village, 21 in xuewaka village, 38 in Beibeng village, 16 in Xueni village, 72 in Ranwu village and 25 in Zhuba village. The effective rate of the questionnaire was 98.6%.
ZHOU Qiang, CHEN Ruishan , LIU Fenggui, LI Wanzhi , LI Shengmei , CHEN Qiong, GAO Haixin
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）
"Disaster data for countries along the belt and road, mainly from the global disaster database.The records information of disaster database are from the United Nations, government and non-governmental organizations, research institutions and the media. It's documented in detail such as the country where the disaster occurred, the type of disaster, the date of the disaster, the number of deaths and the estimated economic losses. This study extracts the natural disaster records of the countries along the One Belt And One Road line one by one from the database, and finally forms the disaster database of 9 major disasters of the 65 countries. The natural disaster records collected can be roughly divided into nine categories, including: floods, landslides, extreme temperatures, storms, droughts, forest fires, earthquakes, mass movements and volcanic activities. From 1900 to 2018, a total of 5,479 disaster records were recorded in countries along the One Belt And One Road. From 2000 to 2015, there were 2,673 disaster records. On this basis, the natural disasters of the countries along the belt and road are investigated from four aspects, including disaster frequency, death toll, disaster-affected population and economic loss assessment. Overall, since 1900, a total of 5479 natural disasters have occurred in countries along the One Belt And One Road, resulting in about 19 million deaths and economic losses of about 950 billion us dollars. Among them, the most frequent occurrence is flood and storm; the biggest economic losses are floods and earthquakes; the most affected people are flood and drought; drought and flooding are the leading causes of death
YIN Jun
1)The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015, and the grid resolution is 300m. 2) China soil erosion prediction model (CSLE) was used to calculate the soil erosion amount of more than 4,000 investigation units on the Qinghai-Tibet Plateau. Soil erosion was interpolated according to land use on Qinghai-Tibet Plateau. According to the soil erosion classification standard, the soil erosion intensity map of Qinghai-Tibet Plateau was obtained. 3) By comparing the differences of three-stage soil erosion intensity data, it conforms to the actual change law and the data quality is good. 4) The data of soil erosion intensity are of great significance to the study of soil erosion in the Qinghai-Tibet Plateau and the sustainable development of local ecosystems. In the attribute table, "Value" represents the erosion intensity level, from 1 to 6, the value represents slight, mild, moderate, intense, extremely intense and severe. "BL" represents the percentage of echa erosion intensity in the total area.
ZHANG Wenbo
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
This data uses a landslide hazard risk assessment model consisting of four modules: landslide hazard causative factors, landslide susceptibility model, exposed population and population casualty rate. The module of hazard-causing factors includes DEM, slope, rainfall, temperature, snow cover, GDP, and vegetation cover factors. The landslide hazard susceptibility model is a statistical analysis using a logistic regression model to obtain landslide susceptibility probability values. The population exposure module uses the landslide susceptibility values overlaid with population data. The population casualty rate module is based on the ratio of historical landslide casualties to the population exposed to landslides during the same period. Finally, by substituting the 2020 population data, the exposed population under different levels of landslide hazard susceptibility is calculated and multiplied with the historical period landslide hazard population casualty rate to assessIntegrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions
WANG Ying
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 relative moisture index is the difference between the precipitation in a certain period of time and the potential evapotranspiration in the same period and then divided by the potential evapotranspiration in the same period.The precipitation data comes from the downscaling of the TRMM/GPM satellite precipitation data, and the potential evapotranspiration is estimated using the Thornthwaite method. For detailed algorithm, please refer to "National Standard for Meteorological Drought of China" (GB/T 20481-2017). The data only covers 34 key node areas along the Belt and Road.
WU Hua
The sand drift potential data sets of Central Asia in 2017 is in tif format. It covers five countries in Central Asia, including Uzbekistan, Tajikistan, Kyrgyzstan, Kazakhstan and Turkmenistan. The sand drift potential is absolutely drift potential, that is, the sum of the flux in all directions, regardless of the direction of the potential. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The temporal resolution is month, the spatial resolution is 0.25°, and the time range is 2017. This data set can be used as an important reference data for sand storm disaster assessment.
GAO Xin
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
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