The data is digitized from a drawing, the map of developmental degree of desertification in Daqinggou, Keerqin (HORQIN) Steppe (1975). The specific information of this map is as follows: * Chief Editor: Zhu Zhenda * Editor: Feng Yusun * Drawer: Feng Yusun, Yao Fafen, Wang Jianhua, Zhao Yanhua, Li Weimin * Mapping unit: Prepared by Desert Research Office, Chinese Academy of Sciences * Publisher: No * Scale: 1: 50000 * Publication time: No * Legend: Gully Dense Forest, Sparse Woods, Brush, Artificial Woodland, Nursery and Vegetable Garden, Grass Land, Dry Farmland (Dry Farmland), Rejected Farmland, Marsh Land, Shifting Snad-Dunes, Semi-Shifting Sand-Dunes, Semi-Fixed Sand-Dunes ), Fixed Sand-Dunes, Water Area, Rice, Residential, Highway 1. File format and naming The data is stored in ESRI Shapefile format, including the following layers: Desertification map of Daqinggou area in Horqin steppe, rivers, swamps, roads, lakes, residential areas 2. Data desertification attribute fields: Type of desertification (Shape), Grassland (Grassland), Woodland (Woodland), Woodland Density (W_density), Farmland (Farmland) 3. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
WANG Jianhua, ZHU Zhenda, FENG Yusun, YAO Fafen
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
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
The data set records the comparison of natural and man-made disaster losses in Qinghai Province from 2011 to 2018. The data is collected from the Department of natural resources of Qinghai Province. The data set contains 12 data tables, which are: comparison of natural and man-made disasters in 2011, natural and man-made disasters in 2012, natural and man-made disasters in 2013, and natural and man-made disasters in 2014 The structure of the data table is the same, including two fields: Field 1: disaster causes Field 2: Proportion It is classified according to human factors and natural factors
Department of Natural Resources of Qinghai Province
Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).
WU Hua
Slope data of economic corridors in Silk Road can reflect the degree of steepness of the surface units of the six major economic corridors, the unit is degree (°). The spatial resolution of the data is 0.016 degrees, which is about 1.8km. The longitude range is 12.09°E-180°, and the latitude range is 10.99°S-90°N. The source is derived from the Global Relief Model built by the National Oceanic and Atmospheric Administration of the United States (NOAA). The range is cut by the border of the Silk Road. This data is one of the basic data necessary to assess the risks of natural disasters (including debris flows, landslides, flash floods, etc.) in the six economic corridors. The application frequency will be high and the prospects will be broad.
ZOU Qiang
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.
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
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 humidity index is the difference between the amount of precipitation in a certain period of time and the potential evapotranspiration over the same period divided by the potential evapotranspiration.
GE Yong, WU Hua
This data is digitized from the `` Map of Desertification Types of Naiman Banner, Kulun Banner, and Horqin Left-wing Rear Banner '' on the drawing.The specific information of this map is as follows: * Chief Editor: Zhu Zhenda * Deputy editors: Liu Shu and Qiu Xingmin * Edit: Feng Yukun * Mapping: Feng Yudi, Zhao Yanhua, Wang Jianhua * Double photo: Li Weimin * Field trip: Zhu Zhenda, Qiu Xingmin, Liu Shu, Shen Jingqi, Feng Yudi, Wang Yimou, Yang Youlin, Yang Taiyun, Wen Zixiang, Liu Yangxuan * Mapping unit: Prepared by Desert Research Office, Chinese Academy of Sciences * Publisher: No * Scale: 1: 300000 * Publication time: No * Legend: undulating undulating sandy loess plain, non-desertified land, grassland, saline-alkali land, woods and shrubs, arable land, mountains, sand dunes File format and naming The data is stored in ESRI Shapefile format, including the following layers: Naiman Banner, Kubian Banner, Kezuohou Banner Desertification Type Map, River, Road, Lake, Railway, Well Spring, Residential Area Data attributes Desertification Grade Vegetation Background Desertified land under development Saline-alkali land Heavily desertified land Woods and shrubs Mountain Strongly developed desertified land Potentially desertified land Lake Non-desertified land Undulating sandy loess plain 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
WANG Jianhua, ZHU Zhenda
The research project on the function and mechanism of sand-fixing afforestation of waste lignin from straw pulp and paper making belongs to the national natural science foundation of China "environment and ecological science in western China" major research program, led by wang hanjie, a researcher of the institute of aviation meteorology and chemical protection, air force equipment research institute. The project ran from January 2004 to December 2006 Remittance data of the project: 1. 2005-08-10 - sand lake - jinsha wan test site image (JPG) 2.2006 field picture of fixed sand test (JPG) 3. Meteorological data of ningxia jinshawan meteorological station (TXT text) Observation data including dry bulb temperature, wet bulb temperature, 0, 5, 10, 15, 20cm ground temperature, evaporation and air temperature were observed at 8:00,14:00 and 20:00 on August 13, 2005 4. Growth data of jinshawan community in ningxia (TXT text) The data of crown diameter and height of four samples are included. 5. Soil water data of jinshawan, ningxia (excel) Soil moisture data of 16 samples at depths of 20CM and 12CM in clear water control area and lignin spraying area by 2 hours in the daytime on August 19, 2005. 6. Soil water data of shahu lake in ningxia (excel) On August 10,11, 2005, soil moisture data of various depths of 10CM,12CM and 20CM were obtained 7. Plant growth data of sand fixation community in shahu, ningxia (excel) Plant growth statistics of 5 sample plots: species name,x,y, base, crown, height, number of plants.
WANG Hanjie
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
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 is digitized from a drawing, the map of developmental degree of desertification in Daqinggou, Keerqin (HORQIN) Steppe (1981). The specific information of this map is as follows: * Chief Editor: Zhu Zhenda * Editor: Feng Yusun * Drawer: Feng Yusun, Yao Fafen, Wang Jianhua, Zhao Yanhua, Li Weimin * Mapping unit: Prepared by Desert Research Office, Chinese Academy of Sciences * Publisher: No * Scale: 1: 50000 * Publication time: No * Legend: Gully Dense Forest, Sparse Woods, Brush, Artificial Woodland, Nursery and Vegetable Garden, Grass Land, Dry Farmland (Dry Farmland), Rejected Farmland, Marsh Land, Shifting Snad-Dunes, Semi-Shifting Sand-Dunes, Semi-Fixed Sand-Dunes ), Fixed Sand-Dunes, Water Area, Rice, Residential, Highway 1. File format and naming The data is stored in ESRI Shapefile format, including the following layers: Desertification map of Daqinggou area in Horqin steppe, rivers, swamps, roads, lakes, residential areas 2. Data desertification attribute fields: Type of desertification (Shape), Grassland (Grassland), Woodland (Woodland), Woodland Density (W_density), Farmland (Farmland) 3. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
WANG Jianhua, ZHU Zhenda, YAO Fafen, FENG Yusun
The dataset contains all individual glacial storage (unit: km3) over the Qinghai-Tibetan Plateau in 1970s and 2000s. It is sourced from the resultant data of the paper entitled "Consolidating the Randolph Glacier Inventory and the Glacier Inventory of China over the Qinghai-Tibetan Plateau and Investigating Glacier Changes Since the mid-20th Century". The first draft of this paper has been completed and is planned to be submitted to Earth System Science Data journal. The baseline glacier inventories in 1970s and 2000s are the Randolph Glacier Inventory 4.0 dataset, and the Glacier Inventory of China, respectively. Based on the individual glacial boundaries extracted from the above-mentioned two datasets, the grid-based bedrock elevation dataset (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m), and the glacier surface elevation obtained by a slope-dependent method, the individual glacier volumes in 1970s and 2000s are then calculated. In addition, the calculated results of individual glacier volumes in this study have been compared and verified with the existent results of several glacier volumes, relevant remote sensing datasets, and the global glacier thickness dataset based on the average of multiple glacier model outputs (https://www.research-collection.ethz.ch/handle/20.500.11850/315707, doi:10.3929/ethz-b-000315707), and the errors in the calculations have also been quantified. The established dataset in this study is expected to provide the data basis for the future regional water resources estimation and glacier ablation-involved researches. Moreover, the acquisition of the data also provides a new idea for the future glacier storage estimation.
WANG Jianhua, ZHU Zhenda, FENG Yusun, YAO Fafen
The data set describes the hypocentre parameters of shallow-focus earthquakes that occurred in the Himalayan-Tibetan Plateau area from 1990 to 2014. Accurate seismic focal depth and focal mechanism solutions can provide an elementary scientific basis for deep Earth deformation and seismogenic structure research. The seismic waveform data are from the IRIS website (http://ds.iris.edu/wilber3/find_event). Teleseismic waveform fitting is used in processing data. The focal depth error is ±3 km. Earthquake number: earthquake number ID for different areas in chronological order Origin Time: mm/dd/yyyy (month/day/year), hh:mm (hour/minute) Earthquake location: longitude, latitude, depth Earthquake magnitude: moment magnitude (Mw) Focal mechanism solution: trend / inclination / inclination angle (strike / dip / slip) Error: the least squares method is used to determine the variance between the theoretical waveform and the observed waveform (misfit) Moho Depth: Moho
BAI Ling
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
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