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
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
Apparent temperature refers to the degree of heat and cold that the human body feels, which is affected by temperature, wind speed and humidity. The spatial scope of the data covers 34 key nodes in the pan-third pole region (Vientiane, Yangon, Kolkata, Warsaw, Karachi, Yekaterinburg, Chittagong, Tashkent, etc.). The spatial resolution is 100m, and the temporal resolution is year. Processing process: Based on the monitoring data of the meteorological station, calculate the apperant temperature based on the Humidex index, and then use the temperature correction method based on elevation correction to obtain 1km gridded data of the entire area, and downscale it to 100m. The heat wave risk dataset mainly uses intensity as the evaluation index. The spatial range and spatial resolution are consistent with the somatosensory temperature data set, and the temporal resolution is years. The criterion for judging the heat wave is: the weather process in which the somatosensory temperature exceeds 29℃ for three consecutive days is judged to be a high-temperature heat wave.
YANG Fei, WU Xilin, YIN Cong
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 percentage of precipitation anomaly is the percentage of the precipitation between a certain period of time and the average climate precipitation of the same period divided by the average climate precipitation of the same period.Based on the daily rainfall data of GPM IMERG Final Run(GPM), this data set calculates the precipitation of the corresponding region, adopts the evaluation index of precipitation anomaly percentage grade, and analyzes the distribution characteristics of drought of different grades. The data area is 34 key nodes of the pan-third pole (Abbas, Astana, Colombo, Gwadar, Mamba, Tehran, Vientiane, etc.).
WU Hua
The pan third pole historical extreme precipitation data set includes 2000-2018 extreme precipitation identification data. One belt, one road, was used to assess the rainfall in the important area along the GPM IMERG Final Run (GPM) daily rainfall. The extreme precipitation threshold of 34 important nodes was evaluated by percentile method. The daily precipitation period was identified by the calculated threshold, and the surface inundation area was produced on the basis of extreme precipitation. The data range mainly includes 34 key nodes of Pan third pole (Vientiane, Alexandria, Yangon, Calcutta, Warsaw, Karachi, yekajerinburg, Chittagong, Djibouti, etc.) The data set can provide the basis for local government decision-making, so as to correctly identify extreme precipitation and reduce the loss of life and property caused by extreme precipitation.
HE Yufeng
Data set of surface inundation caused by historical extreme precipitation evaluated the surface inundation range of One Belt And One Road key areas under extreme precipitation, providing a basis and reference for the decision-making of local government departments, so as to give early warning before the occurrence of extreme precipitation and reduce the loss of life and property caused by extreme precipitation.This data set to the extreme precipitation threshold set "and" the extreme precipitation recognition "as the foundation, to confirm the extreme precipitation time node and the area, and then to NASA's web site to download the submerged range products corresponding to the time and region, combining ArcGIS spatial analysis was used to connect the above data, build the data sets of historical extreme precipitation caused surface submerged range for 34 key nodes. The data mainly includes 34 key nodes (Vientiane, China-Myanmar oil and gas pipeline, China-Laos Thai-Cambodia railway, Alexandria, Yangon, Kwantan, Kolkata, Warsaw, Karachi, Yekaterinburg, Yekaterinburg and other regions).
WU Hua
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. The key nodes data set only contains 11 nodes which have risks (Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).
DONG Wen
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 using100 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.At the same time, the data set includes the corresponding risk index, exposure index and vulnerability assessment results.The key nodes data set only contains 11 nodes which have risks ((Chittagong port, Bangladesh; Kyaukpyu Port, Myanmar; Kolkata, India; Yangon Port, Myanmar; Karachi, Pakistan; Dhaka, Bangladesh; Mumbai, India; Hambantota Port, Sri Lanka; Bangkok, Thailand; China-Myanmar Oil and Gas Pipeline; Jakarta-Bandung High-speed Railway).
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 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
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
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
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 5 m in the year of 2017 in Tibet. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation results and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 30 m in the year of 2017 in Qinhai. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
ZHANG Wenbo
1) The data includes the soil erosion modulus of 22 watersheds with a resolution of 2.5 m in the year of 2019 in the Xinjiang Uygur Autonomous Region. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 22 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 22 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
YANG Qinke
1) The data includes the soil erosion modulus of 18 watersheds with a resolution of 5 m in the year of 2017 in Thailand. 2) Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 18 watersheds of Thailand respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 18 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar region and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
YANG Qinke
1) The data is the layout of sample survey units in 65 countries of Pan-Third Polar region and western China. 2)Sample survey units were set in the pan-third pole region (70 °N-10 °S, 180 °E-180 ° W) . No samplings points were selected in the region with latitude >70 °. In the region wiht latitude of 60 ° -70 ° , sample survey units were selected in cells of 0.5 ° latitude ×1 ° Longitude (about 55km×55km-55km×38km). In the area with latitude of 40°-60°, sample survey units were selected in cells of 0.5 ° latitude×0.75 ° longitude (about 55km×63km-55km×42km). In the area with latitude <40°, sample survey units were selected in cells of 0.5 ° latitude × 0.5 ° longitude. In the Qinghai-Tibet Plateau, sample survey units were selected in cells of 0.25 ° latitude × 0.25 ° longitude. Thesample survey units deployed in the first national water conservancy survey for soil and water conservation were used in current project in five provinces including Xinjiang, Qinghai, Gansu, Sichuan and Yunnan in western China. The total number of sample survey units is 29,651, of which, 4052 are in the Qinghai-Tibet Plateau, 8771 in the western China, and 16,828 in countries outside of China. 3) The selected sample survey units is well distributed and the data quality is good.4) the layout map of sample survey units is of great significance for the study of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the area along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
WEI Xin
1) The dataset includes the raster data of soil erosion intensity in Pan-Third Pole 65 countries.2) The data of soil erosion intensity are obtained by using the Chinese soil loss equation (CSLE). The formula of soil erosion prediction model includes rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover and biological measure factor, engineering measure factor and tillage measure factor. Rainfall erodibility factors are calculated from the daily rainfall data by the US Climate Prdiction Center (CPC); soil erodibility factors are calculated by 250 m soil grid data; engineering measure factors are calculated based on vegetation cover, land use and rainfall erosivity ratio; tillage measure factors haven't been considered yet, and the default value is 1; slope length factors and slope factors are obtained by resampling after calculating 30 m elevation data; vegetation coverage and biological measures factors are obtained by combining fractional vegetation cover with land use data and rainfall erodibility proportionometer. The fractional vegetation cover is calculated by MODIS vegetation index products through pixel dichotomy. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar 65 countries and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
ZHANG Wenbo
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 34 key nodes, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using handred meters grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, distance of offshore line, etc. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method. Finally, the storm surge danger index is normalized to 0-1, which can be used to evaluate the danger level of storm surge in each assessment unit. The key nodes data set only contains 11 nodes which have risks.
GE Yong, LI Qiangzi, DONG Wen
The extreme drought damage historical events data of the 34 key areas along One Belt One Road were collected from Internet. First, a Web crawler was coded by python language. Using several key words about extreme drought damage, web pages were then collected by Google and Baidu search engine. Last, important information about the extreme drought 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 drought in the 34 key areas along One Belt One Road.
GE Yong, LING Feng
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