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.
ZHANG Wenbo
The accuracy of tropical cyclone (tropical storm) track forecasting improved by nearly 50% for lead times of 24–72 h since 1990s. Over the same period forecasting of tropical cyclone intensity showed only limited improvement. Given the limited prediction skill of models of tropical cyclone intensity based on environmental properties, there have been a wealth of studies of the role of internal dynamical processes of tropical cyclones, which are largely linked to precipitation properties and convective processes. The release of latent heat by convection in the inner core of a tropical cyclone is considered crucial to tropical cyclone intensification. 16-year satellite-based precipitation, and clouds top infrared brightness temperature were used to explore the relationship between precipitation, convective cloud, and tropical cyclone intensity change. The 6-hourly TC centers were linearly interpolated to give the hourly and half hourly tropical cyclone center positions, to match the temporal resolution of the precipitation and clouds top infrared brightness temperature. More precipitation is found as storms intensify, while tropical cyclone 24 h future intensity change is closely connected with very deep convective clouds with IR BT < 208 K. Intensifying tropical cyclones follow the occurrence of colder clouds with IR BT < 208 K with greater areal extents. As an indicator of very deep convective clouds, IR BT < 208 K is suggested to be a good predictor of tropical cyclone intensity change（Ruan&Wu，2018，GRL）. The properties of the satellite-based precipitation, and clouds top infrared brightness temperature are therefore suggested to be important measurements to study tropical cyclone intensity, intensity change and their underlying mechanisms. The high resolution of the satellite-based precipitation (3h), and cloud top infrared brightness temperature (half hour) datasets also makes them possible to be used to study tropical cyclone variability associated with diurnal cycle.
WU Qiaoyan
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
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
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
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 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
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
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
Global Tropical cyclone (TC) best track data already exist as individual storm tracks at other agencies. The intent of the IBTrACS project is to overcome data availability issues. This was achieved by working directly with all the Regional Specialized Meteorological Centers and other international centers and individuals to create a global best track dataset, merging storm information from multiple centers into one product and archiving the data for public use. The World Meteorological Organization Tropical Cyclone Programme has endorsed IBTrACS as an official archiving and distribution resource for tropical cyclone best track data. The IBTrACS project: contains the most complete global set of historical tropical cyclones available, combines information from numerous tropical cyclone datasets, simplifies inter-agency comparisons by providing storm data from multiple sources in one place, provides data in popular formats to facilitate analysis and checks the quality of storm inventories, positions, pressures, and wind speeds, passing the information on to the user. The primary intent of IBTrACS is to support scientific research efforts.
GE Yong, LI Qiangzi, DONG Wen
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
This dataset mainly includes the spatial distribution of global SPEI in 1218 in 2018, the global drought intensity in 2018, and the anomalies of precipitation, land surface temperature, 0-10 cm soil moisture and the past 10 years (2009-2018); The flat index method, the maximum value synthesis method and the trend analysis method calculate the global drought intensity and the main meteorological factor anomaly data for 2018. The data time scale is 2018-01-01 to 2018-12-31, and the spatial resolution is 0.5 degree. The data can provide a scientific reference for the analysis of global drought distribution and drought assessment in 2018.
TIAN Feng, WU Jianjun, ZHOU Hongmin
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