Vulnerability assessment dataset of hectometre level for 34 key nodes assessment the flood risk of key nodes in the Belt and Road under the extreme precipitation events, in order to provide basis for decision-making for the local government department, at the same time before flood disaster early warning, which may take the disaster prevention and mitigation measures for the precious time, reduce people's lives and property damage brought by the flood. Based on the data of GDP, population, land ues, road density and river density in the Belt and Road, this dataset combined with the methods of spatial analysis of ArcGIS, assigning different weights to each indicator and building assessment 34 key nodes under the condition of extreme precipitation in flood vulnerability level, which was divided into 5 levels by using natural break point method, representing no vulnerability, low vulnerability, middle vulnerability, high vulnerability, extreme high vulnerability, respectively.
GE Yong, LI Qiangzi, LI Yi
Based on the world surface water data (wod) from 1984 to 2018, this data set selects several indexes of precipitation, topography and land use type, and combines with the spatial analysis method in ArcGIS, constructs and evaluates the risk level of flood disaster in 34 key nodes under extreme precipitation conditions. One belt, one road, 34 critical nodes, is evaluated for the risk of flooding in the key areas along the extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters, and to reduce people's lives and property losses caused by floods. Loss.
GE Yong, LI Qiangzi, LI Yi
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).
Based on 100m risk assessment data set and 100m vulnerability assessment data set, this data set respectively gives different weights to the risk and vulnerability (the risk weight is 0.8, and the vulnerability weight is 0.2), and 34 key node 100m risk assessment data sets are obtained by adding. One belt, one road area, is evaluated for flood risk in extreme areas. The data provide basis for local government departments to make decisions, and early warning before flood disasters, so that we can gain valuable time to take measures to prevent and reduce disasters, and to reduce the loss of lives and property of people caused by floods.
GE Yong, LI Qiangzi, LI Yi
The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00，t1960-90_2.e00，t1960-90_3.e00，t1960-90_4.e00，t1960-90_5.e00， t1960-90_6.e00，t1960-90_7.e00，t1960-90_8.e00，t1960-90_9.e00，t1960-90_10.e00， t1960-90_11.e00，t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00，t1991-20_2.e00，t1991-20_3.e00，t1991-20_4.e00，t1991-20_5.e00， t1991-20_6.e00，t1991-20_7.e00，t1991-20_8.e00，t1991-20_9.e00，t1991-20_10.e00， t1991-20_11.e00，t1991-20_12.e00， Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00，p1960-90_2.e00，p1960-90_3.e00，p1960-90_4.e00，p1960-90_5.e00， p1960-90_6.e00，p1960-90_7.e00，p1960-90_8.e00，p1960-90_9.e00，p1960-90_10.e00， p1960-90_11.e00，p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00，p1991-20_2.e00，p1991-20_3.e00，p1991-20_4.e00，p1991-20_5.e00， p1991-20_6.e00，p1991-20_7.e00，p1991-20_8.e00，p1991-20_9.e00，p1991-20_10.e00， p1991-20_11.e00，p1991-20_12.e00， The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.
NCEP/NCAR Reanalysis 1 is an assimilation of data from the past (1948-recent). It was developed by the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP–NCAR) in the US to act as an advanced analysis and prediction system. Most of the data are from the original daily average data of the PSD (Physical Sciences Division). However, the data from 1948 to 1957 are slightly different because these data are conventional (non-Gaussian) grid data. The information published on the official website is generally from 1948 to the present, and the latest information is generally updated every two days. For data on an isostatic surface, the general vertical resolution is 17 layers, from 1000 hPa to 10 hPa. The horizontal resolution is typically 2.5° x 2.5°. The NCEP reanalysis data are systematically comparable among international atmospheric science reanalysis data sets. Compared with the reanalysis data of the European Center, the initial year is earlier, and the latest data updates are more frequent. These two sets of reanalysis data are currently the most widely used data sets in the world. For details of the data, please visit the following website: https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
LUO Dehai, YAO Yao
The dataset of the truck-mounted dual polarized doppler radar observations (time-continuous 10-minute on the 250m×250m horizontal grid) was obtained in the arid region hydrology experiment area from May 20 to Jul. 5, 2008. The observation site (38.73°N, 100.45°E, 1668m) was typical of complex underlying surface and transit zone landscapes. The aim was to explore and retrieve precipitation type and intensity by radar in cold regions, with the precipitation particle drop size analyzer and ground intensive measurements occurring simultaneously, thus making it possible to produce a high resolution precipitation dataset. The 714XDP X-band dual-linear polarization Doppler weather radar was with a horizontal resolution of 150 m, an azimuth resolution of 1, VCP from 10-22 layers and the scanning cycle 10 minutes. ZH, ZDR and KDP could be acquired together. For more details, please refer to Readme file.
The dataset of the drop spectrometer observations was obtained at an interval of 30 seconds in the cold region hydrology experimental area from Mar. 14 to Apr. 14, 2008. The site was chosen in A'rou (N39.06°, E100.44°, 3002m), Qilian county, Qinghai province. The data mainly included the raindrop grain size and the terminal velocity. Besides, dual polarized radar (X-band) parameters such as ZDR and KDR could be further developed based on those data. The observation was carried out within an area of 5400mm^2; the liquid grain diameter was from 0.2-5mm, and the solid grain diameter was from 0.2-25mm.
The dataset of the truck-mounted dual polarized doppler radar observations (time-continuous 10-minute on the 250m×250m horizontal grid) was obtained in A'rou (39.06°N, 100.44°E, 3002m, typical of complex terrain in high altitude), Qilian county in the upper stream of Heihe river from Mar. 14 to Apr. 14, 2008. The aim was to explore and retrieve precipitation type and intensity by radar in cold regions, with the precipitation particle drop size analyzer and ground intensive measurements occurring simultaneously, thus making it possible to produce a high resolution precipitation dataset. The 714XDP X-band dual-linear polarization Doppler weather radar was with a horizontal resolution of 150 m, an azimuth resolution of 1, VCP from 10-22 layers and the scanning cycle 10 minutes. ZH, ZDR and KDP could be acquired together. For more details, please refer to Readme file.
The dataset of the drop spectrometer (PARSIVEL) observations was obtained at an interval of 30 seconds in the arid region hydrology experiment area from May 18 to Jul. 5, 2008. The site was chosen in Xiaoman township (38.86°N, 100.41°E, 1515m), Ganzhou district, Zhangye city, Gansu province. The data mainly included the raindrop grain size and the terminal velocity. Besides, dual polarized radar (X-band) parameters such as ZDR and KDR could be further developed based on those data. The sampling area of PARSIVEL was 5400mm^2; the liquid grain diameter was from 0.2-5mm, and the solid grain diameter was from 0.2-25mm.