"China's surface climate data daily value data set (V3.0)" contains 699 benchmarks and basic weather stations in China. Since January 1951, the station's air pressure, temperature, precipitation, evaporation, relative humidity, wind direction and wind speed, and sunshine hours. The number and the daily value data of the 0cm geothermal element. After the quality control of the data, the quality and integrity of each factor data from 1951 to 2010 is significantly improved compared with the similar data products released in the past. The actual rate of each factor data is generally above 99%, and the accuracy of the data is close. 100%. China Earth International Exchange Station Climate Data Daily Value Dataset (V3.0), mainly based on the ground-based meteorological data construction project archived "1951-2010 China National Ground Station data corrected monthly report data file (A0/A1/ A) The basic data set was developed. This data can provide a variety of basic drive data for other scientific research.
National Meteorological Information Center
The road data of 34 key areas along the Belt and Road is first collected from the Internet and then re-processed. Road data can be obtained from the OpenStreetMap open source wiki map. OpenStreetMap is a project designed to create and provide free geographic data (such as street maps) to anyone. First, we download the road data with the country where the key area along the One Belt One Road is located, then clip and extract according to the area, and then calculate the road length in each unit to obtain. Based on OpenStreetMap, it is finally integrated into a road length infrastructure element data product. The road length data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.
GE Yong, LING Feng
This data set includes the monthly synthetic 30 m × 30 m surface Lai products in Qilian Mountain Area in 2019. The maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface and calculate the Lai by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
The integration dataset of Tibetan Plateau boundary includes: TPBoundary_2500m：Based on ETOPO5 Global Surface Relief, ENVI+IDL is used to extract the longitude of the Tibetan Plateau (65~105) and the altitude of 2500 meters above the latitude (20~45); TPBoundary_3000m：Based on ETOPO5 Global Surface Relief, ENVI+IDL is used to extract the longitude of the Tibetan Plateau (65~105) and the altitude of 3000 meters above the latitude (20~45); TPBoundary_HF (High Frequency):Li Bingyuan (1987) has conducted a systematic discussion on the principle and specific boundary of determining the scope of the Qinghai-Tibet Plateau. From the perspective of the formation and basic characteristics of plateau geomorphology, Based on the geomorphological features, the plateau surface and its altitude, and considering the integrity of the mountain as the basic principle for determining the plateau range.Zhang Yili (2002) according to the results of new research in related fields and years of field practice, demonstration principles to determine the scope and boundaries of the Tibetan Plateau, Based on the information technology method, the location and boundary position of the Qinghai-Tibet Plateau are accurately located and quantitatively analyzed. It is concluded that the Qinghai-Tibet Plateau is partly in the Pamir Plateau in the west, the Hengduan Mountains in the east, the southern margin of the Himalayas in the south, and the Kunlun Mountains in the north. Mountain - north side of Qilian Mountain. On April 14, 2017, the Ministry of Civil Affairs of the People's Republic of China issued the "Announcement on Supplementing the Public Use of Place Names in the Southern Region of Tibet (First Batch)", adding Wujianling, Mirage, Qu Dengbu, and Mechuca 6 places in southern Tibet such as Baimingla Mountain Pass and Namkam;. TPBoundary_rectangle：According to the range Lon (63~105E) & Lat (20~45N), The data is projected using latitude and longitude WGS84.. Project source: national natural science foundation of China (41571068,41301063) Spatial range and projection mode of data: elevation greater than 2500m, WGS84 projection As the basic data, the boundary of qinghai-tibet plateau can be used as a reference for all kinds of geoscientific research on Qinghai-Tibet Plateau.
ZHANG Yili, REN Huixia, PAN Xiaoduo
This dataset includes stable carbon and oxygen isotopes of carbonates in a 180 m-long sediment core retrieved from Lop Nor, Tarim Basin. Sedimentary carbon and oxygen isotopes from carbonates are two of the most commonly used proxies in paleoclimatic studies, as they have the potential to record past variations in hydrology and vegetation. The sediment samples were grounded and sieved through a 100 mesh screen, and then directly analyzed using an isotope ratio mass spectrometer (MAT-252) with an automated carbonate preparation device (Kiel Ⅱ). Typical analytical errors are within ±0.06‰ and ±0.08‰ for carbon isotope and oxygen isotope, respectively. Based on the high-resolution stable carbon and oxygen isotope data of core Lop Nor, the evolution history of arid environment in the Taklimakan Desert since the Pleistocene can be reconstructed, allowing further exploring of trends, variability and mechanisms of regional climate change. Field photos dataset of the Tibetan Plateau include photos of the stratigraphic profiles.
The meter resolution remote sensing image data of hanbantota area is composed of data fusion and splicing of different satellites. Multispectral remote sensing images with resolution between 0.5 m and 1 m from 2018 to 2019 are selected, and cloud free data with similar time are selected, and the result data set is formed by cutting and splicing according to the research area. The spatial resolution of the data is about 0.6 meters. The data is mainly used to study the high-precision extraction of disaster bearing body elements, such as port facilities, roads and so on. The extracted thematic elements will be used as the basic data of storm surge exposure and vulnerability analysis.
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
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.
This data set includes the monthly composite 30 m × 30 m surface vegetation index products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
This dataset contains the flux measurements from the mixed forest station eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (101.1335° E, 41.9903° N) was located in the Sidaoqiao County, in Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 874 m. The EC was installed at a height of 3.2 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during February 7 to 11, 2018 were missing due to the power loss. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
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 degree of opening to the outside world refers to the degree of opening to the outside world of a country or region's economy, which is embodied in the degree of opening to the outside world of the market, usually including the amount of import and export, the use of foreign capital, the level of tariff, the convenience of customs clearance, free trade agreements, market access, capital exchange, intellectual property protection, etc. The data are one belt, one road, 64 countries, including the net inflow of foreign direct investment (US $100 million), total import (US $100 million) and total export volume (US $100 million). Data sources include the world bank, the United Nations Conference on Trade and development, and the WTO. The 64 countries along the line include 16 in West Asia and North Africa, 16 in central and Eastern Europe, 5 other CIS countries, 8 in South Asia, 11 in Southeast Asia, including Myanmar, Vietnam and Thailand, and 5 in Mongolia, Russia and Central Asia.
Data content: normalized vegetation index data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: the first band of mod13a2 product was extracted from NASA medium resolution imaging spectrometer as leaf area index data and multiplied by the scale factor of 0.0001. Data quality: the spatial resolution is 1000m × 1000m, the temporal resolution is 8 days, and the value of each pixel is the average value of eight days' normalized vegetation index. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other vegetation data to analyze the regional distribution of a certain vegetation type.
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie
The data set recorded one belt, one road, 2002-2016 years' fertilizer and pesticide consumption data in 65 countries. Fertilizer and pesticide consumption refers to the amount of plant nutrients and pesticides consumed per unit of cultivated land. Fertilizer products include nitrogen, potassium and phosphate (including phosphate rock powder), and traditional nutrients animal and plant fertilizers are not included. Data source: Food and Agriculture Organization, electronic files and web site. Fertilizer and pesticide are the main sources of agricultural chemical pollution, which pose a serious threat to the agricultural ecological environment and the sustainable development of agricultural economy. The data set reflects one belt, one road, along the line of fertilizer and pesticide use, and can provide data support for the research on agricultural ecological environment and other related research. The data set contains two data tables: fertilizer consumption (kg / ha of cultivated land) and pesticide consumption (kg / ha of cultivated land).
This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the desert station from January 1 to December 31, 2018. The site (100.9872°E, 42.1135°N) was located on a desert surface in the desert, which is near Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 1054 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 and 10 m, north), wind speed profile (010C; 5 and 10 m, north), wind direction (020C, 10m), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), four-component radiometer (CNR1; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0 m), soil moisture profile (ML3; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0 m). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m; RH_5 m and RH_10 m) (℃ and %, respectively), wind speed (Ws_5 m and Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, Ts_100 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, Ms_100 cm) (%). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This dataset is the boundary vector data of the prefecture-level administrative units in the Qinghai-Tibet Plateau in 2015. The data is in the Shapefile format and includes provincial-level administrative units such as the Tibet Autonomous Region, Qinghai Province, Gansu Province, Yunnan Province, and Xinjiang Uygur Autonomous Region in the Qinghai-Tibet Plateau. The 38 prefecture-level administrative units can be used for the geographical background research of the urbanization and ecological environment interaction stress of the Qinghai-Tibet Plateau. It is the basic geographic data for the statistics of urbanization indicators such as social, economic and population levels of the Qinghai-Tibet Plateau. The data is obtained by means of data capture and collected through the administrative interface data acquisition API interface provided by the high-tech map. The data set uses the geographic coordinate system of WGS84.
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, WU Tonghua WU Tonghua WU Tonghua
This dataset is Meteorologic Elements Dataset of XDT on Qinghai-Tibet Plateau 2014-2018. Meteorologic elements including: 2m air temperature(℃), 2m air humidity(%), precipitation(mm), 2m wind speed(m/s), global radiation(w/㎡). The data are from the XiDaTan monitoring site(site code: XDTMS) of Cryosphere Research Station on Qinghai-Tibat Plateau, Chinese Academy of Sciences(CRS-CAS). These daily data was calculated from the original monitoring data(monitoring frequency is 30min). The missing part of the daily data was marked by NAN, which were manually collated and verified. The missing period was from 2017-7-7 to 2017-10-3.
The data set of agricultural activity intensity of the Qinghai Tibet Plateau is based on the County-Level Agricultural statistical data, including the annual cultivated land area, agricultural, forestry, animal husbandry and fishery labor force, total power of agricultural machinery, rural power consumption, effective irrigation area, pesticide use, fertilizer use, total output of grain crops, and total output value of agricultural, forestry, animal husbandry and fishery. The agricultural input index and output index are taken as the first level indicators, and the unit cultivated land area is constructed The intensity index system of agricultural activity is composed of 10 indexes, such as total power of agricultural machinery, fertilizer application amount per unit cultivated area and labor productivity. Entropy method was used to determine the weight of each index, and the input-output index of county-level agriculture in the Qinghai Tibet Plateau was obtained by AHP. The basic data comes from the statistical data released by the National Bureau of statistics, and the original data has been approved and corrected, with high reliability. The input-output index, input-output index and input-output index of county level in the Qinghai Tibet Plateau from 1980s to 2015 included in the data set reflect the spatiotemporal variation characteristics of the intensity of agricultural production activities in the Qinghai Tibet Plateau to a certain extent, and provide data support and theoretical reference for the local agricultural development.