To describing the quantity of atmospheric water resource gaining over the TP, we provide two indexs based on ERA5 monthly reanalysis. One is called column water income (CWI), defined as the sum of vertical integrated divergence of water vapor flux and surface evaporation. It is 0.25 ×0.25 gridded with unit of kg/m2 or millimeter. Another one is Atmospheric water tower index (AWTI), total of net income of atmospheric water resource for the entire TP area, i.e., and unit is Gt.
1、Based on field eddy correlation (EC) measurement data, using the standard data processing method for EC data, including despiking, coordinate rotation, air density corrections, outlier rejection, and friction velocity threshold (u*) corrections, gap filled, and NEE partition. The dataset collects carbon flux data and microclimate measurement data from 2003 to 2016 in three typical alpine grassland ecosystems on the Qinghai-Tibet Plateau, including Damxung alpine meadow, Haibei alpine meadow ，Naqu alpine meadow，Zoige alpine grassland，Qilian mountion grassland . The time resolution of data is high (30 min), and the interpolation of data is complete throughout the year. This dataset can be applied to carbon flux assessment, comparison and prediction in these alpine meadows, attribution of climate factors affecting carbon flux, validation of model simulation results, etc. 2、Based on the MCDGF43 dataset, we produce the visible and near-infared albedo of Tibetan Plateau, using the standard data processing of hdf to tif , including the moasic, resample and masked by Tibetan Plateau's boundary. The time resolution of dataset is 8 days and the spatial resolution is 500 meters, which span the period of 2003-2016.
ZHANG Yangjian SU Peixi YANG Yan
Based on the ecological environmental risk data of the development of agriculture and animal husbandry in 2030, 2050 and 2070 in the Qinghai Tibet Plateau, the risk values of agriculture and animal husbandry in the six typical years of 198519901995200002010 and 2015 are calculated, and the predicted value of ecological environmental risk in 203020502070 is calculated by using the fuzzy weighted Markov chain model. The grid map of meteorological factors extracted from ArcGIS and the future climate model (rcp4.5) was superimposed to obtain the data of agricultural and animal husbandry ecological environment risk in the Tibetan Plateau in 203020502070.
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.
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
Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. According to the collected the published global GDP data of 2015, a downscaling model, named support vector machine regression kriging was established for predicting 100-m GDP in thirty-four key nodes along the Belt and Road. The remote sensed night light data, land cover, vegetation and terrain indices were employed as ancillary variables in downscaling process. To solve the problem of missing data existing in the ancillary datasets, we will apply kriging and function interpolation methods to fill gaps. The aggregation and resampling were used to obtain 1-km and 500-m all ancillary variables, as well as 100-m terrain indices including elevation, slope and aspect. The adopted downscaling model contains trend and residual predictions. The support vector machine regression is used to model the relationship among GDP and its ancillary variables for obtaining GDP trends at fine scale based on scale invariant of the relationship. And then, the kriging interpolation is used to estimate GDP residuals at fine scale. In the downscaling process, the mentioned downscaling model was firstly employed in 1-km and 500-m data for obtaining 500-m GDP predictions; and it was again used in 500-m and 100-m data for achieving 100-m GDP predictions. The 100-m GDP predictions in constant 2011 international US dollars would provide high spatial resolution data for risk assessments.
GE Yong LING Feng
The data are construction land index of countries along the "the Belt and Road" in 2010 and 2015, also known as the construction land rate. It refers to the proportion of land used for construction in the total land area, including land for urban and rural housing and public facilities, land for industrial and mining purposes, land for energy, transportation, water conservancy, communications and other infrastructure, land for tourism and land for military purposes. The data come from the international statistics website. The area of construction land and relevant land that it had provided, divide the result of total land area of the country to get. It reflects the degree of development of a country's land area and the intensity of infrastructure development. At the same time, its value is also closely related to the national and regional economic development level, population density, urban residential density, traffic network density and so on. In the coordinated development of "the Belt and Road", they can provide important reference for the planning and implementation of national policies and programs, so as to accelerate the economic development of all countries.
CHEN Shaohui LIU Zhenwei
The data set is the population data of countries along the "One Belt And One Road" from 1960 to 2017. Population is a social entity with complex contents and a variety of social relations. It has gender, age and natural composition, as well as a variety of social composition and social relations, as well as economic composition and economic relations. The birth, death and marriage of population are in family relations, ethnic relations, economic relations, political relations and social relations. All social activities, social relations, social phenomena and social problems are related to the process of population development. In the coordinated development of "One Belt And One Road" China and other countries, it can provide important references for the planning and implementation of national policies and programs, thus accelerating the economic development of all countries.
The land-sea thermal contrast is an important driver for monsoon interannual and interdecadal variability and the monsoon onset. The importance of the thermal contrast between the Tibetan Plateau (TP) and the Indian Ocean (IO) in driving the establishment of Indian Summer Monsoon (ISM) has been recognized. The South Asian Summer Monsoon (SASM) is primarily a tropical summer monsoon. As a direct dynamic response to the diabatic heating, the difference between upper and lower-layer winds can be closely linked to the strength of the heat source. The upper-layer thermal contrast is more important for the SASM (Sun et al., 2010; Sun and Ding，2011; Dai et al., 2013). Thermal contrast between the TP and the IO at the mid-upper troposphere is closely related to the onset and the variability of ISM. Considering that the temperature above the TP and IO are the two centers which are most sensitive to the change of ISM, a thermal contrast index (TCI) is proposed based on 500-200hPa air temperature: TCI = Nor[T(25°N-38°N, 65°E-95°E) - T(5°S-8°N, 65°E-95°E)] Where Nor represents standardization and T is 500-200hPa air temperature. The TCI is larger, and the ISM is stronger. The TCI can capture the interannual and interdecadal variability of ISM well. The cooperative thermal effect between TP and IO may contributes more to the ISM than the separately temperature of TP or IO. In addition, from the view of climate mean state, the pentad-by-pentad increment of TCI has a 15-pentad lead when the correlation coefficient between it and the ISM index reaches the maximum. And the correlation coefficient between the pentad-by-pentad increment of TCI and the ISM index is significant when the pentad-by-pentad increment of TCI has a 3-pentad lead. The result indicates the advantage of the TCI for prediction of the ISM. Meanwhile, the averaged pentad-by-pentad increment of TCI for the first 25 (TCI25) pentads may be a predictor of the early or late onset of the ISM. The ISM onset will be earlier when the TCI25 is larger.
LI Zhangqun XIAO Ziniu ZHAO Liang
The data set of socio-economic vulnerability parameters in the agricultural and pastoral areas of the Qinghai Tibet Plateau mainly contains the socio-economic vulnerability parameter data at county level. The data time range is from 2000 to 2015, involving 112 counties and districts in Qinghai Province and Tibet Autonomous Region. The main parameters include population density, the proportion of unit employees in the total population, the proportion of rural employees in the total population, the proportion of agricultural, forestry, animal husbandry and fishery employees in rural employees, per capita GDP, per capita savings balance of residents, per capita cultivated land area, per capita grain output, and people Average oil production, livestock stock per unit area, per capita meat production, the proportion of primary and secondary school students in the total population, and the number of hospital beds per 10000 people. The entropy weight method is used to calculate the weight of each index, and ArcGIS is used to spatialize, and finally the county scale socio-economic vulnerability parameter data is obtained. The original data is from the statistical yearbook of Qinghai Province and Tibet Autonomous Region. The data are expressed by shape file and excel file. This data set will provide reference for socio-economic vulnerability assessment and selection of typical agricultural and pastoral areas.
ZHAN Jinyan TENG Yanmin LIU Shiliang
The study of fossils in Bangor and Lunpola is of great significance, and the date of fossils is indispensable. There are volcanic tuffs in this area. Zircon can be used for U-Pb age analysis to determine the age of strata and fossils. This data shows the zircon U-Pb age analysis results of tuff samples from bango and Lunpola fossil sites in a graphical way. The figure shows the shape of a large number of zircons, and indicates the age analysis results on different zircon samples. The data show the large sample size used in related research, and the analysis results are also clear. The image display of this data is intuitive and clear, and the results are reliable, which is of great significance to the study of the Qinghai Tibet Plateau.
The main idea of water resource estimation is to build a machine learning model using runoff coefficients and runoff influencing factors (including climate, topography, land use, soil, etc.), and then calculate the runoff coefficients based on the runoff depth and precipitation data estimated by the model. First, based on global public data, we build various machine learning models for runoff depth and topography, climate, soil, and land use, evaluate the simulation accuracy and validity of different models, and select the optimal model for runoff depth estimation. Finally, the optimal model is used to estimate and generate the runoff depth distribution in the Belt and Road region, and the runoff coefficient distribution is calculated based on the precipitation distribution data in 2015.
The data set includes the mass balances of Hailuogou Glacier, Parlung No.94 Glacier, Qiyi glacier, Xiaodongkemadi Glacier, Muztagh No.15 Glacier, Meikuang Glacier and NM551 Glacier in the Qinghai Tibet Plateau from 1975 to 2013. Based on several mass balance observations collected from World Glacier Inventory (https://nsidc.org/data/g10002/versions/1) and The Third Pole Environment Database (http://en.tpedatabase.cn/, doi:10.11888/GlaciologyGeocryology.tpe.96.db) by Tandong Yao and the meteorological data obtained from Global Land Assimilation System (GLDAS) (meteorological variables, including precipitation, air temperature, net radiation, evaporation on snow surface, and snow depth, in the central grid of each glacier are extracted from GLDAS data set shown in meteo.xlsx), the mass balances of the above seven glaciers from 1975 to 2013 are reconstructed by using the glacier material balance calculation formula. This reconstruction data is based on the published glacier material balance data to calibrate the parameters in the glacier material balance formula, and to reconstruct the long-time series material balance by using the glacier material balance formula, in which the parameter calibration results and the reconstruction results of the long-time series data are compared with the relevant research results, demonstrating the rationality of the data results Please refer to the following papers. The data can be used to study the change of water resources in the glacial region, expand the data set of Glacier Mass Balance in the Qinghai Tibet Plateau, and provide reference for the future research of Glacier Mass Balance reconstruction.
Paleomagnetic Dataset of Zagros forelandbasin in IranPaleomagnetic Dataset of Zagros forelandbasin in IranPaleomagnetic Dataset of Zagros forelandbasin in IranPaleomagnetic Dataset of Zagros forelandbasin in IranPaleomagnetic Dataset of Zagros forelandbasin in IranPaleomagnetic Dataset of Zagros forelandbasin in Iran
The dataset of restrictive classification/zoning of land resource carrying capacity of countries along the “Belt and Road” includes: 1. Restrictive classification/zoning data of land resource carrying capacity based on human-food balance; 2. Restrictive classification/zoning data of land resource carrying capacity based on equivalent balance, divided into two categories based on heat supply and demand balance and protein supply and demand. Source：Obtained using FAO food production/consumption data and land resource carrying capacity model. Data application：Based on this data, the human-land relationship of the countries along the route can be judged from cultivated land resources to land resources, providing scientific guidance and decision-making basis for optimizing the allocation of regional functions and improving the spatial layout of construction.
In order to investigate the variation characteristics of agricultural water resources vulnerability in Central Asia, an index system was established with 18 indicators from three components, namely exposure, sensitivity and adaptation, according to the scheme of vulnerability assessment. Based on the socio-economic, topography, land cover and soil data, agricultural water resources vulnerability were calculated using the Equal-Weights and Principal Component Analysis (PCA) method. Each original raster data is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes 1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2017and 1992-2017with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for agricultural water supply and demand, development and utilization analysis in five central Asian countries.
LI Lanhai YU Shui
The restrictive classification/zonation of water resource carrying capacity of the countries along the "Belt and Road" is one of the important achievements in water resource carrying capacity evaluation. Restrictive classification/zonation method of water resource carrying capacity: Based on multi-source data such as remote sensing, statistics and research, combined with water resource availability and per capita comprehensive water use evaluation study, to quantitatively evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance according to the relationship between resource supply and demand balance, to study the threshold system of water resource carrying capacity evaluation, to evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance. The restrictive classification/zonation of water resource carrying capacity provides a basis for water security early warning, water resource carrying capacity enhancement and control strategies.
When using the 3DVAR for data assimilation, it is necessary to use error covariance to determine the contribution of background field and observation. Among them, the background field error covariance depends not only on the atmospheric prediction model (such as resolution, parameterization scheme, etc.), but also on the simulation area. Based on the Weather Forecast and Research (WRF) model, this data is estimated by NMC method through the simulation of the Central Asian Great Lakes region (27 km horizontal resolution) in 2017. The variables include stream function, velocity potential function, temperature, relative humidity and surface pressure. This data can be applied to the study and application of data assimilation in the Central Asia Great Lakes region based on WRF model.
We have completed the pollen analyses of 252 sedimentary samples from Dahonggou section in Qaidam Basin covering the Cenozoic. Palynomorph extraction followed the routine process with HCl and HF treatments during the extraction. Airborne pollen-charcoal traps and surface-sediment samples from soils were collected to evaluate the relationship between pollen-charcoal contents and vegetation structure, and effect of sedimentary environment conditions on the pollen dispersal and deposition. Combined with pollen-charcoal data from other sections, we are going to establish the fire history spanning the last 30 Ma years, and to discuss the responds and feedbacks of the vegetation and fire to the climate changes. Our work is beneficial to the knowledge of the processes of aridification in Inner Asia and its mechanism. The submitted pollen data set is according to the proposal, and in order to guarantee data accuracy, 20% of the data have been examined in our lab by random sampling method. Data collection and analysis are continued, we hope our work can contribute more to the project in the next few years.
The DEMs of the typical glaciers on the Tibetan Plateau were provided by the bistatic InSAR method. The data were collected on November 21, 2013. It covered Puruogangri and west Qilian Mountains with a spatial resolution of 10 meters, and an elevation accuracy of 0.8 m which met the requirements of national 1:10 000 topographic mapping. Considering the characteristics of the bistatic InSAR in terms of imaging geometry and phase unwrapping, based on the TanDEM-X bistatic InSAR data, and adopting the improved SAR interference processing method, the surface DEMs of the two typical glaciers above were generated with high resolution and precision. The data set was in GeoTIFF format, and each typical glacial DEM was stored in a folder. For details of the data, please refer to the Surface DEMs for typical glaciers on the Tibetan Plateau - Data Description.