1.South Asia meteorological data set: meteorological data of Kathmandu center for research and education,CAS-TU (2019)

    1) This data includes the basic meteorological data of Kathmandu center for research and education,CAS-TU in 2019; the parameters are: temperature ℃, relative humidity%, atmospheric pressure kPa, precipitation mm, radiation w / m2, wind speed M / s. Table 2 is a description of the weather station, including the geographical location and underlying surface. 2) Data sources and processing methods: the data are from the hourly data of Kathmandu science and education center, Chinese Academy of Sciences, daily average of temperature, air pressure, radiation and wind speed, and daily sum of rainfall. 3) Data quality description: among these parameters, the quality of air pressure data is poor, and there are many missing data due to instrument failure from June to August in 2019 4) Compared with the data of different regions in South Asia, the meteorological data can be used for postgraduates and scientists with atmospheric science, hydrology, climatology, physical geography and ecology.

    ZHU Liping

    doi:10.11888/Meteoro.tpdc.270887 1573 1 Protection period 2020-08-21

    2.Indoor and outdoor physiological and ecological data of four lizards in the Qinghai Tibet Plateau and surrounding typical areas (2013-2019)

    1) data content: including the morphological and reproductive life history data of four lizards, i.e. Phrynocephalus vlangalii, Phrynocephalus przewalskii, Eremias argus and Eremias multiocellata, and the physiological and ecological data of indoor and outdoor activity temperature, selection temperature, tolerant high temperature and tolerant low temperature, which is helpful to understand and analyze the physiological and ecological characteristics of typical lizards. 2) data source and processing method: Based on the indoor and outdoor experiments of typical lizards on the Qinghai Tibet Plateau and pan third pole from 2013 to 2019, the data of physiological and ecological indicators of lizards in the field and the data of reproductive life history of pregnant lizards were recorded. 3) data quality description: the lizard indoor and outdoor data collectors are all graduate students, who have been trained strictly to ensure the quality of the collected data. 4) data application achievements and prospects: Taking the typical lizards in the Qinghai Tibet Plateau and surrounding areas as the object, focusing on the impact of climate change on the thermal regulation behavior and reproductive life history of lizards, obtaining the physiological and ecological change characteristics of lizards under the climate change conditions is helpful to simulate and analyze the response trend of lizards distribution and population change under the climate warming environment.

    ZENG Zhigao

    doi:10.11888/Ecolo.tpdc.270387 1521 1 Protection period 2019-12-19

    3.Integration dataset of Tibet Plateau boundary

    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

    doi:10.11888/Geogra.tpdc.270099 7538 533 Open Access 2019-06-11

    4.China meteorological forcing dataset (1979-2018)

    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

    doi:10.11888/AtmosphericPhysics.tpe.249369.file 46073 2733 Open Access 2019-05-09

    5.Restrictive classification/zoning dataset of water resources carrying capacity of countries along the Belt and Road

    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.

    JIA Shaofeng

    doi:10.11888/Hydro.tpdc.271012 272 20 Open Access 2020-10-25

    6.Dataset of soil water erosion modulus with 2.5 m resolution in 15 watersheds of Pakistan(2019)

    1) The data includes the soil erosion modulus of 15 watersheds with a resolution of 2.5 m in the year of 2019 in Pakistan. 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 15 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 15 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

    doi:10.11888/Soil.tpdc.270429 937 9 Open Access 2020-01-21

    7.Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observ祁连山综合观测网:青海湖流域地表过程综合观测网(高寒草甸草原混合超级站物候相机观测数据集-2019)atory network (Phenology camera observation data set of Alpine meadow and grassland ecosystem Superstation, 2019)

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from phenology camera observation data of the Alpine meadow and grassland ecosystem Superstation from May 1 in 2019 to December 31 in 2019. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The phenology camera adopts a vertical downward method to collect data, with the resolution of 2592*1944. Phenology photos in this data set were taken at 12:10 a day, which has a time error of ±10 min. The image is named as BSDCJZ BEIJING_IR_Year_Month_Day_Time.

    LI Xiaoyan

    doi:10.11888/Meteoro.tpdc.270801 2004 22 Requestable 2020-08-01

    8.Grassland actual net primary production, potential net primary production and potential aboveground biomass on the Tibetan Plateau from 2000 to 2017

    Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.

    NIU Ben, ZHANG Xianzhou

    doi:10.11888/Ecolo.tpdc.271204 75 0 Protection period 2021-03-04

    9.Extreme precipitation disaster risk assessment data set (2020)

    One belt, one road level, is set up. The data set is based on the 100 meter risk assessment data set and the 100m level vulnerability assessment dataset. The risk assessment data set of 34 nodes and 100 meters in the key area of the whole area is calculated based on the international definition of risk, risk (R) = hazard (H) * vulnerability (V). The data set assessed one belt, one road, the extreme precipitation risk under extreme precipitation events, and provided the basis for local government departments' decision-making. At the same time, it could make early warning before the flood disaster, so that we could gain valuable time to take measures to prevent and reduce disasters and reduce the loss of lives and property of people caused by floods.

    GE Yong, LI Qiangzi, LI Yi

    378 3 Open Access 2020-12-23

    10.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (an observation system of meteorological elements gradient of Daman superstation, 2018)

    This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. 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. 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, XU Ziwei, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei

    doi:10.11888/Meteoro.tpdc.270776 4698 147 Requestable 2019-12-19

    11.Railway data of the key areas along One Belt One Road (2015)

    The railway data of 34 key areas along the Belt and Road is collected from the Internet and reprocessed. First, we download the linear railway data from the country where the key node areas along the One Belt One Road are located from the OpenStreetMap, and cut and extracted them by region. Meanwhile, it is compared and analyzed with the railway extraction result based on high resolution remote sensing images, and then updated with data from regional statistical bureaus. It is finally integrated into a railway infrastructure element data product. The format of data is linear shapefile data. The spatial coordinate system of the railway data is WGS84, and it contains attribute fields such as name, class and so on. This data can be used to calculate the length of railways and analyze the distribution of railways in cities. The railway 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

    231 21 Open Access 2020-12-21

    12.Genome resequencing data of equine (2019)

    In order to study the population evolution history and local adaptive genetic mechanism of the main domesticated equine animals in the Qinghai Tibet Plateau and its surrounding areas, and to establish the corresponding germplasm genetic resource bank. We sequenced the whole genome of 100 horse species collected in Qinghai Province, Tibet Autonomous Region and Xinjiang Autonomous Region, including Tibetan horses, Tibetan donkeys, Pingyuan donkeys and local breeds of Jiama plain. A lot of genomic data were generated by sequencing, which provided data for tracing the historical events of domestication, migration and expansion of the main domesticated equine animals in this area, and further exploring the adaptation mechanism of equine animals to the poor environment such as hypoxia, high cold and dry.

    LI Yan

    doi:10.11888/Ecolo.tpdc.270893 787 1 Protection period 2020-01-20

    13.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2018)

    This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the Sidaoqiao mixed forest station from January 1 to December 31, 2018. The site (101.134° E, 41.990° N) was located on a tamarix and populous forest (Tamarix chinensis Lour. and Populus euphratica Olivier.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 874 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (28 m, north), wind speed and direction profile (28 m, north), air pressure (in tamper box), rain gauge (28 m, south), four-component radiometer (24 m, south), two infrared temperature sensors (24 m, south, vertically downward), two photosynthetically active radiation (24 m, south, one vertically upward and one vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0, -1.6, -2.0, -2.4 m), and soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0, -1.6, -2.0, -2.4 m). The observations included the following: air temperature and humidity (Ta_28 m; RH_28 m) (℃ and %, respectively), wind speed (Ws_28 m) (m/s), wind direction (WD_28 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) (℃), photosynthetically active radiation of upward and downward (PAR_up and PAR_down) (μmol/ (s m^-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, Ts_160, Ts_200, Ts_240 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, Ms_100, Ms_160, Ms_200, Ms_240 cm) (%, volumetric water content). 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. Due to the power loss of datalogger, there were occasionally data missing during January 1 to 9, and November 10 to December 14; (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: 2018-9-10 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

    doi:10.11888/Meteoro.tpdc.270771 4646 95 Requestable 2019-12-12

    14.The Food Consumption Data for the Belt and Road

    The data content of the Belt and Road Food Consumption includes: The data of Food Consumption for “B&R” includes: 1. Total food consumption in regions and countries along the “B&R”, mainly including Cereals-Excluding Beer, Meat, Eggs, Milk-Excluding Butter, Fruits - Excluding Wine, Vegetables, Pulses, Starchy Roots, Oilcrops, Sugar& Sweeteners; 2.Nutrient intake in regions and countries along the “B&R”, mainly including energy, protein, fat. Source:FAO. Data application:According to the data provided,the basic characteristics analysis of food consumption, the analysis of the nutrient intake level and the analysis of food consumption pressure can be carried out in the Belt and Road region and the countries along the route, so that the dietary pattern evaluation analysis and the basic characteristics food requirement can be carried out.

    YANG Yanzhao

    992 5 Requestable 2019-01-15

    15.The urbanization rate data of each state in Tajikistan (2000-2016)

    The data set records the urbanization rate data of each state of Tajikistan from 2000 to 2016.The data is from kazakhstan's national statistics bureau. Urbanization is a concept with broad implications.In a narrow sense, it generally refers to the urbanization of population, which refers to the increase of the number of cities and the expansion of the urban scale, and the process of population aggregation to cities in a certain period.Urbanization rate refers to the proportion of permanent urban residents in a region in the total permanent resident population.The name of the original index is Russian, which has been translated and edited.The accuracy of the official data can provide basic data basis for the study of the socio-economic development of central Asian countries.

    HUANG Jinchuan, MA Haitao

    1571 2 Requestable 2019-01-02

    16.The land cover/use data in key areas of the Qilian Mountain (2018)

    This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.

    WANG Hongwei, QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan

    doi:10.11888/Geogra.tpdc.270154 2317 67 Open Access 2019-06-05

    17.Meteorological observation dataset of the standard meteorological station in the Irtysh River basin(1961-2015)

    The "Meteorological observation dataset of the standard meteorological station in the Irtysh River basin" contains the temperature and precipitation observation data at the monthly scale of the Habahe meteorological station, Jimunai meteorological station, Buerjin meteorological station, Fuhai meteorological station, Altay meteorological station and Fuyun meteorological station of the Irtysh river basin. The time scale of the data is month. The data set started in January 1961 (data of Fuyun station was missing from January to May 1961) and ended in December 2015. The special work of ground basic data re-examined the quality of historical informatization documents and revised the site documents with problems and differences. The data set does not revise the homogeneity of data, but segments the stations with obvious heterogeneity.

    ZHANG Wei

    doi:10.11888/Meteoro.tpdc.270903 1689 27 Open Access 2019-01-13

    18.Observation data glacier meteorological station from West Pamir in Tajikistan (2020)

    The West Pamir glacier meteorological station in Tajikistan (38 ° 3 ′ 15 ″ n, 72 ° 16 ′ 52 ″ e, 3730m) is jointly constructed by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The observational data include hourly meteorological elements (average wind direction (°), average internal wind speed (M / s), maximum wind speed (°), maximum wind speed (M / s), average temperature (℃), maximum temperature (℃), minimum temperature (℃), average relative humidity (%), minimum relative humidity (%), average atmospheric pressure (HPA), maximum atmospheric pressure (HPA), minimum atmospheric pressure (HPA)). The data period is from November 1, 2019 to November 30, 2020 Meteorological observation data can provide important basic data for the study of the relationship between climate change, glaciers and water resources in the West Pamir mountains, and provide important data for the economic construction of the lower reaches of the Amu Darya River Basin in Tajikistan.

    HUO Wen, Ruibo Zhang

    doi:10.11888/Meteoro.tpdc.271179 1100 1 Protection period 2021-02-02

    19.Dataset of 100m scale GDP grid in 34 key nodes of Pan third pole region (2010)

    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. We have collected the published GDP data. Collect the public GDP data. On the basic of 1-kilometer scale global GDP grid data in 2010 released by the United Nations, the total GDP of the node area was obtained. The lighting data and land use data of node areas are took as auxiliary data, after data preprocessing, data interpolation and multiple regression analysis, establish the relationship between GDP and the hundred meter scale multiple data, and then, the GDP data of 34 key node areas are obtained.

    GE Yong, LI Qiangzi, DONG Wen

    doi:10.11888/Socioeco.tpdc.270415 2331 6 Open Access 2020-03-16

    20.Simulation data of active layer thickness and ground temperature of permafrost in Qinghai Tibet Plateau (2000-2015, 2061-2080)

    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

    doi:10.17632/hbptbpyw75.1 1004 37 Open Access 2021-03-09