Dataset List

Vol.|Area

No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Spatial-temporal Mean Temperature Dataset in China-Mongolia-Russia Economic Corridor (1982-2018, 1-km/y)


JIAO Yue1,2YANG Jiuchun1LI Guangshuai1,3YU Lingxue*1BAO Yulong4ZHANG Shuwen1
1 Remote Sensing and Geographic Information Research Center，Northeast Institute of Geography and Agroecology，Chinese Academy of Sciences，Changchun 130102，China2 School of Life Science，Liaoning Normal University，Dalian 116029，China3 School of Geography Science，Changchun Normal University，Changchun 130032，China4 School of Geography Science，Inner Mongolia Normal University，Hohhot 010022，China

DOI:10.3974/geodb.2022.01.03.V1

Published:Jan. 2022

Visitors：7673       Data Files Downloaded：98      
Data Downloaded：27492.68 MB      Citations：

Key Words：

Annual mean temperature,China-Mongolia-Russia Economic Corridor,1-km,1982-2018

Abstract：

The Spatial-temporal Mean Temperature Dataset in China-Mongolia-Russia Economic Corridor (1982-2018, 1-km/y) was developed based on the data integration between temperature data from 325 meteorological stations in China-Mongolia-Russia Economic Corridor (CMREC), and ANUSPLIN meteorological interpolation software. The results show that R² was 0.980 and above, where R is the correlation coefficient between meteorological station data and interpolation results. The average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values are 0.348 ℃ and 0.481 ℃ separately. The dataset include: (1) boundary data of the study area; (2) annual mean temperature grid data in 1-km resolution during 1982-2018. The dataset is archived in .shp, .tif and .mdd data formats, and consists of 159 data files with data size of 8.65 GB (compressed into 2 files with 531 MB).Browse

Foundation Item：

Chinese Academy of Sciences (XDA2003020301); National Natural Science Foundation of China (42071025); Ministry of Science and Technology of P. R. China (2017FY101301)

Data Citation：

JIAO Yue, YANG Jiuchun, LI Guangshuai, YU Lingxue*, BAO Yulong, ZHANG Shuwen. Spatial-temporal Mean Temperature Dataset in China-Mongolia-Russia Economic Corridor (1982-2018, 1-km/y)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.01.03.V1.

JIAO Yue, YANG Jiuchun, LI Guangshuai, et al. Spatial-temporal mean temperature dataset in China-Mongolia-Russia Economic Corridor (1982–2018, 1-km/y) [J]. Journal of Global Change Data &Discovery, 2022, 6(2): 225–233

References：

[1] IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2021.
     [2] Jing, W. L., Yang, Y. P., Yue, X. F. Monthly temperature dataset of China at 1 km resolution [J]. Journal of Global Change Data and Discovery, 2017, 1(1): 66-73.
     [3] Yang, Y. Z., Lang, T. T., Zhang, C., et al. Comparative study of different temperature interpolation methods in the Belt and Road regions based on GIS [J]. Journal of Geo-Information Science, 2020, 22(4): 867-876.
     [4] Qian, Y. L., Lv, H. Q., Zhang, Y. H. Application and assessment of spatial interpolation method on daily meteorological elements based on ANUSPLIN software [J]. Journal of Meteorology and Environment, 2010, 26(2): 7-15.
     [5] Jiang, X. J., Liu, X. J., Huang, F., et al. Comparation of spatial interpolation methods for daily meteorological elements [J]. Chinese Journal of Applied Ecology, 2010, 21(3): 624-630.
     [6] He, L. K., Sun, L. Q., Li, Q. L., et al. Comparison of spatial interpolation methods for temperature in Shenzhen [J]. Advances in Meteorological Science and Technology, 2019, 9(3): 179-184.
     [7] Liu, H. L., Fan, Z. L., Han, M. Z., et al. Research on method of establishing Beijing-Tianjin-Hebei Daily Air Temperature Grid Data Set based on ANUSPLIN [J]. Journal of Marine Meteorology, 2020, 40(3): 111-120.
     [8] Peng, B., Zhou, Y. L., Gao, P., et al. Suitability assessment of different interpolation methods in the gridding process of station collected air temperature：A case study in Jiangsu Province, China [J]. Journal of Geo-Information Science, 2011, 13(4): 539-548.
     [9] Yi, G. H., Zhang, T. B., He, Y. X., et al. Applicability analysis of four spatial interpolation methods for air temperature[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2020, 47(1): 115-128.
     [10] Liu, Z. H., Li, L. T., Tim R. McVicar, et al. Introduction of the professional interpolation software for meteorology data: ANUSPLIN [J]. Meteorological Monthly, 2008(2): 92-100.
     [11] Zhao, M. Y., Yu, J., Hu, Y. Y. Spatial interpolation of temperature in Chongqing based on local thin disk smooth spline function [J]. Journal of Shaanxi Meteorology, 2021(1): 50-55.
     [12] Wen, H. Y., Chen, F. J., Li, J., et al. A study on spatial interpolation of temperature in Anhui province based on ANUSPLIN [J]. Meteorological and Environmental Research, 2019, 10(2): 51-55, 60.
     [13] Chen, W., Sun, L. Q., Li, Q. L., et al. An interpolation dataset for temperature and precipitation at 1 km grid resolution in Chinese mainland for recent 38 years [J]. Meteorological Science and Technology, 2021, 49(3): 355-361.
     [14] Wang, J. B., Wang, J. W., Ye, H., et al. An interpolated temperature and precipitation dataset at 1-km grid resolution in China (2000 – 2012) [DB/OL]. Chinese Scientific Data, 2017, 2(1): 73-80+205-212.
     [15] Chen, J. Y., Tao, H., Liu, J. P. A daily meteorological dataset of the China Pakistan Economic Corridor from 1961 to 2015 [DB/OL]. Chinese Scientific Data, 2021, 6(2): 229-238.
     [16] Ulan, Bagen. The China-Mongolia-Russia Economic Corridor has achieved results, faced problems and proposed future proposals [J]. Journal of the Western Mongolian Studies, 2019(3): 64-72+115.
     [17] Piao, J. Y. The main features and problems of building the China-Mongolia-Russia Economic Corridor [J]. Journal of Northeast Asia Studies, 2020(6): 17-30+145.
     [18] Ge, J., Xu, Y. F., An, X. Y., et al. Analysis and research on ecologically sensitive area of China-Mongolia-Russia economic corridor the background of “Belt and road” [J]. Acta Ecologica Sinica, 2019, 39(14): 5051-5057.
     [19] Sun, J., Zhang, X. P., Huang, Y. M. Evalution of precipitation from ERA-intreim, CRU, GPCP and TRMM reanalysis data in Dongting Lake Basin [J]. Resources and Environment in the Yangtze BASIN, 2015, 24(11): 1850-1859.
     [20] Zhu, H. H., Jiang, Z. H., Li, Laurent. Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6 [J]. Bulletin of Science, 2021, 66(24): 2528-2537.
     [21] Danielson, J. J., and Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-file Report 2011-1073 [R], 2011.
     [22] Hutchinson, M. F. Interpolation of Rainfall Data with Thin Plate Smoothing Splines - Part I: Two Dimensional Smoothing of Data with Short Range Correlation. [J]. Journal of Geographic Information and Decision Analysis, 1998(2): 139-151.
     [23] Liu, Z. H., Tim R. McVicar, Li, L. T., et al. Interpolation for time series of meteorological variables using ANUSPLIN [J]. Journal of Northwest A&F University (Natural Science Edition), 2008(10): 227-234.
     [24] Xu, X. Q., Zhu, M. X. Comparison of temperature spatial interpolation methods based on GIS in Jiangxi Province [J]. Journal of Green Science and Technology, 2021, 23(10): 21-24.
     [25] Meng, Q. Spatial-temporal variation and acquisition of raster dataset of precipitation in the Qinling Mountains [D]. Northwest University, 2021.
     [26] Bai, Y. Development of the Spatio-temporal variations dataset of NDVI in Qinling-Daba Mountains of China (2000-2019) [J]. Journal of Global Change Data and Discovery, 2020, 4(4): 346-353.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_CMREC_Temperature_1982-2018.pdf645.00kbDownLoad
1 CMREC_Temp_1982-2018_mdd.rar 149831.09KB
2 CMREC_Temp_1982-2018_tifshp.rar 394723.13KB
Co-Sponsors
Superintend