The annual average ground temperature (MAGT) spatial distribution dataset of permafrost in Northeast China is a high-precision (1km ² spatial resolution) basic data product that reflects the thermal state of permafrost in Northeast China. This dataset is based on the theories of heat transfer and energy exchange, and is generated by constructing a one-dimensional numerical calculation model of the physical processes of permafrost changes (introducing enthalpy temperature phase transition mechanism and semi empirical n-factor boundary conditions) through inversion. The driving data is coupled with the soil parameters of SoilGrid250m and the initial geothermal field of ERA5 Land. This dataset contains the current distribution data from 2010 to 2024 (baseline period). Verified by extensive natural and engineering drilling data (such as along the China Russia crude oil pipeline), the root mean square error (RMSE) of data space simulation is controlled within 0.5 ℃, accurately depicting the distribution characteristics of the "brittle" thermal state dominated by high-temperature frozen soil (0-2.0 ℃) in Northeast China. This dataset can provide core data support for major engineering construction in cold regions (such as linear engineering foundation design), ecological environment protection, and regional response to climate change.
This dataset is stored in GeoTIFF raster format, with raster pixel values representing the annual average ground temperature of permafrost within the 1km ² grid. According to the actual measurement experience in the Northeast region, the annual variation of ground temperature at a depth of 10 meters below the surface is usually less than 0.01 ℃ and is not affected by seasonal fluctuations. Therefore, the grid values in this dataset specifically represent the annual average temperature values at a depth of 10 meters underground.
This dataset was generated through numerical simulations of physical processes. Using high-resolution WorldClim or observational interpolation data as the baseline climate background. The initial geothermal field data was extracted from the ERA5 Land stratified geothermal dataset. The soil type and initial moisture content were extracted from the SoilGrid250m dataset for calculating soil volumetric heat capacity and thermal conductivity.
Sample construction: Basic data for model accuracy validation and regional feature analysis, collected from a long-term drilling monitoring network in the field. Based on the typical permafrost monitoring network established along the Moda Line (MDS) of the China Russia crude oil pipeline. We have continuously collected long-term measured data from 2014 to 2023. Including ground temperature at various depths (with a focus on extracting depths of 10m/15m to avoid seasonal fluctuations and obtain true MAGT), active layer thickness (ALT), ice content (rich/saturated ice state), and surface cover type (peat soil or swamp vegetation, etc.).
Algorithm execution: Before inputting the model, the multi-source data is subjected to core downscaling and parameterization processing. Python uses a fully implicit backward Euler difference format to discretize the time and space and generate the primary product.
Post processing: After the numerical model operation is completed, the output matrix is mapped and spatially analyzed, and the results are converted into a 1000 m resolution grid and projected as WGS84 (EPSG: 4326). Based on GIS technology, the output MAGT grid data is converted into standard GeoTIFF. Extract the isotherm of MAGT=0 ℃ and generate permafrost data for many years.
The data can be used for various spatial analysis tasks, identifying permafrost regions and unstable areas. This data can be directly used for macroscopic route selection in linear engineering projects such as the China Russia crude oil pipeline, high-grade highways, and railways, to avoid high-risk degradation areas with high ice content.
| # | number | name | type |
| 1 | 2022FY100700 | Survey of Permafrost Conditions and Freeze-Thaw Damage in the High-Latitude Regions of Northeast China | Basic Resource Survey Project |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB7285.2026
10.12072/ncdc.nieer.db7285.2026
