东北地区多年冻土年平均地温（MAGT）空间分布数据集是一套高精度（1km²空间分辨率）的反映东北地区冻土热状态的基础数据产品。该数据集基于传热学和能量交换理论，通过构建多年冻土变化物理过程一维数值计算模型（引入焓-温度相变处理机制及半经验n因子边界条件）反演生成。驱动数据耦合了SoilGrid250m土壤参数、ERA5-Land初始地温场。本数据集为2010-2024年（基准期）的现状分布数据。经广泛的天然与工程钻孔实测数据（如中俄原油管道沿线）验证，数据空间模拟的均方根误差（RMSE）控制在0.5℃以内，精准刻画了东北地区以高温冻土（0~-2.0℃）为主的“脆性”热状态分布特征。该数据集可为寒区重大工程建设（如线性工程地基设计）、生态环境保护以及区域应对气候变化提供核心数据支撑。\n
本数据集以GeoTIFF栅格格式存储，栅格像元值（Pixel Value）代表该1km²网格内的多年冻土年平均地温。根据东北区域实测经验，地表以下10m深处的地温年变化幅度通常小于0.01℃，不受季节波动影响。因此，本数据集中栅格值具体代表地下10m深度处的年平均温度值。", "ds_source": "
本数据集通过物理过程数值模拟的方式产生。采用高分辨率的WorldClim或观测插值数据作为基准气候背景。初始地温场数据提取自ERA5-Land的分层地温数据集。土壤类型及初始含水率提取自SoilGrid250m数据集，用于计算土壤体积热容和导热系数。", "ds_process_way": "
样本构建：用于模型精度验证与区域特征分析的基础数据，采集自野外现场长期钻孔监测网络。依托中俄原油管道漠大线（MDS）沿线建立的典型冻土监测网络。持续收集了2014年至2023年的长期实测数据。包括各深度地温（重点提取10m/15m深处以规避季节波动，获取真实MAGT）、活动层厚度（ALT）、冰含量（富冰/饱冰状态）、以及地表覆盖类型（泥炭土或沼泽植被等）。\n
算法执行：在模型输入前，对多源数据进行了核心的降尺度与参数化加工，Python采用全隐式向后欧拉差分格式进行时间与空间的离散化求解生成初级产品。\n
后处理：数值模型运算完成后，对输出矩阵进行了制图与空间分析，将结果转换为1000 m分辨率栅格，投影为 WGS84（EPSG:4326），基于GIS技术将输出的MAGT网格数据转换为标准GeoTIFF。提取 MAGT = 0℃ 的等温线，生成多年冻土数据。", "ds_quality": "
数据可用于多种空间分析任务，识别多年冻土区以及不稳定区域。本数据可直接用于线性工程（如中俄原油管道、高等级公路、铁路）的宏观选线，规避高含冰量的高风险退化区。", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "东北多年冻土区", "ds_acq_lon_east": 134.94166666666666, "ds_acq_lat_south": 38.041666666666664, "ds_acq_lon_west": 112.75, "ds_acq_lat_north": 54.35, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 46136544, "ds_files_count": 6, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "9c16c02b-ea8b-4f96-9df7-2ee831130842.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-04-07 09:11:42", "last_updated": "2026-05-12 11:57:29", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7285.2026", "i18n": { "en": { "title": "1km permafrost ground temperature distribution data of the permafrost region in Northeast China (2010-2024)", "ds_format": "*.tif", "ds_source": "
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.", "ds_quality": "
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.", "ds_ref_way": "", "ds_abstract": "
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.\r\n
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.", "ds_time_res": "", "ds_acq_place": "Northeast Permafrost Region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
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.).\r\n
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.\r\n
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.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "多年冻土", "数值仿真", "地温分布", "东北", "大兴安岭" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北多年冻土区" ], "ds_time_tags": [ 2010, 2024 ], "ds_contributors": [ { "true_name": "李国玉", "email": "guoyuli@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "高凯", "email": "gaokai@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王飞", "email": "wangfei@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "曹亚鹏", "email": "caoyapeng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "尹国安", "email": "yinguoan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "牛富俊", "email": "niufujun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "商允虎", "email": "shangyunhu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "林峻岑", "email": "linjuncen@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "杜青松", "email": "xbdqs@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "贾寒", "email": "jiahan@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王亚鹏", "email": "wangyapeng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "齐舜舜", "email": "qishunshun@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "艾熙惠", "email": "aixihui@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "杨佳伟", "email": "yangjiawei@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李兆祥", "email": "lizhaoxiang@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "胡美容", "email": "humeirong@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "尹国安", "email": "yinguoan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }