在气候变暖的北极放大和冬季放大效应下，土壤冻结深度是反映泛北极冻土变化的灵敏指标。然而，由于现有土壤冻结深度制图方案不兼具对土壤冻结深度高时空异质性和物理约束的刻画能力且主要面向年最大冻结深度，目前在泛北极地区仍缺乏高精度、月度时间分辨率的土壤冻结深度分布数据。我们耦合简化的Stefan方程和随机森林回归模型，构建了一种兼顾物理约束和时空异质性的月度土壤冻结深度制图新方案（MSFDmap）。利用泛北极地区20年间（2004–2023水文年）60个站点处的2123条站点-月度观测数据实现了MSFDmap，生产了2004–2023水文年泛北极冷季（10月–5月）的月度土壤冻结深度分布数据。在下伏多年冻土的地区，数据值表示土壤释放热量全部消耗于土壤冻结所达到的潜在土壤冻结深度（一般大于活动层厚度）。基于站点-月度土壤冻结深度的精度验证结果显示，数据的均方根误差（RMSE）为19.21 cm，决定系数（ R2）为0.91；与现有方案生产的数据相比，RMSE降低了24–55 %，R2提高了8–65 %。站点-月度土壤冻结深度的数据值时序与观测值时序具有高度的趋势一致性：在站点平均水平上，两时序的Pearson相关系数r = 0.99, RMSE = 9.13 cm；在83%的站点中，两时序的趋势一致性处于高水平（r ≥ 0.8）。数据展现了月度土壤冻结深度应有的纬度和海拔梯度，与基于ERA5-Land土壤温度插值所得土壤冻结深度分布的空间格局一致性r = 0.60。该数据有效地刻画了泛北极月度土壤冻结深度的时空动态，精度优于现有方案结果，可为泛北极地区的冻土研究提供基础数据支持。", "ds_source": "
站点多层土壤温度和近地表2 m气温观测源于：（1）俄罗斯水文气象信息研究所世界数据中心（https://meteo.ru/data/）；（2）美国自然资源保护局公开的土壤气候分析网络（Soil Climate Analysis Network）和积雪遥测网络（Snow Telemetry Network）中站点数据 （https://nwcc-apps.sc.egov.usda.gov/imap/）；（3）芬兰气象研究所的气象站数据（https://litdb.fmi.fi/）以及（4）全球多年冻土陆地网络（Global Terrestrial Network for Permafrost）数据库（https://gtnpdatabase.org/boreholes）。这些数据时间分辨率为逐日甚至更高（小时或10分钟），站点主要分布于欧亚北极地区和美国阿拉斯加地区，但不同来源数据的时间范围不一（最多包含1963–2024年）、所记录土壤温度的深度不一（多为1.6 m及更浅处）。\n
环境栅格数据包括：（1）ERA5-Land全球陆地再分析产品，以0.1°分辨率提供月度2 m气温、降水、雪深、多层土壤温度和体积含水量等数据（https://doi.org/10.24381/cds.68d2bb30）；（2）MCD15A3H v061，500 m分辨率的4天合成叶面积指数，源于Terra和Aqua卫星上的中分辨率成像光谱仪观测（https://doi.org/10.5067/MODIS/MCD15A3H.061）；（3）SoilGrids 2.0和HiHydroSoil v2.0，全球土壤属性数据，二者的分辨率为250 m、包含最深达2 m的6个土壤深度处的数据（https://doi.org/10.5194/soil-7–217-2021，https://www.futurewater.eu/projects/hihydrosoil/）以及（4）Global Multi-resolution Terrain Elevation Data 2010，为7.5″分辨率的全球高程数据（https://doi.org/10.3133/ofr20111073）。", "ds_process_way": "
（1）将多层土壤温度观测确定的土壤冻结深度和气温观测确定的空气冻结指数输入简化的Stefan方程，计算站点-月度的热传递因子。\n
（2）基于环境栅格数据，生产冻结期内月度累计平均的环境变量分布，并提取站点值。\n
（3）以站点-月度的环境变量为自变量，构建月度热传递因子的随机森林回归模型。\n
（4）采用月度累计平均的环境变量分布驱动月度热传递因子的随机森林回归模型，生产月度热传递因子的分布。\n
（5）根据简化的Stefan方程，利用月度热传递因子和空气冻结指数的分布实现月度土壤冻结深度制图。", "ds_quality": "
采用10折交叉检验法对MSFDmap生产的站点-月度土壤冻结深度数据值进行精度验证的结果显示，均方根误差（RMSE）为19.21 cm，决定系数（R2）为0.91；与现有方案生产的数据相比，RMSE降低了24–55 %，R2提高了8–65 %。采用Pearson相关系数（r）和RMSE验证站点-月度土壤冻结深度数据值时序与观测值时序的趋势一致性：在站点平均水平上，两时序的Pearson相关系数r = 0.99, RMSE = 9.13 cm；在83%的站点中，两时序的趋势一致性处于高水平（r ≥ 0.8）。数据展现了月度土壤冻结深度应有的纬度和海拔梯度，与基于ERA5-Land土壤温度插值所得土壤冻结深度分布的空间格局一致性r = 0.60。以上验证结果表明，该数据有效地刻画了泛北极月度土壤冻结深度的时空动态，精度优于现有方案，可为泛北极地区的冻土研究提供基础数据支持。", "ds_acq_start_time": "2003-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "泛北极地区", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 60.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 85.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 341641074, "ds_files_count": 2, "ds_format": "Geotiff", "ds_space_res": "11132 m （0.1°）", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "86dcd91b-7090-48fe-9bfc-50b19b5f1c9a.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.4510" ], "quality_level": 0, "publish_time": "2026-05-13 16:01:35", "last_updated": "2026-05-13 16:01:35", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7144.2026", "i18n": { "en": { "title": "A dataset of monthly soil freezing depth distribution in the Pan Arctic from 2004 to 2023 hydrological years", "ds_format": "Geotiff", "ds_source": "
Site observations of both multi-layer soil temperature and 2 m air temperature were obtained from four sources: (1) the Russian Research Institute of Hydrometeorological Information – World Data Center (https://meteo.ru/data/); (2) in-situ records from sites in the Soil Climate Analysis Network and Snow Telemetry Network provided by the Natural Resources Conservation Service (https://nwcc-apps.sc.egov.usda.gov/imap/); (3) meteorological station data from the Finnish Meteorological Institute (https://litdb.fmi.fi/); and (4) the Global Terrestrial Network for Permafrost database (https://gtnpdatabase.org/boreholes). These datasets have daily or higher temporal resolution (hourly or 10-minute) and are primarily located in the Eurasian Arctic and Alaska. Their time ranges extend up to 1963-2024, and the depths of soil temperature measurements vary by source, with most observations recorded at 1.6 meters or shallower.\r\n
Environmental raster datasets included several products. ERA5-Land, a global reanalysis dataset, provides monthly 2 m air temperature, precipitation, snow depth, and multi-layer soil temperature and volumetric water content at 0.1° resolution (https://doi.org/10.24381/cds.68d2bb30). MCD15A3H v061 offers 4-day composite leaf area index data at 500 m resolution, derived from the Moderate Resolution Imaging Spectroradiometer onboard Terra and Aqua satellites (https://doi.org/10.5067/MODIS/MCD15A3H.061). SoilGrids 2.0 and HiHydroSoil v2.0 provide global soil properties at 250 m resolution and six soil depths up to 2 m (https://doi.org/10.5194/soil-7–217-2021; https://www.futurewater.eu/projects/hihydrosoil/). The Global Multi-resolution Terrain Elevation Data 2010 supplies global elevation data at 7.5 arc-second resolution (https://doi.org/10.3133/ofr20111073).", "ds_quality": "
The accuracy of site-month soil freeze depth derived from MSFDmap, assessed by 10-fold cross-validation, is high with a root mean square error (RMSE) of 19.21 cm and a coefficient of determination (R2) of 0.91. Compared with data produced by existing schemes, RMSE decreases by 24–55 %, and R2 increases by 8–65 %. Site-month SFD series derived from the dataset shows a highly consistent trend with observed series, with a Pearson correlation coefficient (r) of 0.99 and RMSE of 9.13 cm at the site-average level; 83% of sites exhibited strong consistency between the two trends (r ≥ 0.8). Spatially, the dataset presents the expected latitudinal and altitudinal gradients of monthly SFD, with the pattern consistency (r) relative to ERA5-Land soil-temperature-based estimates is 0.6. Overall, the dataset effectively captures the spatiotemporal dynamics of monthly SFD in the pan-Arctic region and outperforms data produced by existing schemes, providing a valuable data basis for frozen soil research.", "ds_ref_way": "", "ds_abstract": "
Under the Arctic and winter amplification of warming, soil freeze depth (SFD) serves as a sensitive indicator of changes in pan-Arctic frozen soils. However, existing SFD mapping schemes fail to capture both spatiotemporal heterogeneity and physical constraints, and they mainly focus on annual maximum values. Consequently, high-precision datasets of monthly SFD in the pan-Arctic region are still lacking. We developed a monthly SFD mapping scheme (MSFDmap) by coupling the simplified Stefan equation with a random forest regression model, thereby integrating both physical constraints and spatiotemporal heterogeneity. The MSFDmap was implemented using 2123 site-month observations from 60 pan-Arctic sites over 20 years (water years 2004–2023), producing a monthly SFD dataset for the cold season (October–May) in the pan-Arctic region. In permafrost-underlain regions, data values represent the potential SFD—generally exceeding the active layer thickness—determined by assuming all soil heat loss is consumed for freezing. Validation against site-month observations shows high accuracy, with a root mean square error (RMSE) of 19.21 cm and a coefficient of determination (R2) of 0.91. Compared with data produced by existing schemes, RMSE decreases by 24–55 %, and R2 increases by 8–65 %. Site-month SFD series derived from the dataset shows a highly consistent trend with observed series, with a Pearson correlation coefficient (r) of 0.99 and RMSE of 9.13 cm at the site-average level; 83% of sites exhibited strong consistency between the two trends (r ≥ 0.8). Spatially, the dataset presents the expected latitudinal and altitudinal gradients of monthly SFD, with the pattern consistency (r) relative to ERA5-Land soil-temperature-based estimates is 0.6. Overall, the dataset effectively captures the spatiotemporal dynamics of monthly SFD in the pan-Arctic region and outperforms data produced by existing schemes, providing a valuable data basis for frozen soil research.", "ds_time_res": "", "ds_acq_place": "Pan Arctic region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) The observed soil freeze depth, determined from in-situ multi-layer soil temperature measurements, and the air freezing index, derived from air temperature records, were input into the simplified Stefan equation to calculate the site-month heat transfer factor.\r\n
(2) Based on environmental raster datasets, cumulative monthly average distributions of environmental variables were generated, and site-month values were extracted from them. \r\n
(3) Site-month environmental variables were then used as independent predictors to construct a random forest regression model of the monthly heat transfer factor. \r\n
(4) Driven by the cumulative monthly average distributions of environmental variables, the distribution of the monthly heat transfer factor was estimated through the random forest regression model.\r\n
(5) Monthly soil freeze depth was mapped with the simplified Stefan equation, using the distributions of the monthly heat transfer factor and air freezing index.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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": [ "土壤冻结深度", "热传递", "Stefan方程", "机器学习", "泛北极地区" ], "ds_subject_tags": [ "大气科学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泛北极地区" ], "ds_time_tags": [ 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 ], "ds_contributors": [ { "true_name": "陈力原", "email": "lychen@mail.bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "朱文泉", "email": "zhuwq75@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈力原", "email": "lychen@mail.bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "陈力原", "email": "lychen@mail.bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "极地" }