本数据集提供了一套1980-2018年青藏高原逐年冻土分布产品，采用基于地图的参数校准策略（CaliMAP），以高精度的2010年青藏高原多年冻土分布图作为空间约束目标，通过贝叶斯优化算法，对Noah-Tibet陆面过程模型的26个异质性敏感参数进行优化，最终提取13组最优参数集进行集合模拟。\n
本数据集包含了1980至2018年共39个GeoTIFF格式的年度栅格文件，空间分辨率为0.1°，采用Albers等面积圆锥投影。\n
栅格值分类如下：1代表多年冻土（Permafrost），2代表季节性冻土（Seasonally Frozen Ground），3代表无冻土（Non-frozen ground），冰川、湖泊及研究区外区域均被掩膜为NoData。", "ds_source": "
本数据集的生成基于多源气象驱动数据、地表环境数据、高精度基准约束图以及用于独立验证的实地观测数据。主要数据源包括：\n
1. 气象驱动数据：采用中国区域地面气象要素驱动数据集 (He et al., 2020)，包含1979-2018年0.1°/3小时分辨率的近地面气温、气压、比湿、全风速、向下短波辐射通量、向下长波辐射通量、降水率共7个气象要素，获取于https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file.。\n
2. 地表环境数据：植被类型来自《中华人民共和国植被图（1:1 000 000）》(Zhang, 2007)，获取于https://www.plantplus.cn/doi/10.12282/plantdata.0155；叶面积指数（LAI）气候态来自GIMMS产品 (Zhu et al., 2013)；土壤质地数据来自《青藏高原多层土壤质地数据集》 (Wu et al., 2016)。\n
3. 空间校准基准图：用作空间约束目标的青藏高原2010年多年冻土分布图(Cao et al., 2023) ，获取于https://doi.org/10.12072/ncdc.permalab.db3965.2023。\n
4. 冰川与湖泊掩膜数据：用于剔除水体和冰川面积。冰川掩膜来自《中国第二次冰川编目数据集（v1.0）（2006–2011）》(Guo et al., 2015)，获取于https://doi.org/10.3972/glacier.001.2013.db.；湖泊动态掩膜来自《青藏高原大于1平方公里湖泊数据集（v3.1）（1970s-2022）》(Zhang et al., 2019)，获取于https://www.tpdc.ac.cn/zh-hans/data/7fee8675-d4ab-4f97-8bbf-269e20b7ac16/。\n
5. 验证数据：包含来自12个活动层监测站点和84个钻孔的实测数据(Zhao et al., 2021)，获取于https://doi.org/10.11888/Geocry.tpdc.271107.。", "ds_process_way": "
基于改进的Noah-Tibet陆面过程模型，具体流程如下：首先，模型利用1979-1983年的气象强迫数据循环运行500年进行Spin-up，以建立初始的土壤水热准平衡态，随后进行1979-2018年的瞬态模拟。其次，采用基于地图的参数校准策略（CaliMAP）：在敏感过渡区，采用贝叶斯优化算法，以最大化2010年冻土模拟结果与2010基准图的Kappa系数为目标，对26个异质性敏感参数进行调优。从迭代结果中提取13组最优参数集，取集合模拟平均。最后，根据15.2米深度的模拟土壤温度剖面进行像素重分类：若至少一层连续两年温度≤0 ℃则划为多年冻土(1)；有季节性冻结但不足两年的划为季节性冻土(2)；全年未冻结的划为无冻土(3)。同时应用冰川（静态）和湖泊（动态）掩膜设为NoData。", "ds_quality": "
模型验证采用了完全独立于空间校准过程的实测数据。验证结果显示，活动层厚度（ALT）的均方根误差（RMSE）为0.68米（n=83）；多年冻土顶面温度（TTOP）的RMSE为0.41 ℃（n=83）；10米深地温（GT10m）的RMSE为1.30 ℃（n=291）。在多年代际变化趋势的验证中，ALT变化率（8个站点）的RMSE仅为0.08 m/10a，TTOP变化率（8个站点）的RMSE为0.36 ℃/10a，而GT10m变化率（38个钻孔位点）的RMSE为0.64 ℃/10a。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 418408, "ds_files_count": 2, "ds_format": "GeoTIFF", "ds_space_res": "0.1度", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic projection", "ds_thumbnail": "338c21b5-7fdd-42de-b701-39fc3abcc2cf.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2026-03-19 16:36:10", "last_updated": "2026-03-20 17:50:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PERMALAB.DB7196.2026", "i18n": { "en": { "title": "A map-constrained dataset of frozen ground distribution changes on the Qinghai-Tibetan Plateau from 1980 to 2018", "ds_format": "GeoTIFF", "ds_source": "
The generation of this dataset is based on multi-source meteorological forcing data, land surface environmental data, a high-fidelity benchmark constraint map, and in-situ observation data used for independent validation. The primary data sources include:\n
1. Meteorological forcing data: The China meteorological forcing dataset (He et al., 2020) was adopted, containing 7 meteorological elements (near-surface air temperature, surface pressure, specific humidity, wind speed, downward shortwave radiation, downward longwave radiation, and precipitation rate) at a spatial resolution of 0.1° and a temporal resolution of 3 hours from 1979 to 2018. Retrieved from https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file.\n
2. Land surface environmental data: Vegetation types were derived from the Vegetation Map of the People's Republic of China (1:1,000,000) (Zhang, 2007), retrieved from https://doi.org/10.12282/plantdata.0155; Leaf Area Index (LAI) climatology was sourced from the GIMMS product (Zhu et al., 2013); Soil texture data were obtained from A Multilayer Soil Texture Dataset for the Qinghai-Tibetan Plateau (Wu et al., 2016).\n
3. Spatial calibration benchmark map: The 2010 permafrost distribution map over the Qinghai-Tibet Plateau (Cao et al., 2023), used as the spatial constraint target, was retrieved from https://doi.org/10.12072/ncdc.permalab.db3965.2023.\n
4. Glacier and lake mask data: Used to exclude water bodies and glacier areas. The glacier mask was derived from The Second Glacier Inventory Dataset of China (version 1.0) (2006–2011) (Guo et al., 2015), retrieved from https://doi.org/10.3972/glacier.001.2013.db; the dynamic lake masks were sourced from The lakes larger than 1 km² in Tibetan Plateau (v3.1) (1970s-2022) (Zhang et al., 2019), retrieved from https://www.tpdc.ac.cn/zh-hans/data/7fee8675-d4ab-4f97-8bbf-269e20b7ac16/.\n
5. Validation data: Consists of in-situ measurement data from 12 active layer monitoring sites and 84 boreholes (Zhao et al., 2021), retrieved from https://doi.org/10.11888/Geocry.tpdc.271107. .", "ds_quality": "
The dataset’s thermodynamic reliability was evaluated against an extensive network of independent in-situ observations across the QTP. Crucially, these observation sites were strictly spatially independent from the data used in the CaliMAP procedure. \nThe assessment focused on three key permafrost thermal state variables derived from the simulated soil temperatures: ALT, TTOP, and ground temperature at a 10m depth (GT10m). Performance was quantified primarily using the root mean square Error (RMSE). The evaluation yielded an RMSE of 0.68 m for ALT (n= 83 samples), 0.41 °C for TTOP (n=83 samples), and 1.30 °C for GT10m across a comprehensive compilation of 291 samples.\n
The simulated long-term change rates were further evaluated against observed multi-year time series. The dataset achieved an RMSE of 0.08 m/10a for ALT change rates (n=8 sites), 0.36 °C/10a for TTOP change rates (n=8 sites), and 0.64 °C/10a for GT10m change rates (n=38 borehole locations).", "ds_ref_way": "", "ds_abstract": "
This dataset provides a set of annual frozen ground distribution products for the Qinghai-Tibetan Plateau from 1980 to 2018. It employed a Map-based Calibration(CaliMAP) strategy, utilizing a highly accurate reference map (hereafter referred to as the 2010 map) as a region-wide spatial constraint. Through a Bayesian optimization algorithm, 26 heterogeneous sensitive parameters of the Noah-Tibet land surface model were optimized, and 13 best-performing parameter combinations from the optimization posterior were selected to construct a parameter ensemble.\n
This dataset contains 39 annual raster files in GeoTIFF format from 1980 to 2018, with a spatial resolution of 0.1°, utilizing the Albers Equal Area Conic projection.\n
The raster pixel values are classified as follows: 1 represents Permafrost, 2 represents Seasonally Frozen Ground (SFG), and 3 represents Non-frozen ground. Glaciers, lakes, and areas outside the study region are all masked as NoData.", "ds_time_res": "年", "ds_acq_place": "Qinghai-Tibet Plateau", "ds_space_res": "0.1度", "ds_projection": "", "ds_process_way": "
Based on the modified Noah-Tibet land surface model, the specific workflow is as follows: First, to establish quasi-equilibrium initial thermal and hydrological conditions, the model underwent a 500-year spin-up by repeatedly recycling the first five years of forcing data (1979–1983). Following this spin-up phase, the main simulation was driven by the transient climate forcing from 1979 to 2018. Second, we implemented the CaliMAP strategy utilizing a parameter-ensemble approach. To improve optimization efficiency and reduce parameter equifinality, the calibration was focused exclusively on \"sensitive regions\", i.e., transition zones encompassing over 13,000 cells where initial default simulations disagreed with the 2010 map. Within these sensitive regions, 26 heterogeneous parameters were tuned using a Bayesian optimization algorithm to maximize the Kappa coefficient of agreement between the simulated 2010 permafrost distribution and the 2010 map. Rather than selecting only the single deterministic optimal parameter set, we ranked all evaluated sets based on their final Kappa coefficients and selected 13 best-performing parameter combinations from the optimization posterior to construct a parameter ensemble. The Noah-Tibet model was then driven by these 13 optimal parameter sets to generate 13 independent historical simulations (1980–2018). The final frozen ground distribution dataset represents the ensemble mean of these 13 simulations. Finally, pixel reclassification was physically based on the simulated thermal state of the 15.2 m soil column. Specifically, a grid cell is classified as permafrost if the simulated soil temperature of at least one layer remains at or below 0 °C continuously throughout both the current and the preceding year. Terrestrial cells that do not meet this continuous freezing criterion are further differentiated based on their seasonal thermal dynamics: they are designated as SFG if the soil experiences periodic freezing during the cold season, or as non-frozen ground if the entire soil column remains unfrozen year-round. Furthermore, modeling cells corresponding to existing glaciers and large lakes are explicitly excluded from the frozen ground classification.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "青藏高原", "冻土分布", "长时序" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "南卓铜", "email": "nanzt@shnu.edu.cn", "work_for": "上海师范大学", "country": "中国" }, { "true_name": "嵇海龙", "email": "jihailongnnu@gmail.com", "work_for": "南京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "南卓铜", "email": "nanzt@shnu.edu.cn", "work_for": "上海师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "南卓铜", "email": "nanzt@shnu.edu.cn", "work_for": "上海师范大学", "country": "中国" }, { "true_name": "嵇海龙", "email": "jihailongnnu@gmail.com", "work_for": "南京师范大学", "country": "中国" } ], "category": "冻土" }