全球变暖引发多年冻土快速退化，青藏高原作为中低纬度多年冻土核心分布区，冻土退化对高寒生态、区域水资源与工程安全具有显著影响。本数据集为 1982-2020 年青藏高原多年冻土模拟结果：基于 CRU 降尺度月气温计算大气冻融指数，结合 N 系数、土壤参数，使用 TTOP 模型反演冻土顶板温度；收集活动层厚度实测数据，整合地形、气候、土壤、植被因子构建样本，通过机器学习获取 E 参数，利用 Stefan 公式模拟活动层厚度。成果经野外观测与文献资料交叉验证，可反映冻土温度、活动层厚度的时空变化，支撑高寒生态保护、水资源管理、冻土灾害评估和工程规划相关研究。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 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": "apply-access", "ds_total_size": 512968500, "ds_files_count": 2, "ds_format": "*.tif", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "6f8e0b91-c4c0-41f9-bc36-293b2537cf97.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.45", "170.4510" ], "quality_level": 0, "publish_time": "2026-10-20 16:26:59", "last_updated": "2026-10-20 17:01:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.permafrost.db7851.2026", "i18n": { "en": { "title": "Simulated Spatio-temporal Variation Dataset of Permafrost (TTOP and ALT) on the Qinghai-Tibet Plateau (1982–2020)", "ds_format": "*.tif", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Global warming is causing rapid degradation of permafrost across many regions. The Tibetan Plateau, being a key area for permafrost in mid-to-low latitude zones, is particularly vulnerable to the impacts of permafrost degradation on alpine ecosystems, regional water resources, and engineering safety. This dataset provides simulation results for the permafrost conditions in the Tibetan Plateau from 1982 to 2020. The data utilizes monthly temperature measurements from CRU’s downscaled datasets to calculate atmospheric freeze-thaw indices. Additionally, N coefficients and soil parameters are considered, along with the TTOP model to estimate the temperature of the permafrost surface layer. Actual thickness measurements of the active layer are collected, and these data are combined with factors such as topography, climate, soil, and vegetation to create a comprehensive dataset. Machine learning techniques are used to determine E parameters, while the Stefan formula is employed to simulate the thickness of the active layer. The results have been cross-verified through both field observations and literature studies. These findings can provide insights into the spatiotemporal variations in permafrost temperatures and active layer thicknesses, thereby supporting research in areas such as alpine ecosystem protection, water resource management, assessment of permafrost-related disasters, and engineering planning.
", "ds_time_res": "year", "ds_acq_place": "Qinghai-Tibet Plateau", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "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, "belong_to_nieer": false, "allow_update_data": false, "created_from": "uc", "ds_topic_tags": [ "多年冻土", "顶板温度", "活动层厚度" ], "ds_subject_tags": [ "地理学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 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, 2019, 2020 ], "ds_contributors": [ { "true_name": "陈方方", "email": "dalichen@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": "罗栋梁", "email": "luodongliang@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_meta_authors": [ { "true_name": "陈方方", "email": "dalichen@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": "罗栋梁", "email": "luodongliang@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": "罗栋梁", "email": "luodongliang@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": "陈方方", "email": "dalichen@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" }