多年冻土地下冰是关键的固态水资源与工程地质载体。青藏工程走廊高含冰量冻土对退化响应极为敏感，制约了气候模型对冻土退化的精准模拟，也不利于冰冻圈脆弱区工程灾害防控措施的科学制定。\n
本研究基于 1158 组地下钻孔实测数据（实测、工程勘察钻孔资料及公开文献与科学数据中心共享资料），引入随机森林与决策树两种机器学习算法，结合年平均气温、地温、海拔、坡度、植被指数等13项环境因子，实现了走廊全线200m分辨率的地下冰含量空间模拟。结果显示，青藏工程走廊范围内50% 区域发育高含冰量冻土；年均气温、地温、活动层厚度、海拔与降水是控制地下冰空间分异的主导因子，详细内容见(Fan et al., 2026)。\n
本研究可为区域地下冰储量精细化评估提供基础本底数据，同时为冻土区生态保护与工程建设设计提供科学支撑。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "青藏工程走廊", "ds_acq_lon_east": 104.0, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 73.0, "ds_acq_lat_north": 39.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 3460876, "ds_files_count": 4, "ds_format": "*.tif", "ds_space_res": "200m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "59b8b58d-f507-4a80-a190-e85132a4e67b.png", "ds_thumb_from": 2, "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": 0, "publish_time": "2026-05-11 11:05:02", "last_updated": "2026-05-11 11:13:23", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.permafrost.db7317.2026", "i18n": { "en": { "title": "Permafrost underground ice content in the Qinghai Tibet Engineering Corridor (QTEC)", "ds_format": "*.tif", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Permafrost and underground ice are key solid water resources and engineering geological carriers. The high ice content permafrost in the Qinghai Tibet Engineering Corridor is extremely sensitive to degradation response, which restricts the accurate simulation of permafrost degradation by climate models and is not conducive to the scientific formulation of engineering disaster prevention and control measures in vulnerable areas of the cryosphere.\r\n
This study is based on 1158 sets of measured data from underground boreholes (measured data, engineering survey borehole data, and shared data from public literature and scientific data centers). Two machine learning algorithms, random forest and decision tree, are introduced, combined with 13 environmental factors such as annual average temperature, ground temperature, altitude, slope, vegetation index, etc., to achieve spatial simulation of underground ice content at a resolution of 200m along the entire corridor. The results show that 50% of the areas within the Qinghai Tibet Engineering Corridor are developed with high ice content permafrost; The annual average temperature, ground temperature, active layer thickness, altitude, and precipitation are the dominant factors controlling the spatial differentiation of underground ice, as detailed in Fan et al. (2026).\r\n
This study can provide basic background data for the refined assessment of regional underground ice reserves, and also provide scientific support for ecological protection and engineering construction design in permafrost areas.", "ds_time_res": "", "ds_acq_place": "Qinghai Tibet Engineering Corridor", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 1, "recommendation_value": 2, "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, "ds_topic_tags": [ "多年冻土地下冰", "青藏工程走廊（QTEC）", "机器学习", "含冰量" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏工程走廊" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "范星文", "email": "fanxingwen@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": "林战举", "email": "zhanjulin@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": "高泽永", "email": "gaozy@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": "罗京", "email": "luojing@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": "李文娇", "email": "liwenjiao21@mails.ucas.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": "吴旭阳", "email": "wuxuyang@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": "范星文", "email": "fanxingwen@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": "林战举", "email": "zhanjulin@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": "高泽永", "email": "gaozy@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": "罗京", "email": "luojing@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": "李文娇", "email": "liwenjiao21@mails.ucas.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": "吴旭阳", "email": "wuxuyang@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_managers": [ { "true_name": "范星文", "email": "fanxingwen@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": "林战举", "email": "zhanjulin@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" }