{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T04:51:14Z","timestamp":1766724674691,"version":"3.48.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of Heilongjiang Power Grid Company","award":["522448250003"],"award-info":[{"award-number":["522448250003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Permafrost degradation under climate warming has profound implications for ecological processes, hydrology, and human activities. Northeast China, characterized by sporadic and isolated patch permafrost near the southern limit of latitudinal permafrost (SLLP), represents one of the most sensitive and complex permafrost regions. This study aims to improve the reliability of permafrost mapping by incorporating parameter uncertainty into simulations. We developed a Monte Carlo\u2013Temperature at the Top of Permafrost (TTOP) (MC\u2013TTOP) framework that integrates an equilibrium model with Monte Carlo sampling to quantify parameter sensitivity and model uncertainty. Using all-sky daily air temperature data and land use and land cover information, we generated probabilistic estimates of mean annual ground temperature (MAGT), permafrost occurrence probability (PZI), and associated uncertainties. Model validation against borehole observations demonstrated improved accuracy compared with global-scale simulations, with a reduced bias and RMSE. Results reveal that permafrost in Northeast China was relatively stable during 2003\u20132010 but experienced pronounced degradation after 2011, with the total area decreasing to ~2.79 \u00d7 105 km2 by 2022. Spatial uncertainty was greatest in transitional zones near the southern boundary, where PZI-based delineations tended to overestimate permafrost extent. Regional comparisons further showed that permafrost in Northeast China is more fragmented and uncertain than that on the Tibetan Plateau, owing to complex snow\u2013vegetation\u2013topography interactions and intensive human disturbances. Overall, the MC-TTOP simulations indicate an accelerated permafrost degradation after 2011, with the highest uncertainty concentrated in southern transitional zones near the SLLP, demonstrating that the MC-TTOP framework provides a robust tool for probabilistic permafrost mapping, offering improved reliability for regional-scale assessments and important insights for future risk evaluation under climate change.<\/jats:p>","DOI":"10.3390\/ijgi15010009","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T03:06:02Z","timestamp":1766718362000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Monte-Carlo-Based Method for Probabilistic Permafrost Mapping Across Northeast China During 2003 to 2022"],"prefix":"10.3390","volume":"15","author":[{"given":"Yao","family":"Xiao","sequence":"first","affiliation":[{"name":"State Grid Heilongjiang Economic and Technological Research Institute, Harbin 150010, China"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Grid Heilongjiang Economic and Technological Research Institute, Harbin 150010, China"}]},{"given":"Shuqi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Heilongjiang Economic and Technological Research Institute, Harbin 150010, China"}]},{"given":"Xuyang","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8696-0218","authenticated-orcid":false,"given":"Kai","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Yunhu","family":"Shang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1038\/s41467-018-08240-4","article-title":"Permafrost is warming at a global scale","volume":"10","author":"Biskaborn","year":"2019","journal-title":"Nat. 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