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To clarify the mechanisms by which climate change affects the integrity of buried pipelines， this study developed a comprehensive probabilistic assessment framework grounded in physical mechanisms. The framework integrates predictive data from the 6th Coupled Model Intercomparison Project Phase 6 (CMIP6)， a corrosion-driven leakage model based on soil chloride-ion-induced electrochemistry， and a Monte Carlo uncertainty-propagation analysis method. The study used the Delta downscaling method and the CN05.1 grid dataset to bias-correct temperature data from the CESM2-WACCM model in CMIP6. It optimized the probability distribution model for key environmental variables using the Akaike Information Criterion (AIC) and verified the model’s rationality with quantile-quantile plots and Kolmogorov-Smirnov tests. Based on the Shared Socioeconomic Pathways (SSPs) simulation of environmental degradation scenarios， five typical climate cities in China， namely Chengdu， Wuhan， Yingtan， Fuzhou， and Guangzhou， were selected to evaluate the spatiotemporal evolution of pipeline corrosion rate and failure probability under different radiation forcing levels from SSP1-2.6 to SSP5-8.5. The Sobol index method was used to quantify the sensitivity of pipeline failure probability to parameters. The results indicate significant regional heterogeneity in the risk of climate-driven pipeline degradation and failure. Under the SSP5-8.5 high radiation-forcing scenario in 2100， the corrosion rate of pipelines in Guangzhou exceeds 4 mm/a. In contrast， in Chengdu and Wuhan， it is less than 3 mm/a， with regional differences exceeding 1 mm/a. The corrosion rate in all cities increases monotonically over time. The probability of pipeline failure continues to increase with rising annual average temperature and radiation forcing， exhibiting a significant spatial distribution characterized by “high in the south and low in the north”. The increase in failure risk in southern cities， such as Guangzhou and Fuzhou， is much higher than in central and western cities. Temperature is the dominant factor affecting the probability of pipeline failure， followed by chloride ion concentration， and pH has the lowest sensitivity. These findings quantify the potential threats of long-term climate change to infrastructure， providing a theoretical basis for pipeline lifecycle risk design， monitoring， and planning， as well as the development of climate adaptation strategies.
[1] Shen Yufei， Zhou Wenxing. Finite element analyses of burst capacities of corroded pipelines containing pinhole-in-corrosion defects[J]. Engineering Failure Analysis， 2024， 163: 108553.
[2] 张来斌，胡瑾秋，肖尚蕊. 油气智慧管道“端+云+大数据”跨域“信息-物理”安全保障技术现状及发展趋势[J].安全与环境学报，2023，23(06):1825-1836.
[3] Wang Yihuan， Zhu Shiyi， Wang Bohong， et al. Structural health monitoring of oil and gas pipelines: Developments， applications and future directions[J]. Ocean Engineering， 2024， 308: 118293.
[4] Brown J. CO2 pipelines: navigating the complexities and nuances through expert opinions[R]. Washington DC: Institute for Carbon Removal and Law， 2021.
[5] Harris S J， Schwietzke S， France J L， et al. Methane emissions from the Nord Stream subsea pipeline leaks[J]. Nature， 2025， 637(8048): 1124-1130.
[6] Andrade D M， Areias B S， Costa M L M， et al. Failure of composite repair systems used in corroded pipelines due to pressure transients[J]. Engineering Failure Analysis， 2025， 171: 109368.
[7] Soomro A A， Mokhtar A A， Kurnia J C， et al. Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review[J]. Engineering Failure Analysis， 2022， 131: 105810.
[8] Medland CJ， Chenoweth J， Mulheron M. Infrastructure climate change resilience: A review of resilience assessment frameworks[C]//Proceedings of the Institution of Civil Engineers-Engineering Sustainability. Leeds， West Yorkshire， UK: Emerald Publishing Limited， 2024: 1-14.
[9] 秦国晋，罗杰，张鹏.基于耦合协调模型的区域天然气产业“可能三角”剖析：以长江三角洲地区为例[J].天然气工业，2025，45(02):170-179.
[10] Goerlandt F， Li J. Forty years of risk analysis: a scientometric overview[J]. Risk Analysis， 2022， 42(10): 2253-2274.
[11] H?rteborn A， Ringsberg J W， Lundb?ck O， et al. Probabilistic analysis of ship-bridge allisions when designing bridges[J]. Reliability Engineering & System Safety， 2025， 260: 111026.
[12] Van Deventer C G. Guidelines for predicting the remaining life of underground pipe networks that are subjected to the combined effects of external corrosion and internal pressure[D]. Pretoria: University of Pretoria (South Africa)， 2002.
[13] Chung N T， So Y S， Kim W C， et al. Evaluation of the influence of the combination of ph， chloride， and sulfate on the corrosion behavior of pipeline steel in soil using response surface methodology[J]. Materials， 2021， 14(21): 6596.
[14] Xu Lei， Wen Shaomu， Huang Hongfa， et al. Corrosion failure prediction in natural gas pipelines using an interpretable XGBoost model: Insights and applications[J]. Energy， 2025， 325: 136157.
[15] 梁瑞，王妮鹏，周文海，等. 基于智能优化与特征选择的埋地管道腐蚀深度预测研究[J/OL].安全与环境学报， 2025 (网络首发) :1-11[2026-02-02].https://doi.org/10.13637/j.issn.1009-6094.2025.1319.
[16] Tuninetti V， Huentemilla M， Gomez A， et al. Evaluating pipeline inspection technologies for enhanced corrosion detection in mining water transport systems[J]. Applied Sciences， 2025， 15(3): 1316.
[17] Sung S H， Song S W， Kim S J， et al. A SHAP value method for ultimate strength prediction of stiffened panel: A data-driven tool in engineering[J]. Ocean Engineering， 2026， 343: 123159.
[18] Zhang Jia， Sun Mingnan， Qin Lin， et al. In-line inspection methods and tools for oil and gas pipeline: A review[J]. International Journal of Pressure Vessels and Piping， 2025， 214: 105409.
[19] Cai Zhichao， Liu Yunsheng， Liu Xuehua， et al. Prediction of interference current of buried pipeline and study on corrosion of Q235A steel[J]. Construction and Building Materials， 2023， 400: 132739.
[20] Xie Wen， Jia Wenlong， Mou Lei， et al. Big data-driven quantitative analysis of influencing factors importance for internal corrosion in sour gas pipelines[J]. Process Safety and Environmental Protection， 2025，200: 107432.
[21] 巴振宁，王鸣铄，梁建文，等. 改进的一次二阶矩计算埋地管道失效概率方法及其应用[J]. 安全与环境学报， 2021， 21 (06): 2371-2377.
[22] Naserzadeh Z， Nohegar A. Development of corrosion prediction approach for natural gas pipelines: A novel deep learning SVM method[J]. Iranian Journal of Chemistry and Chemical Engineering， 2025， 44(2): 572-582.
[23] 何婷，杨松，张开，等. 水平定向钻载荷下天然气管道失效智能预测研究[J].安全与环境学报， 2025， 25(04): 1370-1379.
[24] 王艺环，张真维，秦国晋. 含重叠腐蚀缺陷的氢气管道失效智能预测模型研究[J].安全与环境学报， 2025， 25(09):3473-3481.
[25] Eyring V， Bony S， Meehl G A， et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development， 2016， 9(5): 1937-1958.
[26] 周天军，邹立维，陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述[J].气候变化研究进展， 2019， 15(05):445-456.
[27] 秦晓霞. 埋地管道土壤腐蚀性与防护研究[D]. 北京: 中国石油大学， 2009.
[28] Shabangu T H， Ponnle A A， Adedeji K B， et al. Effects of soil properties on corrosion of buried steel pipeline: a case study of Rand Water Pipeline， South Africa[C]//Proceedings of the 2015 AFRICON Conference. Piscataway: Institute of Electrical and Electronics Engineers， 2015: 1-5.
[29] Yu Weichao， Song Shangfei， Li Yichen， et al. Gas supply reliability assessment of natural gas transmission pipeline systems[J]. Energy， 2018， 162: 853-870.
[30] Simillion H， Dolgikh O， Terryn H， et al. Atmospheric corrosion modeling[J]. Corrosion Reviews， 2014， 32(3-4): 73-100.
[31] Chung N T， Choi S R， Kim J G. Comparison of response surface methodologies and artificial neural network approaches to predict the corrosion rate of carbon steel in soil[J]. Journal of The Electrochemical Society， 2022， 169(5): 051503.
[32] Qin Guojin， Cheng Yufeng Frank， Zhang Peng. Finite element modeling of corrosion defect growth and failure pressure prediction of pipelines[J]. International Journal of Pressure Vessels and Piping， 2021， 194: 104509.
[33] Liu Aihua， Chen Ke， Huang Xiaofei， et al. Corrosion failure probability analysis of buried gas pipelines based on subset simulation[J]. Journal of Loss Prevention in the Process Industries， 2019， 57: 25-33.
[34] Al-Amin M， Kariyawasam S， Zhang S， et al. Non-linear corrosion growth: a more appropriate and accurate model for predicting corrosion growth rate[C]//Proceedings of the 2016 International Pipeline Conference. New York: American Society of Mechanical Engineers， 2016: V001T03A021.
[35] Canadian Standards Association. CSA Z662: Oil and gas pipeline systems [S]. Toronto: Canadian Standards Association， 2007.
[36] Al-Amin M， Zhou Wenxing. Evaluating the system reliability of corroding pipelines based on inspection data[J]. Structure and Infrastructure Engineering， 2014， 10(9): 1161-1175.
[37] Zhou Wenxing， Xiang Wei， Hong Hanping. Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects[J]. Reliability Engineering & System Safety， 2017， 167: 428-438.
[38] 杨小玲.CMIP6优选模式集合对中国温度和降水的模拟评估与预估[D]. 南京: 南京信息工程大学， 2022.
[39] 吴佳， 高学杰. 一套格点化的中国区域逐日观测资料及与其它资料的对比[J].地球物理学报， 2013， 56(04): 1102-1111.
[40] Goerlandt F， Reniers G. Prediction in a risk analysis context: Implications for selecting a risk perspective in practical applications[J]. Safety Science， 2018， 101: 344-351.
[41] 中国腐蚀与防护学会. 2023年中国腐蚀调查报告[R].北京: 化学工业出版社，2023.
Basic Information:
DOI：10.13637/j.issn.1009-6094.2026.0202
China Classification Code:TG172;U178;P467
Citation Information:
[1]QIN Guojin,JIN Dan,WANG Yihuan.Spatiotemporal risk evolution of corroded pipelines considering climate change[J].Journal of Safety and Environment().DOI:10.13637/j.issn.1009-6094.2026.0202.
Fund Information:
国家重点研发计划项目（2024YFC3810702）; 国家自然科学基金项目（52304258）
2026-08-11
2026-08-11
2026-08-11