Mapping of the susceptibility of China‒Russia crude oil pipelines to water damage in permafrost regions in Northeast China
In permafrost regions, climate warming and extreme precipitation events, combined with rugged local terrains, pose considerable threats of water damage to buried crude oil pipelines. However, the susceptibility to water damage in these areas has received limited attention and research. Aiming to eva...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Advances in Climate Change Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674927825000814 |
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| Summary: | In permafrost regions, climate warming and extreme precipitation events, combined with rugged local terrains, pose considerable threats of water damage to buried crude oil pipelines. However, the susceptibility to water damage in these areas has received limited attention and research. Aiming to evaluate the susceptibility to water damage (STWD) of the China‒Russia Crude Oil Pipelines (CRCOPs) I and II, random forest (RF) algorithms, correlation analysis of influencing factors and on-site surveys were employed. The assessment, based on RF algorithms, field survey data from 2019 to 2022 and 14 geographically related factors, reveals that approximately 14.5% of the study area demonstrates high STWD, indicating a generally low risk of STWD across most segments of the CRCOPs. The pipeline segments between Wu’erqi–Jagdaqi and Jingsong–Xinlin display the highest STWD. Areas with high STWD typically experience ample precipitation, flow accumulation in flat, low-lying terrains, low surface roughness, over unconsolidated deposits and warm (>−1 °C) Xing’an (hemiboreal) permafrost and proximity to rivers. This study not only enhances theoretical understanding of mitigating water damage to pipeline foundations in cold regions but also offers important technical insights for the sustainable operation of these lifeline infrastructures. Future research should focus on continuous monitoring of pipeline foundation soil safety, improving numerical models for pipeline river crossing evaluations and refining water damage risks assessment through deep learning-based models. |
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| ISSN: | 1674-9278 |