Jianlin Zhou

Beijing Normal University

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Shu‐zhong Shen

Nanjing University

Mengchun Cao

Nanjing University

Feifei Zhang

Nanjing University

Na Li

University of Science and Technology of China

Guang‐Yi Wei

Nanjing University

Yongbo Peng

Nanjing University

Hua Zhang

Beijing Normal University

Jiří Frýda

Czech Geological Survey

Zhenguo Wang

University of Michigan–Ann Arbor

Guolin Xiong

Nanjing University

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Nanjing University

Chinese Academy of Sciences

Beijing Normal University

Ministry of Natural Resources

Nanjing Institute of Geology and Paleontology

Peking University

Chengdu University of Technology

China University of Geosciences

Czech Academy of Sciences, Institute of Geology

University of Chinese Academy of Sciences

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Marine anoxia as a trigger for the largest Phanerozoic positive carbon isotope excursion: Evidence from carbonate barium isotope record

Earth and Planetary Science Letters (2022)

Feifei Zhang Jiří Frýda Mojtaba Fakhraee Yibo Lin Guang‐Yi Wei

Excursion

Isotopic shift

10.1016/j.epsl.2022.117421

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Assessing bulk carbonates as archives for seawater sulfur isotopic composition using shallow water cores from the South China Sea

Palaeogeography Palaeoclimatology Palaeoecology (2022)

Na Li Feifei Zhang Jianhua Gao Mengchun Cao Guang‐Yi Wei

δ34S

10.1016/j.palaeo.2022.111029

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Assessment of landslide susceptibility in watersheds during extreme rainfall using a complex network of slope units

Scientific Reports (2025)

Chenlu Wang Jianlin Zhou Zhenguo Wang Youtian Yang Jingyi Lu

Rainfall-induced landslides present significant challenges in regional landslide prediction and management. Traditional regional landslide susceptibility assessment models often evaluate individual units in isolation, neglecting the hydrological connections between slope units within a watershed. This approach fails to account for the occurrence of landslides in groups. To address this limitation, we propose the "Network-based Landslide Susceptibility Assessment Model" (NLSAM). This model incorporates the impact of water transfer between slope units using a complex network and integrates a physically-based model to account for interactions between slopes. In this study, we applied the NLSAM to a watershed in Fuyang District, Zhejiang Province, China. Experimental results show that extreme rainfall increases water transfer between slope units, destabilizing more slopes and elevating landslide susceptibility. Validation results demonstrate that the recall of NLSAM is 0.93, confirming the model's ability to identify group-occurring landslides. NLSAM captures rainfall propagation paths and quantifies their impacts, assisting decision-makers in formulating more effective management strategies.

10.1038/s41598-025-89039-4

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