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<p>River blockage serves as a critical link in the cascading hazards of post-earthquake debris flows. The early identification of potential debris flows that may block the river is essential for post-earthquake disaster prevention and risk reduction. Based on multi-source remote sensing images, field surveys and data collection, we compiled a dataset of river blockage events caused by debris flows following the 2008 Wenchuan earthquake. Simultaneously, we identified the primary controlling factors of the river blockages by post-earthquake debris flows and developed the prediction models based on data-driven and artificial intelligence. The results show that the root mean square error of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were 0.141, 0.134 and 0.086, respectively. Among the three models, LightGBM performed with higher prediction accuracy and lower variability. Based on Shapley Additive Explanation (SHAP), we found that the co-seismic landslide area connectivity (LSN) and the slope of the deposition area (FANS) are the first-order dominant controlling factors of the river blockage hazards caused by post-earthquake debris flows, followed by the topographic roughness index (TRI), the stream power index (SPI), the average slope of the catchment (CAS), the hypsometric integral (HI), the flow accumulation ratio (FR), and the stream steepness index (KSN). Subsequently, we further examined the relationship between the debris flow river-blockage index (PBR) and its influencing factors, and determined factor thresholds that correspond to the high hazard of river blockage. This research can provide insights for the prediction and risk mitigation of river blockage hazards caused by post-earthquake debris flows.</p>

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Quantitative forecasting of river-blocking catastrophes: decoupling the roles of geomorphic, hydrologic and seismic drivers in post-earthquake debris flow sequences

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • Ming Chen,
  • Chuan Tang,
  • Ming Chang,
  • Jiang Xiong

摘要

River blockage serves as a critical link in the cascading hazards of post-earthquake debris flows. The early identification of potential debris flows that may block the river is essential for post-earthquake disaster prevention and risk reduction. Based on multi-source remote sensing images, field surveys and data collection, we compiled a dataset of river blockage events caused by debris flows following the 2008 Wenchuan earthquake. Simultaneously, we identified the primary controlling factors of the river blockages by post-earthquake debris flows and developed the prediction models based on data-driven and artificial intelligence. The results show that the root mean square error of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were 0.141, 0.134 and 0.086, respectively. Among the three models, LightGBM performed with higher prediction accuracy and lower variability. Based on Shapley Additive Explanation (SHAP), we found that the co-seismic landslide area connectivity (LSN) and the slope of the deposition area (FANS) are the first-order dominant controlling factors of the river blockage hazards caused by post-earthquake debris flows, followed by the topographic roughness index (TRI), the stream power index (SPI), the average slope of the catchment (CAS), the hypsometric integral (HI), the flow accumulation ratio (FR), and the stream steepness index (KSN). Subsequently, we further examined the relationship between the debris flow river-blockage index (PBR) and its influencing factors, and determined factor thresholds that correspond to the high hazard of river blockage. This research can provide insights for the prediction and risk mitigation of river blockage hazards caused by post-earthquake debris flows.