Landslide-induced river damming poses a considerable threat to the safety of humans and infrastructure. Prediction of landslide-induced river damming is of great significance for quantitative risk assessment and emergency response planning. However, owing to the large uncertainties embedded in the input parameters and dynamic numerical models, a reliable physically based prediction of landslide dam formation is still challenging. In this study, we proposed a probabilistic framework to predict rockslide-induced river damming and its associated barrier lake. Both parameter and model uncertainties were considered to reproduce the run-out process and the deposition behavior of the Baige landslide based on dynamic numerical simulation, while calibrating the input parameters through a sequential Bayesian back analysis. The first slide of the Baige landslide was considered to calibrate the input parameters with observations at several deposition depths. The proposed method was validated by predicting the second slide-induced river damming using the calibrated results from the first slide. Furthermore, the second slide with deposition depth observations was employed to update the input parameters again. Through the sequential Bayesian back analysis, the probability of the river damming induced by the potentially unstable rock masses was predicted, which yielded a hazard zonation map of the barrier lake exceeding various water levels. This hazard zonation map may be employed to guide quantitative risk assessment and corresponding emergency response plans for future landslide-induced upstream backwater-inundation.

山体滑坡引起的河流筑坝对人类和基础设施的安全构成了相当大的威胁。滑坡诱发河流筑坝预测对于定量风险评估和应急响应规划具有重要意义。然而，由于输入参数和动态数值模型中存在很大的不确定性，可靠的基于物理的滑坡坝形成预测仍然具有挑战性。在这项研究中，我们提出了一个概率框架来预测滑坡引起的河流筑坝及其相关的堰塞湖。在动态数值模拟的基础上，考虑参数和模型的不确定性来再现白格滑坡的跳动过程和沉积行为，同时通过顺序贝叶斯反分析校准输入参数。Baige 滑坡的第一张滑坡被认为是通过在几个沉积深度的观测来校准输入参数。通过使用第一个幻灯片的校准结果预测第二个幻灯片引起的河流筑坝来验证所提出的方法。此外，使用具有沉积深度观察的第二张幻灯片再次更新输入参数。通过序贯贝叶斯反分析，预测了潜在不稳定岩体引起的河流筑坝概率，得到了堰塞湖超过各种水位的危险分区图。该灾害分区图可用于指导未来滑坡引起的上游回水淹没的定量风险评估和相应的应急响应计划。