Seismic Monitoring and Geomorphic Impacts of the Catastrophic 2018 Baige Landslide Hazard Cascades in the Tibetan Plateau

Abstract

Surface hazards can form hazard cascades which expand their reach. However, our understanding of their complex dynamics and ability to mitigate their impacts remain limited. In 2018, two landslides dammed the Jinsha River in the Tibetan plateau and formed landslide‐dammed lakes. Subsequent dam breaches prompted the evacuation of >120,000 people. An early warning system for floods using a regional seismic network has been proposed on the basis of catastrophic floods having been detected ∼100 km away, with seismic energy proportional to discharge. Surprisingly, we find that this catastrophic outburst flood was undetectable beyond a few kilometers, with peak seismic energy preceding peak discharge. We propose that river channel stability also controls seismic energy generation and should be considered for accurate monitoring of catastrophic floods. In contrast, we find that the various processes during dam breach can be well‐characterized seismically further away and provide warning ∼60 min before discharge exceeds monsoon flood levels. We also show that numerical modeling of dam breaches which typically lacks in situ measurements can benefit from incorporating seismic data as constraints. Finally, we show that this event drastically increased sediment fluxes ∼670 km downstream for years and may significantly reduce the capacity of hydropower plants. Our results reveal ways to improve early warning of catastrophic outburst floods and the need to consider surface hazards' long‐term impact when managing infrastructure in mountainous regions.

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

“…Exploring the factors that trigger these responses will be an intriguing avenue for future research. Considering that this creeping landslide case occurs on a seismically active Qinghai-Tibet Plateau, which undergoes freeze-thaw cycles and is affected by temperature and humidity variations, a rich tapestry of factors should be considered (W. Fan et al, 2023;Z. Zhang et al, 2024;Yan et al, 2022).…”

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

“…As a result, the prediction of landslide displacement has become a pivotal research focus within the field of disaster prevention and reduction. Accurate displacement prediction can significantly enhance our ability to anticipate and mitigate the risks associated with landslides, thereby saving lives and reducing economic losses in susceptible areas [1][2][3][4][5].…”