Assessing the impact of road on sensitivity of large slow-moving landslides to precipitation by integrating multi-temporal InSAR and panel regression

Abstract

Human-landscape interactions in mountainous terrains are complex and multi-faceted. Settlements tend to focus on relatively gently undulating terrains, which are often found in areas where ground conditions are weak and thus substantial ground movements prevail. Interventions in these precarious landscapes, such as progressive expansion of interconnecting transport infrastructure, affect the stress balance and hydrology of already critical slopes, potentially enhancing their sensitivity to changes. The mountainous Bailong River Corridor (BRC) in Northwest China is dominated by large slow-moving landslides, where any transport infrastructure expansion has to transit through. A complex interplay of human, precipitation, and seismic factors determines the triggering dynamics of these large movements. This study integrates displacement time series, precipitation, and road distribution to quantify the impact of road emplacement on the sensitivity of large slow-moving landslides to precipitation regionally using panel regression analysis. It is shown that road disturbance significantly amplifies the sensitivity of landslide displacements to precipitation, and paved roads on the large slow-moving landslides increase their sensitivity to precipitation by 40%. Roads (both paved and unpaved) also reduce the threshold of antecedent cumulative precipitation required to trigger significant displacement, shortening the typical period from 132 days to only 120 days. The enhanced response frequency increases large reactivated landslide risk, and impacts road operation and management. This better understanding of the precipitation signature in the dynamics of large slow-moving landslides transited by roads contributes to improving future road planning, enhancing landslide risk mitigation, and strengthening urban resilience in vulnerable alpine environments.