A Sequentially Coupled Catchment‐Scale Numerical Model for Snowmelt‐Induced Soil Slope Instabilities. Issue 5 (6th May 2020)

• Record Type: Journal Article
• Authors: Siva Subramanian, S.; Fan, X.; Yunus, A. P.; van Asch, T.; Scaringi, G.; Xu, Q.; Dai, L.; Ishikawa, T.; Huang, R.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-06
• Subjects: Snowmelt‐induced landslide -- seasonally cold region -- shallow slope failure -- catchment‐scale landslide hazard -- slope stability modeling -- GIS; Geomorphology -- Periodicals; 551.3
• DOI: 10.1029/2019JF005468
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: The frequency of snowmelt‐induced soil slope instabilities is increasing in some seasonally cold regions because of climate change. Reliable hazard assessment and risk mitigation of snowmelt‐induced landslides require physically‐based prediction models. However, existing models either apply only at the slope scale or assume precipitation as the sole landslide trigger. In doing so, they neglect the complexity and coupled nature of the thermo‐hydro‐mechanical processes leading to slope instability in seasonally cold regions (such as snow accumulation and melting, infiltration and surface runoff, soil saturation, pore water pressure buildup and dissipation). Here, we present a spatially distributed and sequentially coupled numerical model to simulate snowmelt‐induced slope instabilities at the catchment scale. The model accounts for temperature‐dependent changes in the soil hydraulic behavior related to changes in water state by means of a routine implemented in a geographic information system. We verified the performance of the model using a case study of spring snowmelt‐induced soil slope failures that occurred after the 2004 Mid‐Niigata earthquake in Japan. Considering limitations and simplifications, the model was able to predict the triggering condition, magnitude, and spatial distribution of the snowmelt‐induced landslides with a satisfactory degree of accuracy. We believe that the robustness and simplicity of our numerical approach make it suitable for implementation in early warning systems. Plain Language Summary: Climate change is responsible for an increasing number of natural disasters in seasonally cold regions, where heavier snowfalls and sudden snowmelts can cause destructive landslides on hill slopes. These landslides are not easy to study because the mechanisms that trigger them are different from those of shallow landslides occurring in response to rainfall. Moreover, advanced models that incorporate the complex relations among rain, snow, temperature, soil freezing, melting, and slope stability at a detailed scale are still cumbersome and very demanding in terms of computational power. For these reasons, evaluating the risk to people and assets in seasonally cold regions is not straightforward, and existing models tend to underestimate it. In this paper, we propose a novel modeling strategy to study the stability of soil slopes under the influence of both snowmelt and rainfall at a regional scale. First, we explain the governing mechanisms of snowmelt‐induced landslides; then, we present our strategy along with detailed explanations of the equations we use. We test our model by predicting a known case study (the snowmelt‐induced landslides of 2005 in Niigata, Japan) and obtain a reasonably good result. This simple strategy could be applied in an early warning system for snowmelt‐induced landslides. Key Points: Developed a novel spatially distributed numerical model for snowmelt‐induced shallow landslides Simplified account of below‐freezing temperature on hydro‐mechanical‐behavior for catchment‐scale slope stability assessment Model performance evaluated on snowmelt‐induced post‐seismic landslides occurred in Niigata, Japan, in 2005