Improved landslide susceptibility prediction for sustainable forest management in an altered climate

Landslide occurrences, which result in significant casualties, economic losses, and ecological impacts, have been increasing worldwide over the last few decades. Thus, it is crucial for future landslide susceptibility to be considered when making long-term plans for timber extraction. Two factors that are known to reduce soil strength and increase landslide susceptibility are clear cutting (due to reduced root contributions to soil strength) and degree of soil saturation. Therefore, as projected climate change is expected to result in storms with higher intensity precipitation in many mountainous regions, these areas are likely to become more susceptible to landslide activity resulting in potentially severe consequences to aquatic habitat due to increased sediment loads. There is a need to investigate potential management plans that simultaneously protect the economic viability of the forest industry and the ecosystem services of the forest. The primary objectives of this study are to explore the impact of timber harvesting on landslide susceptibility under climate change and to create high resolution (10 m) landslide susceptibility maps to inform land management decisions in an altered climate. The Distributed Hydrology Soil Vegetation Model (DHSVM), a physically-based hydrology model that has been improved to incorporate mass wasting and erosion processes, was used to assess the sensitivity of landslide susceptibility to timber extraction. To investigate the impacts of climate change on landslide susceptibility we applied downscaled output from two General Circulation Models (GCMs) with two greenhouse gas (GHG) emission scenarios, A1B and B1, for the year 2045. The areal extent classified with a high landslide susceptibility increased on average by 7.1% and 10.7% for the B1 and A1B GHG emissions scenarios, respectively. The landslide susceptibility maps produced in this study can enable forest managers to plan for climate change by identifying areas that are more prone to landslide activity under altered climate conditions. The methodologies developed herein can be used by forest managers around the world to better assess landslide potential.