Simulating vegetation controls on hurricane‐induced shallow landslides with a distributed ecohydrological model. Issue 2 (27th February 2015) - Hwang, Taehee; Band, Lawrence E.; Hales, T. C.; Miniat, Chelcy F.; Vose, James M.; Bolstad, Paul V.; Miles, Brian; Price, Katie
Abstract: The spatial distribution of shallow landslides in steep forested mountains is strongly controlled by aboveground and belowground biomass, including the distribution of root cohesion. While remote sensing of aboveground canopy properties is relatively advanced, estimating the spatial distribution of root cohesion at the forest landscape scale remains challenging. We utilize canopy height information estimated using lidar (light detecting and ranging) technology as a tool to produce a spatially distributed root cohesion model for landslide hazard prediction. We characterize spatial patterns of total belowground biomass based on the empirically derived allometric relationship developed from soil pit measurements in the Coweeta Hydrologic Laboratory, North Carolina. The vertical distribution of roots and tensile strength were sampled at soil pits allowing us to directly relate canopy height to root cohesion and use this model within a distributed ecohydrological modeling framework, providing transient estimates of runoff, subsurface flow, soil moisture, and pore pressures. We tested our model in mountainous southern Appalachian catchments that experienced a number of landslides during the 2004 hurricane season. Slope stability estimates under the assumption of spatially uniform root cohesion significantly underpredicted both the total number of landslides and the number of "false positives, " unfailed areas of the landscape that were predicted to fail. When we incorporate spatially distributed root cohesion, the accuracy of the slope stability forecast improves dramatically. With the growing availability of lidar data that can be used to infer belowground information, these methods may provide a wider utility for improving landslide hazard prediction and forecasting. Key Points: Lidar remote sensing is effective in estimating spatial root cohesion patterns Belowground vegetation information improves slope stability prediction