Influence of subsurface flow by Lidar DEMs and physical soil strength considering a simple hydrologic concept for shallow landslide instability mapping

Abstract

High resolution DEMs and physical soil strength, among other factors, have significant effects on the shallow landslide instability mapping. Subsurface flow in soil mantle normally is assumed by slope parallel flow based on resolution of DEMs and, measuring soil strength, which is affected by subsurface flow within the soil mantle, is difficult in both the field and the laboratory. Thus, the aims of this study are to investigate the effect of subsurface flow by high resolution Lidar DEMs and the effect of physical soil strength attributes on shallow landslide instability mapping, respectively. In this study, two DEMs (with 1 m and 5 m resolutions) were used and, physical soil strength was calibrated using a simple subsurface hydrological concept with LiDAR and field survey data in order to quantify the influence of soil strength on shallow landslide instability mapping. To this end, various field surveys were performed at Woomyeon Mountain, Seoul, Republic of Korea, where shallow landslides occurred in 2012. The physical shallow landslide stability (SHALSTAB) model were applied. The modified success rate (MSR) method were applied to assess the predicted results. In the first series of simulations, using the two DEMs and experimentally derived soil strength values, relatively low MSR values of 0.42–0.468 for the 1 m DEM and 0.42–0.47 for the 5 m DEM were recorded. In the second series of simulations, using soil strength calibrated using a simple theoretical approach, the MSR for the 1 m DEM was 0.78–0.823 and the MSR for the 5 m DEM was 0.723–0.80. These results indicated that soil strength had a more important role in shallow landslide instability mapping than assuming subsurface flow by topographic resolution. Therefore, it may be useful to apply field-collected soil strength data using hydrological concepts to improve the accuracy of predictive models based on high-resolution surface data.