
handle: 11577/2842500 , 11570/3000773 , 20.500.11850/97490 , 2158/1084302
Abstract. This work evaluates the predictive power of the quasi-dynamic shallow landslide model QD-SLaM to simulate shallow landslide locations in a small-scale Mediterranean landscape, namely, the lower portion (2.6 km2) of the Giampilieri catchment, located in Sicily (Italy). The catchment was impacted by a sequence of high-intensity storms over the years 2007–2009, resulting in widespread landsliding, with a total landslide initiation area amounting to 2.6% of the basin area. The effect of high-resolution digital terrain models (DTMs) on the quality of model predictions is tested by considering four DTM resolutions: 2, 4, 10 and 20 m. Moreover, the impact of the dense forest road network on the model performance is evaluated by separately considering road-related landslides and natural landslides. The landslide model does not incorporate the description of road-related failures and is applied without calibration of the model parameters. The model predictive power is shown to be DTM-resolution dependent. Use of coarser resolution has a smoothing effect on terrain attributes, with local slope angles decreasing and contributing areas becoming larger. The percentage of watershed area represented by the model as unconditionally unstable (i.e. failing even without the addition of water from precipitation) ranges between 6.3% at 20 m DTM and 13.8% at 2 m DTM, showing an overestimation of the mapped landslide area. We consider this prediction as an indication for likely failing sites in future storms rather than areas proved stable during previous storms. When assessed over the sample of mapped non-road-related landslides, better model performances are reported for 4 and 10 m DTM resolution, thus highlighting the fact that higher DTM resolution does not necessarily mean better model performances. Model performances over road-related failures are lower than for the natural cases, and slightly increase with decreasing DTM resolution. These findings indicate that to realize the full potential of high-resolution topography, more extensive work is needed aiming more specifically to identify the extent of the artificial structures and their impact on shallow landsliding processes.
Technology, T, Environmental technology. Sanitary engineering, G, Environmental sciences, LiDAR; landslides; Forest roads; DTM; rainfall, Geography. Anthropology. Recreation, GE1-350, Shallow landslides, roads, grid resolution, TD1-1066
Technology, T, Environmental technology. Sanitary engineering, G, Environmental sciences, LiDAR; landslides; Forest roads; DTM; rainfall, Geography. Anthropology. Recreation, GE1-350, Shallow landslides, roads, grid resolution, TD1-1066
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 54 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
