Abstract
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.
Highlights
The occurrence of precipitation-triggered shallow landslides is of concern in hydro-geomorphic and natural hazards science due to the high ranking of such events among natural disasters in terms of both the number of people affected globally and the proportion of fatalities on the affected population
To investigate how different digital terrain models (DTMs) resolutions (2, 4, 10 and 20 m) effect the relative shallow landslide susceptibility distribution, the behaviour of three topographic indices derived from the DTM and used in the susceptibility assessment, that is local slope, effective contributing area and quasi-dynamic wetness index (QDWI), was www.hydrol-earth-syst-sci.net/18/2127/2014/
Examination of the results shows that even though the catchment percentages falling into the two main critical rainfall frequencies are roughly similar with coarsening DTM resolution (Table 4), the corresponding spatial organizations of the critical rainfall frequency with respect to the mapped scars are markedly different
Summary
The occurrence of precipitation-triggered shallow landslides is of concern in hydro-geomorphic and natural hazards science due to the high ranking of such events among natural disasters in terms of both the number of people affected globally and the proportion of fatalities on the affected population. Shallow landsliding can evolve into debris flows, resulting in high risk where vulnerable targets are involved (Petley, 2012). Landslide susceptibility maps represent one of the key elements for landslide risk management. Baeza and Corominas, 2001; Lee et al, 2003; Fell et al, 2008), or spatially distributed process-based models of slope stability and hydrology A number of recent process-based susceptibility models combine steady-state or quasi-steady-state hydrology concepts with the infinite-slope stability model to calculate the critical rainfall – i.e. the rainfall required to initiate a landslide One of the most important factors controlling the landslide location is surface topography through slope and concentration of shallow subsurface flow (Borga et al, 2002; Penna et al, 2011). Other relevant factors are rainfall intensity and soil and vegetation properties
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