Influence of Specific Contributing Area algorithms on slope failure prediction in landslide modeling

Abstract

Abstract. This study anatomized algorithm effects of specific contributing area (SCA) on soil wetness estimation, consequently landslide prediction, in SHALSTAB. A subtropical mountainous catchment during three typhoon invasions is targeted. The peak 2-day rainfall intensity of the three typhoons: Haitang, Mindulle and Herb are 144, 248 and 327 mm/day, respectively. We use modified success rate (MSR) to retrieve the most satisfying mean condition for model parameters in SHALSTAB at three rainfall intensities and respective pre-typhoon NDVI themes. Simulation indicates that algorithm affects the prediction of landslide susceptibility (i.e. FS, Factor of Safety) significantly.Based on fixed NDVI and the mean condition, we simulate by using full scale rainfall intensity from 0 to 1200 mm/day. Simulations show that predicted unstable area coverage increases non-linearly as rainfall intensity increases for all algorithms yet with different increasing trends. Compared to Dinf, D8 always gives lower coverage of predicted unstable area during three typhoons. By contrast, FD8 gives higher coverage areas. The absolute difference (compared to Dinf) in predicted unstable area ranges from ∼−3% to +4% (percent watershed area). The relative difference (compared to Dinf) ranges from −15% to as high as +40%. The maximum absolute and relative differences in unstable area prediction occur around the condition of 100-300 mm/day, which is common in subtropical mountainous region.Theoretical relationship among slope, rainfall intensity, SCA and FS value was derived in which FS values are very sensitive to algorithms in the field of slope from 37 to 52de-gree. Results imply any comparison among SCA-related landslide models or engineering application of rainfall return period analysis must base on the same algorithm to obtain comparable results. This study clarifies the SCA algorithm effect on FS prediction and deepens our understanding on landslide modeling.

Terrain attributes of earthquake‐ and rainstorm‐induced landslides in orogenic mountain Belt, Taiwan

“…Earthquake‐induced landslides are primarily governed by the interaction of seismic waves and topography; thus landslides occur along ridgetops. In contrast, the rainstorm‐induced landslides are frequently found in the slope toes due to greater degree of soil water saturation (Montgomery and Dietrich, ; Huang et al , ). However, some studies in central Taiwan did not show such a clear pattern (Lin et al , ; Meunier et al , ).…”

Pre-processing algorithms and landslide modelling on remotely sensed DEMs

“…Mendicino, 1999) or detachment/sedimentation rate (e.g. Schoorl et al, 2000), predicting ephemeral gullies (Desmet and Govers, 1996), extracting channel network for runoff and pollution evaluation (Endreny and Wood, 2003) and evaluating performances of shallow landslide models (Huang et al, 2007). In contrast few works compare the influence of DEM artifact correction methods: Srivastava (2000) analyzed the performances of Garbrecht and Martz's vs. flooding approach, whereas Nardi et al (2008) used several combinations of flow direction schemes and DEM pre-processing algorithms to highlight their differences when computing some hydrologic attributes (drainage density, Horton's parameters, flow path length etc.)…”

A new region-based preparatory factor for landslide susceptibility models: the total flux

“…Landslides are among the most common and dangerous natural hazards in mountainous regions (Domakinis et al 2008;Huang et al 2007;Petley 2012). According to Petley (2012), between 2004 and 2010, 2620 fatal landslides were recorded globally, resulting in a total death toll of at least 32,322.…”