Abstract
Background: Landslides can cause substantial environmental, social and economic impacts. Under future climate scenarios the frequency of landslide-triggering events is likely to increase. Land managers, therefore, urgently require reliable high-resolution landslide susceptibility models to inform effective landslide risk assessment and management. Methods: In this study, gridded rainfall, topography, lithology and land cover surfaces were used to develop a high-resolution (10 m x 10 m) spatial model of landslides that occurred in Tasman, New Zealand during a period when ex-tropical Cyclone Gita brought heavy rain to the region. We separately modelled landslides in the same dataset as a function of the erosion susceptibility classification (ESC) data layer used to determine the level of control applied to forestry activities under the National Environmental Standards for Plantation Forestry (NES-PF). Models were fit using boosted regression trees. Results: Our preferred model had excellent predictive power (AUROC = 0.93) and included the parameters: aspect, elevation, mid-slope position, land cover, rainfall, slope, and a descriptive seven-class topographical index. Land cover, elevation, rainfall, slope and aspect were the strongest predictors of landslides with the land cover classes ‘seral native vegetation’ and clear-felled plantation forest’ predicting higher probabilities of landslides and tall native forest and closed canopy plantation forest predicting lower probabilities of landslides. The ESC was a poor predictor of landslides in the study area (AUROC = 0.65). Conclusions: Our study shows that accurate, high-resolution landslide probability surfaces can be developed from landslide distribution, land cover, topographical and rainfall data. We also show that landslide occurrence in the Tasman region could be substantially reduced by increasing the extent of permanent forest cover and by limiting clear-fell harvest of plantation forests on landslide-prone slopes. The ESC framework that underpins the NES-PF was a poor predictor of landslides and, therefore, an unreliable basis for regulating forestry activities in the Tasman, New Zealand.
Highlights
Landslides can cause substantial environmental, social and economic impacts (Dymond et al 2010; Fahey & Coker 1992; Gordon 2007; Kemp et al 2011; Krausse et al 2001; Ryan et al 2008; Thrush et al 2004)
Of the variables included in the model, land cover, elevation, rainfall, slope and aspect were most informative, with the land cover classes clear-felled plantation forest (CfPF) predicting the highest probability of landslides and tall native forest (TNF) predicting the lowest probability of landslides
Fitted functions for the topographical position index (TPI) and mid-slope position index (MPI) indicated that landslide occurrence was highest on mid-slope and upper-slope drainages, but these parameters explained substantially less deviance in the response and had low importance scores (Figure 3)
Summary
Landslides can cause substantial environmental, social and economic impacts (Dymond et al 2010; Fahey & Coker 1992; Gordon 2007; Kemp et al 2011; Krausse et al 2001; Ryan et al 2008; Thrush et al 2004). Topography, land cover, land use and climate surfaces paired with site specific data on previous landslide events, it is possible to accurately model landslide susceptibility and risk at the scale of land use activities (Basher et al 2015a). Such modelling can help land management agencies identify areas that are susceptible to landslides and to recognise and manage land use activities that increase or decrease landslide risk. Urgently require reliable highresolution landslide susceptibility models to inform effective landslide risk assessment and management
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