This study describes the use of inventory-based landslide susceptibility index (LSI) models based on the selection of causative factors, functional relationships between factors and integration to a hazard-warning model. We first merged landslide inventory data of five typhoon-training events and obtained sets of data representing environmental conditions where landslides are likely to occur. These well-defined data sets are used to select five representative causative factors, i.e. the slope angle, rock strength, drainage, curvature and soil type. Four bivariate statistical model combinations were tested: the linear combination, geometric mean, and two other mixed combinations. As a result, the modulation effects between (i) rock strength and slope and (ii) drainage and curvature were intensified in mixed model 2 (MX2) through factor multiplication. The MX2 LSI was integrated with three multivariate landslide hazard-warning models and tested with four triggering factor rainfall parameter sets. Results lead to the conclusion that threshold models with terraininfluenced rainfall can better identify hazard-warning locations. Modulating factor combinations for the hazard warning can also mirror true environmental conditions, yielding more representative model results. This study improves the method for identifying LSI models for the application to rainfall-triggered landslide hazard models.

This study examines the impacts of storm-triggered landslides on downstream sediment and turbidity responses in the Gaoping River Basin, Taiwan using the Soil and Water Assessment Tool (SWAT). Attention is given to analyzing the increased and altered baseline of suspended sediment load and turbidity after the disturbances caused by the rainfall and landslides associated with Typhoon Morakot in 2009. SWAT parameters were calibrated by the observed hydrometric data from 1999 to 2003 using the log-scale root-mean-square error (log-RMSE) and Nash-Sutcliffe Model Efficiency. Both parameter sets were applied for the simulation of suspended sediment yield and turbidity with annual updated landslide inventories for the period 2004-2012. The landslide updating mirrors the physical land-cover changes and has slightly improved the model performance, yet landslides alone cannot explain the difference between Morakot-induced and SWAT-simulated sediment discharge. The set of parameters calibrated by log-RMSE can better approximate the increased baseline and typhoon induced alterations. The results show alterations in sediment erosion and transport: (1) drastically increased the turbidity baseline and occurrence of high-turbidity; (2) altered coefficient and exponent values of the sediment rating curve; and (3) altered relationship between rainfall and induced turbidity during major rainfall events. The research in this study provides an improved modeling approach to typhoon-induced alterations on river sediment loads and turbidity.

This study investigates community-based landslide mitigation planning. The combination of a landslide susceptibility index (LSI) model, landslide inventory datasets, and field work is used to identify hazard-prone areas in Maolin District, Taiwan. Furthermore, to identify the challenges and opportunities affecting the sustainable development of mountain communities, a pilot survey was conducted in three such communities (Dona Village, Wanshan Village, and Maolin Village). The results reveal that there are two types of significant mass movement in such areas: debris avalanche and debris flow. The results also show that the LSI map and multi-temporal landslide inventory datasets correlate with landslide locations. Meander is identified as an important factor in landslide activity. The questionnaire results show that the residents of the study area lack awareness of and access to information related to landslide activity. Similarly, the local residents favor increased environmental protection, working within their community, and additional government spending in regard to managing geohazards. To increase the resilience of the community, an improved landslide susceptibility map is proposed based on the output of the results. Thus, this research improves upon the process of identifying, supporting, and bettering the management of communities prone to landslides.