Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM₁₀ concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM₁₀ concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM₁₀ concentration, but daily maximum PM₁₀ concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM₁₀ concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.

In recent years, extreme rainfall events occur frequently, causing serious watershed sediment disasters, destroying mountain roads, and endangering the safety of residents' lives and property. This study aims to deal with the spatial change of potential sediment movement on the road slope pre-disaster and to screen disaster hot spots for early warning and control system. The conceptual model is used to simulate the distribution of primary and/or derived disasters on a watershed scale to assess the impact of sediment disasters caused by heavy rain event. Correlation analysis shows that the models in assessment of primary disaster and derived disaster are significantly correlated with the collapse ratio and disaster ratio, respectively. Since the primary disaster has been considered when calculating the derived disaster risk, the terrain subdivision along Provincial Highway 21 (Tai-21) is extracted to understand the derived sediment disaster on the road slope. The model can effectively evaluate the road sections prone to disasters. According to the risk level, the hot spot of road slope disasters and the management of disaster resilience are determined and can be the reference for disaster prevention and control.

Several related pre-event environment indices coupled with a 200-year event of Typhoon Morakot were used in this study to analyse landslide risk/scale for mapping and verifying the danger grade of watershed landslides. Risk analysis was modelled using maximum daily rainfall of the tested event as the hazard factor, and a complex indicator derived from indices of return period, road development and green deterioration as the vulnerability factor. The vulnerability factor was then modified using the slope to adjust the weight for excluding the flat areas and increasing impact of steep areas. A comprehensive indicator integrated from indices of river concave, headward erosion and dip slope was provided as the hotspot of potential large-scale landslides which were modified by vegetation index to correct sediment yield due to antecedent effects. Finally, sites with high potential large-scale landslide could be interpreted by the danger grade calculated from collapse risk/scale analysis. The results show that the higher the danger grade, the greater the collapse ratio; and the coefficient of determination for the relationship is greater than 0.9. The model developed in this study for accurately estimating the risk/scale of rainfall-induced landslide can be fulfilled.