Large rainfall-induced landslides are among the most dangerous natural hazards in Taiwan, posing a risk for people and infrastructure. Thus, better knowledge about the evolution of landslides and their impact on the downstream area is of high importance for disaster mitigation. The aim of this study is twofold: (1) to semi-automatically map the evolution of the Butangbunasi landslide in south-central Taiwan using satellite remote sensing data, and (2) to investigate the potential correlation between changes in landslide area and heavy rainfall during typhoon events. Landslide area, as well as temporary landslide-dammed lakes, were semi-automatically identified using object-based image analysis (OBIA), based on 20 Landsat images from 1984 to 2018. Hourly rainfall data from the Taiwan Central Weather Bureau (CWB) was complemented with rainfall data from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to examine the potential relationship between landslide area changes and rainfall as a triggering factor. The OBIA mapping results revealed that the most significant landslide extension happened after typhoon Morakot in 2009. We found a moderate positive relationship between the landslide area change and the duration of the heavy rainfall event, whereas daily precipitation, cumulative rainfall and mean intensity did not present strong significant correlations.

Abstract. Controls on landsliding have long been studied, but the potential for landslide-induced dam and lake formation has received less attention. Here, we model possible landslides and the formation of landslide dams and lakes in the Austrian Alps. We combine a slope criterion with a probabilistic approach to determine landslide release areas and volumes. We then simulate the progression and deposition of the landslides with a fluid dynamic model. We characterize the resulting landslide deposits with commonly used metrics, investigate their relation to glacial land-forming and tectonic units, and discuss the roles of the drainage system and valley shape. We discover that modeled landslide dams and lakes cover a wide volume range. In line with real-world inventories, we further found that lake volume increases linearly with landslide volume in the case of efficient damming – when an exceptionally large lake is dammed by a relatively small landslide deposit. The distribution and size of potential landslide dams and lakes depends strongly on local topographic relief. For a given landslide volume, lake size depends on drainage area and valley geometry. The largest lakes form in glacial troughs, while the most efficient damming occurs where landslides block a gorge downstream of a wide valley, a situation preferentially encountered at the transition between two different tectonic units. Our results also contain inefficient damming events, a damming type that exhibits different scaling of landslide and lake metrics than efficient damming and is hardly reported in inventories. We assume that such events also occur in the real world and emphasize that their documentation is needed to better understand the effects of landsliding on the drainage system.

<p><strong>Abstract.</strong> In August 2009, Typhoon Morakot caused a record-breaking rainfall in Taiwan. The heavy rainfall triggered thousands of landslides, in particular in the central-southern part of the island. Large landslides can block rivers and can lead to the formation of landslide-dammed lakes. Cascading hazards like floods and debris flows after landslide dam breaches pose a high risk for people and infrastructure downstream. Thus, better knowledge about landslides that significantly impact the channel system and about the resulting landslide-dammed lakes are key elements for assessing the direct and indirect hazards caused by the moving mass. The main objectives of this study are 1) to develop an object-based image analysis (OBIA) approach for semi-automated detection of landslides that caused the formation of landslide-dammed lakes and 2) to monitor the evolution of landslide-dammed lakes based on Landsat imagery. For landslide and lake mapping, primarily spectral indices and contextual information were used. By integrating morphological and hydrological parameters derived from a digital elevation model (DEM) into the OBIA framework, we automatically identified landslide-dammed lakes, and the landslides that likely caused the formation of those lakes, due to the input of large amounts of debris into the channel system. The proposed approach can be adapted to other remote sensing platforms and can be used to monitor the evolution of landslide-dammed lakes and triggering landslides at regional scale after typhoon and heavy rainstorm events within an efficient time range after suitable remote sensing data has been provided.</p>

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