Debris flow initiates from the shallow landslide can develop by eroding surface materials. If numerous debris flows occur concurrently in a single catchment, each debris flow can join each other and increase its effect on the downstream area. Under such conditions, the topographic change affects the inundation of the debris flow itself and the following floods. The debris flow's initiation location may have a high effect on the damage distribution in the catchment. However, the predicting method of such points has not been established. In this study, we introduced statistical landslide prediction to generate such point data artificially. Cell-by-cell logistic regression model estimated the possibility to be the debris-flow initiation point from the terrain and precipitation data. This possibility distribution and pseud-random number sets generated artificial landslide location. On the other hand, we have also developed the 2D simulation based on an existing stony debris flow model. The code has been parallelized by introducing the OpenMP and MPI hybrid parallelization to enable application on supercomputers. This simulation only requires the location of debris flow sources (i.e., landslides) and topographical conditions (DEM), and several physical parameters. By using the numerical model, firstly, we have conducted the simulation employing actual location data. The results showed high reproducibility of the actual affected area. Artificial landslide data are also used instead of the actual initiation points To evaluate source locations' effect. Multiple simulation results revealed that the downstream area has low variability compared to the upstream area under the landslides. This result also indicated that accurate landslide location data is not necessary to predict the downstream area's damage.

The slope at Huafan University is recorded as a dip slope on the Dalun Mountain in the northern Western Foothills of Taiwan. The lithology of the dip slope is mainly composed of intercalation of sandstone and shale and the thickness of the sandstone varies from thin to massive interbedded with shale of Miocene age. By interpolating the thickness of colluvium as the cover material derived from the borehole data and analysing the contouring of the interpolation result in the surfer software, it is revealed that debris accumulates at the slope foot toward the southwest in the direction of movement. Because of the influences of tectonic activities, especially, according to previous investigations, two faults pass through the study area: the one (Fault A) extending perpenticular to the Nanshihkeng Fault plays an important role to change the orientation of the strata. For detailed analysis of the subsurface geological structure of this slope, this study focuses on the development of a 3D geological model by using a polynomial surface fitting equation which is aimed to compute the regressive orientation of bedding plane derived from the boreholes. In the first stage, the orientation of the dip slope is determined by calculating the regression plane passing through the elevations of the boundary between the sandstone and overlying shale. The results show that the regression plane dips southwestward with an inclination angle approximating 16°. In the second stage, several cross-sectional profiles are made to visualize and clarify the 3D geological model. The geological model will contribute significantly to the phenomenon of the slope failure and can be guidelines to minimize the disasters. Finally, numerical simulations could be performed in the future to define the best fit to the observed landslide behaviour by Finite Element Method (FEM).

Landslides were triggered in Mon State of Myanmar in August, 2019 during the monsoon season. Total 90 rainfall-triggered landslides are registered from Google Earth imageries and most of them are relatively shallow and small in dimension (500-15000 sq. m). Among these a massive one, hit Thae Phye Kone village, 30 buildings were damaged and 75 people were killed by the sliding debris. Weathered granites are major lithology of this area. A mountain extended from northwest to southeast provides orographic effect results significant variation in distribution of landslides. 77 out of 90 landslides located on the southwest side of the ridge due to the direction of monsoon wind is from the southwest to the northeast. The topographic features of 35 landslides with area >4000 sq.m were identified by using SRTM and Google eath images. The results shows that these rainfall-triggered are mainly located on the lower slope but approximately 20% of  are located on the higher slope, which is not common and sperated that with thick weathered regolith accumulated on gentle topography near the ridge, enhances the erosion. Landslides mobility is decreased (angle of reach increased) with increasing slope angle of the landslide source area reflects  on slope angle of the landslide source area correlating the friction angle of sliding debris positively. The angle of reach is controlled by other factors such as strength heterogeneity, local topography aside of the valley rather than slope gradient could obstacle the mobility.

The purpose of this study is to establish the basis for improving the skill score of early warning of landslides by identifying the characteristics of soil moisture variation based on the Noah Land Surface Model (LSM) as well as rainfall and for analyzing the soil moisture thresholds that cause the rainfall-induced landslides. Soil moisture is more directly related to slope stability than rainfall because it affects pore water pressure that causes landslides, whereas rainfall thresholds indirectly determine the likelihood of landslides through cumulative rainfall or rainfall intensity or rainfall duration. The commonly-used antecedent precipitation index (API) has a limit because it calculates the recession coefficient by reflecting only the daily average temperature to estimate the soil moisture by considering ground evaporation. Although an attempt to improve API has been made via correlation analysis with soil moisture observations, it does not reflect the fact that the maximum retentive capacity, i.e., porosity, is different for different soil texture. The Noah LSM estimates soil moisture content considering the evapotranspiration from the ground, based on various atmospheric and ground conditions, including temperature, relative humidity, vegetation cover, potential evaporation, soil texture, soil water flow, etc. Therefore, the utilization of Noah LSM has advantages of estimating the soil moisture variation for each soil texture type and for various environmental conditions and identifying integrated soil moisture and rainfall threshold information for each rainfall event, which are essentially required for the landslides early warning system. Furthermore, it is useful to recognize shallow landslides by considering the rainfall conditions, e.g., the recent 3-day cumulative rainfall, and the soil moisture's critical condition.

The 20 September 2018 Naga Landslide in Cebu Island represents a classic deep-seated, low-angle, high velocity translational slope failure in a suburban open-pit mining environment. With a total of 134 casualties, the Naga landslide is considered as the largest landslide in terms of volume and risk in the Philippines’ recorded history. Drone photo interpretation, field investigation, analysis of video footage, and back analyses gave light to the initiation, transport, depositional mechanisms and pre-failure slope stability. The Naga landslide has a peculiar low-angle slip surface, was spread over an area of 9.5 × 10⁵ m², a volume of 27 M m³ and a distal reach of 1.3 km. This complex landslide, moved predominantly as an extremely rapid translational block slide, with minor dry avalanches, and rockfalls. Slide blocks were detached along tensional fractures that formed and grew at least three weeks prior to failure. There was no apparent direct trigger (e.g., rainfall, earthquake) but preliminary back analyses, using limit equilibrium method, yielded unstable to marginally stable failure surfaces even under dry conditions. The 2018 massive failure was interpreted to be caused by progressive weakening of the cut slopes and exposure of potential failure surfaces from slope excavation. The Naga landslide event highlights the need to re-examine policies and scientific protocols in the country to avert a similar disaster in the future.

We present the occurrence tendency of rainfall prone to landslide hazards in the whole Japanese archipelago under climate change influence in the finest resolution of 1 by 1 km. In this study the proxy tool to identify the hazardous rainfall is the well-calibrated critical line method, which is currently being applied for official early warning practice by the Japanese government. The analysis output is based on the grid system in the very fine resolution of 1 by 1 km. For future climate, we analyzed the future projections under the scenarios of RCP2.6 and RCP8.5 from the famous 2-km and 5-km Nonhydrostatic Regional Climate Models, developed and published by Meteorological Research Institute of Japan Meteorological Agency (JMA). Using 15-year reanalyzed precipitation data published by JMA as the observation dataset, a bias-correction method is applied to adjust the extracted simulated precipitation at each 1-km grid. Then, with the critical line method the future changes of hazardous rainfall are successfully obtained in the nationwide and geographically regional scales. The seasonal change is also revealed. The results indicate a clear increasing trend of landslide risk in the period from July to September in the whole Japan, and mostly in the regions of Pacific Ocean side. Our analysis provides a new methodology to quantify valuable and high-resolution hazard map of future landslide risk.

Sufficient information on slope characteristics is key in detailed mapping of landslides. On-site investigations are mainly conducted to closely identify landslide features and gather relevant geomorphological data. Remote sensing is also used as a supplementary tool as it offers a different perspective in landslide investigations, allowing additional insights that are not commonly observed with conventional in situ methods. We used unmanned aerial vehicles (UAVs) to survey deep-seated landslide sites in the Philippines, and generated digital elevation models (DEMs) from the acquired aerial images. Different morphometric parameters were extracted from the DEMs producing nine derivatives. These derivatives were consolidated through principal component analysis into a composite raster image that emphasized morphologically-distinct surface features. Five morphometric parameters, namely, slope, aspect, multiple shaded relief, roughness, and surface relief ratio, were found to be the most descriptive of the surface morphology of two deep-seated landslide sites. In the village of Parasanon, Pinabacdao, Samar Island, most of the previously mapped landslide scarps and cracks were highlighted. In addition, potential new features such as the upper boundary of the landslide, extensions of cracks, and a linear feature at the right flank were identified. In Manghulyawon village in La Libertad, Negros Island, the extent of the head scarp was recognized, and depletion and accumulation zones were revealed. These features require further field verification, as would be the case with every other remotely-sourced data. In addition, it should be noted that analysis from this method is only limited to the ground surface without significant vegetative cover and aboveground structures. The study demonstrated how remote sensing through UAV-derived DEMs supplemented findings from conventional approaches, providing a better understanding of landslides.

Taiwan has risk of natural disaster due to young and complex geological condition, leading high vulnerability to landslide. Knowing more about the mechanism of the natural disaster is critical for mitigation, hence the monitoring implementation will be essential accordingly. The landslide monitoring methods would be divided into the underground, surface to the space monitoring one. Among remote sensing technologies, Synthetic Aperture Radar (SAR) is considered as a powerful tool in this recent decade, and SAR can monitor the land surface movement clearly without the distraction of the cloud or night effect. Therefore, the Interferometry Synthetic Aperture Radar (InSAR) method was proposed to accurately retrieve small displacement. Recently, some techniques of InSAR method have been further improved, such as Persistent Scatter (PS)-InSAR for long-term and robust monitoring. However, there is few rule or guideline especially for the landslide monitoring in practice. Thus, the parametric study was proposed to examine the appropriate ranges of each parameter during PS-InSAR processes, and Ali-Mt. was chosen as the verified case with in-field GNSS observations. First, SNAP software was suggested to be employed at the beginning and combined with STAMPS method for PS-InSAR. Then one significant parameter in SNAP was revealed as the degree of flat earth polynomial which depends on the site location, and in STAMPS step we found the capable range for: (1) filter_grid_size is 15-50; and (2) unwrap_time_win is 20-100 in landslide monitoring case. Further discussion will be proceeded with different case study in near future.