Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County, China

A catastrophic landslide occurred at Xinmo village in Maoxian County, Sichuan Province,China, on June 24, 2017. A 2.87×106 m3 rock mass collapsed and entrained the surface soil layer along the landslide path. Eighty-three people were killed or went missing and more than 103 houses were destroyed. In this paper, the geological conditions of the landslide are analyzed via field investigation and high-resolution imagery. The dynamic process and runout characteristics of the landslide are numerically analyzed using a depth-integrated continuum method and Mac Cormack-TVD finite difference algorithm.Computational results show that the evaluated area of the danger zone matchs well with the results of field investigation. It is worth noting that soil sprayed by the high-speed blast needs to be taken into account for such kind of large high-locality landslide. The maximum velocity is about 55 m/s, which is consistent with most cases. In addition, the potential danger zone of an unstable block is evaluated. The potential risk area evaluated by the efficient depthintegrated continuum method could play a significant role in disaster prevention and secondary hazard avoidance during rescue operations.

Landslide susceptibility assessment at Kathmandu Kyirong Highway Corridor in pre-quake, co-seismic and post-quake situations

Kathmandu Kyirong Highway(KKH)is one of the most strategic Sino-Nepal highways.Lowcost mitigation measures are common in Nepalese highways,however,they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road,and thus less resources are made available for its maintenance.It is subjected to frequent landslide events in an annual basis,especially during monsoon season.The Gorkha earthquake,2015 further mobilized substantial hillslope materials and damaged the road in several locations.The aim of this research is to access the dynamic landslide susceptibility considering pre,co and post seismic mass failures.We mapped 5,349 multi-temporal landslides of 15 years(2004-2018),using high resolution satellite images and field data,and grouped them in aforementioned three time periods.Landslide susceptibility was assessed with the application of’certainty factor’(CF).Seventy percent landslides were used for susceptibility modelling and 30%for validation.The obtained results were evaluated by plotting’receiver operative characteristic’(ROC)curves.The CF performed well with the’area under curve’(AUC)0.820,0.875 and 0.817 for the success rates,and 0.809,0.890 and 0.760 for the prediction rates for respective pre,co and post seismic landslide susceptibility.The accuracy for seismic landslide susceptibility was better than pre and post-quake ones.It might be because of the differences on completeness of the landslide inventory,which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations.The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents.This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk,which will further help to design the efficient mitigation measures along the mountain highways.

Mathematical model research on landslide monitoring through GPS

Assuming that road slope and landslide object are rigid, the landslide’s moving displacement was drawn based on their geometry shapes and the physi-mechanical features of materials, and the dynamic model of landslide was also set up, then DDOD(double difference observation data) was combined with the deformed monitoring point and the carrier phase observation data on base point, which can be used to monitor the landslide’s deformation rule from horizontal, vertical and directional view simultaneously. Observing equation was set up, which sufficiently reflects the activities of landslide in entire directions. Filter model includes some information such as mechanical state and GPS observing data by Kalman filter.

Dynamic Regularity and Prognoses of Landslide

Dynamic regularity is discussed tightly combining with method and principle of displacement monitoring for landslide . By the principle of dynamic energy analysis is performed emphatically for the broken - line condi on of sliding surface being always made of multiple combination of unit geostructural planes with different dip angles .Several formulae are derived from given conditions and , presented to describe the dynamic regularity . Based on the regularity an example of huge landslide is cited to calculate water urge height of reservoir . By Poisson cycle principle the latter was made for another large slide . The results showed themselves to have very approached vis-a-vis the actual ones .

From spatio-temporal landslide susceptibility to landslide risk forecast

The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored.This statement holds true,particularly in the context of landslide risk,where few scientific contributions investigate risk dynamics in space and time.This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years(from 2013 to 2021).For the analyses,the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit,resulting in a total of 236,997 units.This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature(e.g.,variable interaction plots).However,the main innovative effort is in the subsequent phase of the protocol we propose,as we used climate projections of the main trigger(rainfall)to obtain future estimates of yearly susceptibility patterns.These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model,assuming vulnerability=1.Overall,this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.