Evolution of Coseismic and Post‐seismic Landsliding After the 2015 M_w 7.8 Gorkha Earthquake, Nepal

Abstract

Coseismic landslides represent a major cascading hazard associated with high-magnitude earthquakes in mountainous environments (Fan, Scaringi, Domènech, et al., 2019; Fan, Scaringi, Korup, et al., 2019). The widespread landsliding observed in many recent large continental earthquakes has led to substantially higher death tolls when compared to earthquakes without landslides (Budimir et al., 2014), disruption to infrastructure (Aydin et al., 2018; Bird & Bommer, 2004), and the mobilization and transport of large volumes of sediment (M. Y. F. Huang & Montgomery, 2012; Wang et al., 2015). Increased interest in understanding the spatial distribution, impacts, and timing of coseismic landslides in recent decades has resulted in the production of a growing number of coseismic landslide inventories (Tanyas et al., 2017). In contrast, despite growing evidence for the persistence of enhanced landslide rates and the consequent long-term impacts of coseismic hillslope damage in the years to decades after a major earthquake (e.g., Dadson et al., 2004; Hovius et al., 2011; Marc et al., 2015; Parker et al., 2015), our current understanding of the post-seismic evolution of landslides is limited. As a result, we remain incapable of anticipating the spatio-temporal evolution of landslide hazard after a large earthquake, which frustrates our ability to inform response, recovery, and reconstruction (e.g., Robinson et al., 2017; Williams et al., 2018), and limits understanding of the long-term role of earthquakes in the overall mountain sediment cascade. A standard approach to tracking post-seismic landsliding is to develop multi-temporal landslide inventories, usually by mapping from airborne or satellite imagery. This is a time-consuming and potentially expensive Abstract Coseismic landslides are a major hazard associated with large earthquakes in mountainous regions. Despite growing evidence for their widespread impacts and persistence, current understanding of the evolution of landsliding over time after large earthquakes, the hazard that these landslides pose, and their role in the mountain sediment cascade remains limited. To address this, we present the first systematic multi-temporal landslide inventory to span the full rupture area of a large continental earthquake across the pre-, co-and post-seismic periods. We focus on the 3.5 years after the 2015 M w 7.8 Gorkha earthquake in Nepal and show that throughout this period both the number and area of mapped landslides have remained higher than on the day of the earthquake itself. We document systematic upslope and northward shifts in the density of landsliding through time. Areas where landslides have persisted tend to cluster in space, but those areas that have returned to pre-earthquake conditions are more dispersed. While both pre-and coseismic landslide locations tend to persist within mapped postearthquake inventories, a wider population of newly activated but spatially dispersed landslides has developed after the earthquake. This is particularly important for post-earth...

New insight into post-seismic landslide evolution processes in the tropics

“…This means that for the whole areas affected by these earthquakes these observations may not valid. However, compared to the similar works in the literature suggesting at least a few years for returning to the pre-seismic susceptibility levels (e.g., Fan et al, 2018;Kincey et al, 2021;Marc et al, 2015), our findings still point out a relatively short period.…”

“…In the geoscientific literature, the post-seismic landslide evolution is examined on the basis of the temporal variation of several parameters such as landslide rate (km 2 /year, in Barth et al, 2019), landslide density (m 2 /km 2 , in Marc et al, 2019), climate normalized landslide rate (Marc et al 2015), number of landslides (Saba et al 2010), total landslide area (Shafique 2020) and cumulative landslide area/volume (Fan et al 2018). The timespan of the post-seismic period required to restore a given area to pre-seismic landslide susceptibility levels is called landslide recovery time (e.g., Kincey et al, 2021;Marc et al, 2015). And, it is mostly identified using one of the parameters listed above.…”

“…1). These earthquakes correspond to: (1) 1993 Finisterre (Papua New Guinea, M w=6.9) (Marc et al 2015), (2) 1999 Chi-Chi (Taiwan, Mw=7.7) (Shou et al 2011a;Marc et al 2015), (3) 2004 Niigata (Japan, Mw=6.6) (Marc et al 2015), (4) 2005 Kashmir (India-Pakistan, Mw=7.6) (Saba et al 2010; Shafique 2020), (5) 2008 Iwate (Japan, Mw=6.9) (Marc et al 2015(Marc et al ), (6) 2008 Wenchuan (China, Mw=7.9) (e.g., Tang et al 2016;Zhang et al 2016;Yang et al 2017;Fan et al 2018;Chen et al 2020b), (7) 2012 Haida Gwaii (Canada, Mw=7.8) (Barth et al 2020) and 92015 Gorkha (Nepal, Mw=7.8) (Marc et al 2019;Kincey et al 2021). On the basis of the analyses executed on these events, there is a general agreement that earthquakes elevate the landslide susceptibility in post-seismic periods.…”

“…Wenchuan (e.g., Fan et al 2018;Chen et al 2020b) and Gorkha (e.g., Kincey et al, 2021;Marc et al, 2019) earthquakes. Notably, the inconsistency between different observations could be related to the boundaries of examined areas (e.g., Shafique, 2020;Yunus et al, 2020) because the ground shaking level spatially varies, hence the its effect varies as well.…”

New Insight into Post-seismic Landslide Evolution Processes in the Tropics

“…In the geoscientific literature, the post-seismic landslide evolution is examined on the basis of the temporal variation of several parameters such as landslide rate (km 2 /year, in Barth et al, 2020), landslide density (m 2 /km 2 , in Marc et al, 2019), climate normalized landslide rate (Marc et al, 2015), number of landslides (Saba et al, 2010), total landslide area (Shafique, 2020) and cumulative landslide area/volume (Fan et al, 2018). The timespan of the post-seismic period required to restore a given area to pre-seismic landslide susceptibility levels is called landslide recovery time (e.g., Marc et al, 2015;Kincey et al, 2021). And, it is mostly identified using one of the parameters listed above.…”