Abstract. We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3-D data. Our aim is to fully characterise geomorphological change typified by an event magnitude-frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than 7 orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval.The data reported have been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3-D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3-D change to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ∼ 9 × 10 3 surveys acquired at ∼ 1 h intervals over 10 months. The magnitude-frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m 3 ) rockfalls increases from 67 to 98 % of the total, and the number of individual rockfalls observed increases by over 3 orders of magnitude. High-frequency monitoring therefore holds considerable implications for magnitude-frequency derivatives, such as hazard return intervals and erosion rates. As such, while high-frequency monitoring has potential to describe short-term controls on geomorphological change and more realistic magnitude-frequency relationships, the assessment of longer-term erosion rates may be more suited to less-frequent data collection with lower accumulative errors.

Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.

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...

Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 M w 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

Rockfalls commonly exhibit power law volume‐frequency distributions, where fewer large events are observed relative to more numerous small events. Within most inventories, the smallest rockfalls are the most difficult to detect and so may not be adequately represented. A primary challenge occurs when neighboring events within a single monitoring interval are recorded as one, producing ambiguity in event location, timing, volume, and frequency. Identifying measurement intervals that minimize these uncertainties is therefore essential. To address this, we use an hourly data set comprising 8,987 3‐D point clouds of a cliff that experiences frequent rockfalls. Multiple rockfall inventories are derived from this data set using change detections for the same 10‐month period, but over different monitoring intervals. The power law describing the probability distribution of rockfall volumes is highly sensitive to monitoring interval. The exponent, β, is stable for intervals >12 hr but increases nonlinearly over progressively short timescales. This change is manifested as an increase in observed rockfall numbers, from 1.4 × 103 (30 day intervals) to 1.4 × 104 (1 hr intervals), and a threefold reduction in mean rockfall volume. When the monitoring interval exceeds 4 hr, the geometry of detected rockfalls becomes increasingly similar to that of blocks defined by rock mass structure. This behavior change reveals a time‐dependent component to rockfall occurrence, where smaller rockfalls (identifiable from more frequent monitoring) are more sensitive to progressive deformation of the rock mass. Acquiring complete inventories and attributing discrete controls over rockfall occurrence may therefore only be achievable with high‐frequency monitoring, dependent upon local lithology.

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;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-earthquake recovery plans that ...

Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. This study presents a new, rapid method for assessing the total distribution and relative magnitude of coseismic landsliding in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in GIS to assess which predisposing factors have influenced landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the relative magnitude (number density) of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high fidelity mapping of landslides following an earthquake is not necessary. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the model. This method is therefore suited to conditions in which imagery is affected by partial cloud cover, or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake, and may therefore inform emergency operations more effectively compared to current practice.

Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment, and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved, and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end-users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.