Abstract
AbstractRockfalls 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.
Highlights
Geomorphic processes that erode landscapes involve a broad range of event sizes, commonly characterized by fitting magnitude‐frequency curves to event inventories
The volume‐frequency power law coefficient β decreased in an asymptotic manner from β0 = 2.382 (Tint = 1 hr) tending toward β = 1.995 (Tint = 30 days), indicating that small rockfalls represent a greater proportion of the total detached material (Figures 3a and 3b; see supporting information Text S3)
We have identified a high degree of sensitivity of rockfall volume‐frequency scaling to the time between monitoring surveys
Summary
Geomorphic processes that erode landscapes involve a broad range of event sizes, commonly characterized by fitting magnitude‐frequency curves to event inventories. A consequence is that events that would otherwise be observed as larger individual rockfalls may be the sum of multiple smaller components (Kromer et al, 2017; Royán et al, 2015; Stock et al, 2012). This is important because the smallest events are often the most numerous, which holds implications for the assessment of both the largest credible and most probable event based upon previous observations (Corominas et al, 2018). Censoring of the smallest events must increase with monitoring interval (Tint) and, importantly, when rockfalls may be related in both space and time (Rosser et al, 2007), exactly how remains unknown
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