Controls on the grain size distribution of landslides in Taiwan: the influence of drop height, scar depth and bedrock strength

Abstract

Abstract. The size of grains delivered to rivers by hillslope processes is thought to be a key factor controlling sediment transport, long-term erosion and the information recorded in sedimentary archives. Recently, models have been developed to estimate the grain size distribution produced in soil, but these models may not apply to active orogens where high erosion rates on hillslopes are driven by landsliding. To date, relatively few studies have focused on landslide grain size distributions. Here, we present grain size distributions (GSDs) obtained by grid-by-number sampling on 17 recent landslide deposits in Taiwan, and we compare these GSDs to the geometrical and physical properties of the landslides, such as their width, area, rock type, drop height and estimated scar depth. All slides occurred in slightly metamorphosed sedimentary units, except two, which occurred in younger unmetamorphosed shales, with a rock strength that is expected to be 3–10 times weaker than their metamorphosed counterparts. For 11 landslides, we did not observe substantial spatial variations in the GSD over the deposit. However, four landslides displayed a strong grain size segregation on their deposit, with the overall GSD of the downslope toe sectors being 3–10 times coarser than apex sectors. In three cases, we could also measure the GSD inside incised sectors of the landslides deposits, which presented percentiles that were 3–10 times finer than the surface of the deposit. Both observations could be due to either kinetic sieving or deposit reworking after the landslide failure, but we cannot explain why only some deposits had strong segregation. Averaging this spatial variability, we found the median grain size of the deposits to be strongly negatively correlated with drop height, scar width and depth. However, previous work suggests that regolith particles and bedrock blocks should coarsen with increasing depth, which is the inverse of our observations. Accounting for a model of regolith coarsening with depth, we found that the ratio of the estimated original bedrock block size to the deposit median grain size (D50) of the deposit was proportional to the potential energy of the landslide normalized to its bedrock strength. Thus, the studied landslides agree well with a published, simple fragmentation model, even if that model was calibrated on rock avalanches with larger volume and stronger bedrock than those featured in our dataset. Therefore, this scaling may serve for future modeling of grain size transfer from hillslopes to rivers, with the aim to better understanding landslide sediment evacuation and coupling to river erosional dynamics.

The Upper Limit of Denudation Rate Measurement From Cosmogenic 10Be(Meteoric)/9Be Ratios in Taiwan

“…Hence, the contribution of landslide debris to all the mainstem locations is mainly sourced from the same unsampled upper reaches, that is, upstream of LW3. Furthermore, based on the average area of individual landslide scars in the upstream of LW3 (Kuo & Brierley, 2013) and an empirical landslide depth-area relationship (Larsen et al, 2010), we roughly estimate an average landslide depth of ∼4.8 m. This depth value estimated from mean landslide area may be overestimated, but it indeed falls within the range of scar depths estimated from other landslides in Taiwan (Marc et al, 2021) and is much deeper than the soil depth in the Liwu Basin.…”

Measuring the grain‐size distributions of mass movement deposits

“…Whilst Locat et al (2006) obtained this conclusion using photographs of grains, they did note that their proportions of fines were likely to be an under‐estimate. Hence, whilst broad patterns can be well captured using more accessible, common methods (Marc et al, 2021), it is important to capture full GSDs for deposits, using multiple methods, when identifying depositional and transport processes.…”

“…The methodological uncertainties associated with comparing GSDs and percentiles obtained using different methods can have consequences for accurate process interpretation. For example, the factor of two difference in grain-size percentile estimates from survey tape counts relative to sieving for a fine deposit could shift the D 50 value from suspended load to bedload, which would have implications for estimates of sediment export rates and onward transport (Croissant et al, 2021;Marc et al, 2021). Similarly, by excluding up to 20% by weight of the finest grains, all non-sieving methods are unable to find evidence for processes where the proportion of sand and silt is influential (de Haas et al, 2015;Kaitna et al, 2016;Makris et al, 2020).…”

“…Mass movement deposit GSDs are typically heterogeneous, can extend up to eight orders of magnitude (from <1 μm to >10 m), and vary spatially and with depth. These GSD characteristics reflect source properties, such as lithology or fracture spacing (Attal & Lavé, 2006; Marc et al, 2021) and processes occurring during transit, including winnowing (Crosta et al, 2007; Dufresne & Dunning, 2017; Locat et al, 2006). There remains no single method that can record GSDs over the range and scale of most mass movement deposits (Table 1).…”

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

“…The initial size or shape distributions are controlled by fragmentation, weathering processes and structure of the rock mass (e.g., fracture density and orientation, mineral size) (e.g., Molnar et al, 2007;Garzanti et al, 2008;Sklar et al, 2017;DiBiase et al, 2018;Neely and DiBiase, 2020;Verdian et al, 2021). These initial distributions then evolve due to the action of geomorphological processes, including attrition, chipping, abrasion, fragmentation, chemical weathering and transport of grains by wind, river flow, avalanches along hillslopes, and sea waves and currents (e.g., Attal and Lavé, 2006;Domokos et al, 2014;Miller et al, 2014;Várkonyi et al, 2016;Novák-Szabó et al, 2018;Marc et al, 2021). Grains are also found at the surface of other planetary bodies or asteroids (Burke et al, 2021) and offer unique constraints on their surface conditions.…”