Measuring the grain-size distributions of mass movement deposits

Mass movement deposit grain-size distributions (GSDs) record initiation, transport and deposition mechanisms, and contribute to the rate at which sediment is exported from hillslopes to channels. Defining the GSD of a mass movement deposit is a significant challenge because they are often difficult to access, are heterogeneous in planform and with depth, contain grain sizes from clay (<63 mu m) to boulders (>1 m), and require considerable time to calculate accurately. There are numerous methods used to measure mass movement GSDs, but no single method alone can measure the entire range of grain sizes. This paper compares five common methods for determining mass movement deposit GSDs to assess how their accuracy may affect their applicability to different research areas. We applied an automated wavelet analysis (pyDGS), Wolman pebble counts, survey tape counts, manual photo counts and sieving to three different mass movement deposits (two debris flows, one rockslide) in Tredegar, Wales and the Longmen Shan, China. We found that pyDGS and survey tape counts produced comparable GSDs to sieving over a single order of magnitude. PyDGS required calibration to achieve accurate results, limiting its use for many applications. In Tredegar, Wolman pebble counts over-estimated grain sizes in the lower 80% of the distribution relative to the other four methods used. We demonstrate that method choice can introduce significant uncertainties, particularly at the edges of the distributions, such that D-16 values differ by up to a factor of five. These methodological uncertainties limit GSD comparisons across studies, particularly where these are used to infer processes within deposits. To minimize these challenges, the methods chosen should be both carefully reported and consistent with the research question.

Automated summary

The grain-size distribution of mass movement deposits is important for studying initiation, transport, and deposition mechanisms. However, determining the distribution is challenging due to inaccessibility, heterogeneity, and time-consuming calculations. This study compares five common methods and finds that method choice can introduce significant uncertainties.