Meta-analysis of a large bedload transport rate dataset

Abstract

The estimation of bedload transport, a type of coarse fluvial sediment motion, is challenging because of higher-order, complex, and time-variable environmental processes. Still, models for calculating bedload transport mostly rely on semi-empirical formulae derived from time-averaged laboratory flume or field measurements and involve cross-section averaging of hydro-environmental parameters. Additionally, many models assume infinite, constant availability of sediment, which is unrealistic in natural environments. While most published models yield excellent results on the dataset they were trained on, they may be significantly inaccurate when applied to other cases. Also, more recent formulae relying on considerable amounts of data still lead to high calculation uncertainty. While the importance of short-term dynamics, for example, driven by turbulence, for bedload transport is well known, this study investigates the relevance of time-variable processes at monthly to seasonal scales. Our analysis of a large dataset and remote sensing data shows evidence for seasonal variability in measurements of bedload transport rates and challenges the validity of Gaussian statistics for interpolating bedload transport models. Measured bedload transport rates were smaller when snowmelt was taking place and when no glacier was present in the catchment, and were best represented by extreme value distributions. By controlling for biases from measurement devices, geomorphic patterns, and the presence of an upstream dam, we show that steady, constant at-a-station hydraulics only explain a limited share of the variance in the measurements. Ultimately, a model for estimating bedload transport rates should consider the temporal variability of processes across all scales, from turbulence to climate.