Alluvial rivers are the most important agents of sediment transport in continental basins, whose fluvial deposits enclose information related to the time when rivers were active. In order to extract the most information from fluvial deposits in the sedimentary record, it is imperative to quantify the natural variability of channel patterns at the global scale, explore what controls may influence their development, and investigate whether channel pattern information is preserved in the alluvial plains in order to develop tools for recognizing them in the sedimentary record. By surveying 361 reaches of modern alluvial rivers with available water discharge data at a global scale, we present a quantitative channel pattern classification based on sinuosity and channel count index applicable to the recognition in the rock record. A continuum of channel patterns ranging from high-sinuosity single channel to lowsinuosity multichannels is documented, along with the proportion of depositional elements in their alluvial plains and their conditions of occurrence. Preserved barforms in the alluvial plains of these rivers are used to infer and quantify paleoflow directions at the channel-belt scale and result in ranges of paleocurrent circular variance that may lead to channel pattern identification in the rock record. Data from this work indicate that the recognition of channel patterns may be used to predict paleogeographic features such as the scale of drainage basin area and discharge, slope, and annual discharge regimes.

Abstract. Data on grain sizes of pebbles in gravel-bed rivers are of key importance for the understanding of river systems. To gather these data efficiently, low-cost UAV (uncrewed aerial vehicle) platforms have been used to collect images along rivers. Several methods to extract pebble size data from such UAV imagery have been proposed. Yet, despite the availability of information on the precision and accuracy of UAV surveys as well as knowledge of errors from image-based grain size measurements, open questions on how uncertainties influence the resulting grain size distributions still persist. Here we present the results of three close-range UAV surveys conducted along Swiss gravel-bed rivers with a consumer-grade UAV. We measure grain sizes on these images by segmenting grains, and we assess the dependency of the results and their uncertainties on the photogrammetric models. We employ a combined bootstrapping and Monte Carlo (MC) modeling approach to model percentile uncertainties while including uncertainty quantities from the photogrammetric model. Our results show that uncertainty in the grain size dataset is controlled by counting statistics, the selected processed image format, and the way the images are segmented. Therefore, our results highlight that grain size data are more precise and accurate, and largely independent of the quality of the photogrammetric model, if the data are extracted from single, undistorted nadir images in opposition to orthophoto mosaics. In addition, they reveal that environmental conditions (e.g., exposure to light), which control the quality of the photogrammetric model, also influence the detection of grains during image segmentation, which can lead to a higher uncertainty in the grain size dataset. Generally, these results indicate that even relatively imprecise and inaccurate UAV imagery can yield acceptable grain size data, under the conditions that the photogrammetric alignment was successful and that suitable image formats were selected (preferentially single, undistorted nadir images).

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