Different Methods of Estimating Riverbed Sediment Grain Size Diverge at the Basin Scale

Peter Regier^1*Yunxiang Chen²Kyongho Son²Jie Bao²Brieanne Forbes²Amy E Goldman²Matt Kaufman^2,3Kenton Rod²James C. Stegen^2,4

• ¹Marine and Coastal Research Laboratory, Pacific Northwest National Laboratory (DOE), Sequim, Washington, United States
• ²Pacific Northwest National Laboratory (DOE), Richland, United States
• ³Department of Earth, Environment, and Physics, Worcester State University, Worcester, MA, United States
• ⁴School of the Environment, Washington State University, Pullman, WA, United States

The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (D50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual D50 measurements are time-consuming and ignore larger grains, while approaches to model D50 based on catchment characteristics may over-generalize and miss site-scale heterogeneity. Machine learning-enabled object detection methods like You Only Look Once (YOLO) provides an alternative that enables estimation of D50 that is faster than manual measurements and more site-specific than predictions based on catchment characteristics. To understand the potential role of object detection methods for improving understanding of D50, we compared D50 estimates made manually, predicted from catchment characteristics, and using a YOLO-enabled approach across the Yakima River Basin. We found distinct differences between methods for D50 averages and variability, and relationships between D50 estimates and basin characteristics. We discuss the advantages and limitations of object detection methods versus current methods, and explore potential future directions to combine D50 methods to better estimate spatiotemporal variation of D50, and improve incorporation into basin-scale models.