Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). 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 methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf.

本数据包与发表于《地球科学前沿（Frontiers in Earth Science）》的论文《流域尺度下河床沉积物粒径估算方法存在差异》（Regier等，2025）相关。溪流与河流中的沉积物粒径分布通常以中值粒径（median grain size, d50）进行量化，这是理解和预测溪流与河流水文及生物地球化学功能的关键指标。手动测量d50的方法耗时较长，且会遗漏较大粒径的颗粒；而基于模型的d50估算方法往往过度泛化流域特征，因此无法精准表征位点尺度的异质性。本研究采用一种结合机器学习的摄影测量方法——You Only Look Once（YOLO，单阶段目标检测算法），基于从亚基马河流域（Yakima River Basin, YRB）全域溪流与河流采集的图像，对粒径大于2 mm的颗粒开展d50估算。为明确此类方法能否弥合手动估算与基于流域特征模型的d50估算在分辨率与精度上的差距，我们将YOLO估算的d50值与亚基马河流域内的手动估算及模型估算结果进行了对比。研究发现不同方法得到的d50平均值与变异性存在显著差异，且d50估算结果与流域特征之间存在关联。原始图像可在https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052获取。本数据包最初于2023年5月发布，2025年8月更新至v2版本（新增并修改了部分文件），未通过修订文件名与文件夹名体现变更详情，更多信息请参阅自述文件（readme）中的变更历史章节。除自述文件外，本数据包还包含用于描述各文件的文件级元数据（file-level metadata, FLMD）文件，以及用于说明所有列/行表头与变量定义的数据字典（data dictionary, DD）。本数据集包含一个主数据文件夹，内含：(1) 文件级元数据；(2) 数据字典；(3) 自述文件；(4) 用于存放数据、图表与脚本的子文件夹。其中，数据文件夹包含用于论文分析的数据集，格式涵盖图像、文本分隔文件或地理空间参考文件；图表文件夹包含论文中不同格式的配图；脚本文件夹包含用于完成论文分析的全部代码。所有文件格式包括.csv、.rds、.dbf、.prj、.shp、.shx、.jpg、.png、.R、.Rproj或.pdf。