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

Dataset · Tue May 02 04:00:00 EDT 2023

DOI:https://doi.org/10.15485/1972232· OSTI ID:1972232

Regier, Peter ^[1]; Chen, Yunxiang ^[1]; Son, Kyongho ^[1]; Bao, Jie ^[1]; Forbes, Brieanne ^[1]; Goldman, Amy E. ^[1]; Kaufman, Matthew H. ^[1]; Rod, Kenton A. ^[1]; Stegen, James C. ^[1]

---

1. Pacific Northwest National Laboratory

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.

• Research Organization: River Corridor and Watershed Biogeochemistry SFA
• Sponsoring Organization: U.S. DOE > Office of Science > Biological and Environmental Research (BER)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 1972232
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Basin area
Biogeochemistry
Computer vision
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROCESSES > EVAPOTRANSPIRATION
EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID PRECIPITATION > SNOW
EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE/LAND COVER CLASSIFICATION
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > WATERSHED CHARACTERISTICS > WATERSHED LENGTH
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > WATERSHED CHARACTERISTICS > WATERSHED SLOPE
ESS-DIVE CSV File Formatting Guidelines Reporting Format
ESS-DIVE File Level Metadata Reporting Format
Elevation
Freshwater
Grain size distribution
Hydrology
Image suitability
Machine learning
Median grain size
Median grain size (d50)
Photogrammetry
Precipitation as snow
Respiration
River
Riverbed
Stream
Stream length
Stream order
Stream slope
Urban land cover