Articles | Volume 7, issue 3
https://doi.org/10.5194/esurf-7-789-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-7-789-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2019
Research article |
| 02 Sep 2019
| 02 Sep 2019
Percentile-based grain size distribution analysis tools (GSDtools) – estimating confidence limits and hypothesis tests for comparing two samples
Brett C. Eaton
CORRESPONDING AUTHOR
Department of Geography, The University of British Columbia, 1984 West Mall, Vancouver, BC, Canada
R. Dan Moore
Department of Geography, The University of British Columbia, 1984 West Mall, Vancouver, BC, Canada
Lucy G. MacKenzie
Department of Geography, The University of British Columbia, 1984 West Mall, Vancouver, BC, Canada
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Cited
20 citations as recorded by crossref.
- Uncertainty analysis of sediment size estimations in gravel-bed reaches in Southern Brazil F. Zambrano et al. https://doi.org/10.1016/j.jsames.2025.105544
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. https://doi.org/10.1002/esp.5755
- La mesure granulométrique par imagerie en contexte torrentiel : apports et perspectives des nouvelles méthodes par intelligence artificielle J. Berthet et al. https://doi.org/10.4000/15pwo
- Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. https://doi.org/10.1002/esp.5337
- Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. https://doi.org/10.1016/j.sedgeo.2023.106340
- The variability of grain size metrics in gravel-bed rivers D. Vázquez-Tarrío & A. Recking https://doi.org/10.1016/j.catena.2025.108974
- OrthoSAM: multi-scale extension of the Segment Anything Model for river pebble delineation from large orthophotos V. Chan et al. https://doi.org/10.5194/esurf-14-391-2026
- Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen https://doi.org/10.1029/2021JF006260
- ImageGrains 2.0: Improved precision and generalization for grain segmentation D. Mair et al. https://doi.org/10.5194/esurf-14-527-2026
- A Field Imagery and Measurement Dataset for Grain-size Analysis of Riverbed Gravels in the Mid-Geum River H. Yoon et al. https://doi.org/10.22761/GD.2025.0042
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. https://doi.org/10.5194/esurf-10-953-2022
- Terrain‐derived measures for basin conservation and restoration planning J. Matt et al. https://doi.org/10.1002/rra.4181
- Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. https://doi.org/10.5194/esurf-10-1211-2022
- Downstream fining of fluvial gravels along the eastern Tibetan Plateau rivers Z. Ma et al. https://doi.org/10.1002/esp.70016
- Roughness Calibration to Improve Flow Predictions in Coarse‐Bed Streams R. Ferguson https://doi.org/10.1029/2021WR029979
- An Integrated Framework for the Assessment of Meso‐Scale Physical Habitats in Gravel‐Bed Rivers Using Remote Sensing and 2D Hydraulic Modeling D. Farò et al. https://doi.org/10.1002/wat2.70027
- How many microplastics do you need to (sub)sample? W. Cowger et al. https://doi.org/10.1016/j.ecoenv.2024.116243
- Localised geomorphic response to channel-spanning leaky wooden dams J. Wolstenholme et al. https://doi.org/10.5194/esurf-13-647-2025
- How many tagged stones are needed to estimate the mean travel distance of sediment from tracer experiments in gravel‐bed rivers? D. Vázquez‐Tarrío et al. https://doi.org/10.1002/esp.70352
- Surface grain-size mapping of braided channels from SfM photogrammetry L. Ribet et al. https://doi.org/10.5194/esurf-13-607-2025
20 citations as recorded by crossref.
- Uncertainty analysis of sediment size estimations in gravel-bed reaches in Southern Brazil F. Zambrano et al. https://doi.org/10.1016/j.jsames.2025.105544
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. https://doi.org/10.1002/esp.5755
- La mesure granulométrique par imagerie en contexte torrentiel : apports et perspectives des nouvelles méthodes par intelligence artificielle J. Berthet et al. https://doi.org/10.4000/15pwo
- Measuring the grain‐size distributions of mass movement deposits E. Harvey et al. https://doi.org/10.1002/esp.5337
- Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. https://doi.org/10.1016/j.sedgeo.2023.106340
- The variability of grain size metrics in gravel-bed rivers D. Vázquez-Tarrío & A. Recking https://doi.org/10.1016/j.catena.2025.108974
- OrthoSAM: multi-scale extension of the Segment Anything Model for river pebble delineation from large orthophotos V. Chan et al. https://doi.org/10.5194/esurf-14-391-2026
- Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes B. Purinton & B. Bookhagen https://doi.org/10.1029/2021JF006260
- ImageGrains 2.0: Improved precision and generalization for grain segmentation D. Mair et al. https://doi.org/10.5194/esurf-14-527-2026
- A Field Imagery and Measurement Dataset for Grain-size Analysis of Riverbed Gravels in the Mid-Geum River H. Yoon et al. https://doi.org/10.22761/GD.2025.0042
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. https://doi.org/10.5194/esurf-10-953-2022
- Terrain‐derived measures for basin conservation and restoration planning J. Matt et al. https://doi.org/10.1002/rra.4181
- Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds P. Steer et al. https://doi.org/10.5194/esurf-10-1211-2022
- Downstream fining of fluvial gravels along the eastern Tibetan Plateau rivers Z. Ma et al. https://doi.org/10.1002/esp.70016
- Roughness Calibration to Improve Flow Predictions in Coarse‐Bed Streams R. Ferguson https://doi.org/10.1029/2021WR029979
- An Integrated Framework for the Assessment of Meso‐Scale Physical Habitats in Gravel‐Bed Rivers Using Remote Sensing and 2D Hydraulic Modeling D. Farò et al. https://doi.org/10.1002/wat2.70027
- How many microplastics do you need to (sub)sample? W. Cowger et al. https://doi.org/10.1016/j.ecoenv.2024.116243
- Localised geomorphic response to channel-spanning leaky wooden dams J. Wolstenholme et al. https://doi.org/10.5194/esurf-13-647-2025
- How many tagged stones are needed to estimate the mean travel distance of sediment from tracer experiments in gravel‐bed rivers? D. Vázquez‐Tarrío et al. https://doi.org/10.1002/esp.70352
- Surface grain-size mapping of braided channels from SfM photogrammetry L. Ribet et al. https://doi.org/10.5194/esurf-13-607-2025
Saved (final revised paper)
Latest update: 13 Aug 2026
Short summary
Researchers studying gravel bed rivers almost always require a sample of the bed surface grain size range. These samples are typically expressed as cumulative frequency distributions. We present statistical techniques for generating and plotting confidence intervals for the various size percentiles and for determining whether size percentiles from two samples are statistically different. The techniques are implemented in an R package; a simplified version is implemented in a spreadsheet.
Researchers studying gravel bed rivers almost always require a sample of the bed surface grain...
