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Near Channel Suspended Sediment Prediction Dataset


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Created: Jul 23, 2025 at 7:28 p.m. (UTC)
Last updated: Jul 23, 2025 at 7:44 p.m. (UTC)
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Abstract

High concentrations of suspended sediment (SSC) in a river can represent a critical water quality concern, reduce the storage capacity of reservoirs, and impact aquatic habitat. Within a river, SSC can be conceptualized as a function of reach-scale hydraulics translating discharge into shear stress and watershed processes that determine the types and quantities of sediment supplied to the river. To explore watershed controls on sediment supply, we utilized SSC data from over 1000 US Geological Survey gages spread across the continental United States (CONUS). We find that the geometric mean SSC spans over five orders of magnitude with clustered high and low values throughout the CONUS indicating a dependence on regional watershed properties. Here we utilize publicly available geospatial datasets (topography, soils, land use, and climate) to explore the potential dependence of mean SSC for over 100 variables. We find that catchment-wide and point-scale geospatial variables provide few explanatory univariate trends for the observed mean SSC patterns. We utilized principal components analysis to reduce the dimensionality of the exploration to a limited subset of variables. Extreme variability within mean SSC and data limitations prevents a complete prediction of SSC from geospatial data, however multiple nonlinear regression reveals that the geospatial pattern in mean SSC is primarily a function of climate (aridity), vegetation, and soil properties. Understanding SSC dependence on watershed properties represents an important step for linking watershed processes and fine-grained transport dynamics and how changes in climate and the environment may further affect sediment volumes and watershed management.

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The content of this resource is derived from Sigman, Aaron J., "Exploring the Role of Near Channel Geospatial Attributes to Predict Suspended Sediment Concentration Patterns Across the CONUS Region" (2024). All Graduate Theses and Dissertations, Fall 2023 to Present. 121. https://digitalcommons.usu.edu/etd2023/121

How to Cite

Sigman, A. (2025). Near Channel Suspended Sediment Prediction Dataset, HydroShare, http://www.hydroshare.org/resource/ff550d5a764d49b5881845847469cf6b

This resource is shared under the Creative Commons Attribution CC BY.

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