Data for "Assessing Suspended Sediment Concentration for an Ungauged Arctic River"
Fluvial suspended sediments are a key driver of landscape evolution, ecohydrologic functioning, and water resource sustainability. For decades, monitoring suspended sediment transport relied on gauge data integrated with field measurements, making it difficult to assess rivers without any gauge stations or with limitd in-situ data. To quantify suspended sediment transport in ungauged rivers, this study develops a three-step workflow that integrates multispectral remote sensing analysis with minimal but targeted field measurements. Focusing on northern Alaska’s Noatak River, the largest ungauged and undammed river of North America, we quantified the spectral characteristics during ice-free seasons over 2020-2024 using PlanetScope imagery with high spatiotemporal resolution. We also conducted a field campaign to collect water samples for analyzing suspended sediment concentration (SSC) along with turbidity measurements. The campaign straddled a rainstorm that caused a rapid transformation of water from nearly clear to turbid within 36 hours, typical of medium-to-small-sized rivers in high-latitude or high-altitude environments, allowing for the establishment of a robust SSC-turbidity correlation. Using turbidity as a surrogate for SSC, we compared the explanatory power of various spectral band metrics for turbidity-spectral correlation and derived an inversion model for SSC estimation. The modeled SSC exhibited pronounced variability across both seasonal and event-based timescales, indicating the dominance of thermal-nival processes during the spring ice break-up and pluvial processes throughout the summer. With the pressing need for monitoring Arctic rivers under global environmental change, our approach offers a scalable and adaptable framework for sediment-transport monitoring in rivers.
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| Work Title | Data for "Assessing Suspended Sediment Concentration for an Ungauged Arctic River" |
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| License | CC BY 4.0 (Attribution) |
| Work Type | Dataset |
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| Publication Date | 2025 |
| DOI | doi:10.26207/t9d0-0697 |
| Deposited | June 13, 2025 |
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