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ARS Home » Southeast Area » Oxford, Mississippi » National Sedimentation Laboratory » Watershed Physical Processes Research » Research » Publications at this Location » Publication #382319

Research Project: Managing Water and Sediment Movement in Agricultural Watersheds

Location: Watershed Physical Processes Research

Title: Spatiotemporal patterns of fractional suspended sediment dynamics in small watersheds

Author
item AL-GHORANI, NISREEN - University Of British Columbia
item HASSAN, MARWAN - University Of British Columbia
item Langendoen, Eddy

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2021
Publication Date: 11/4/2021
Citation: Al-Ghorani, N.G., Hassan, M.A., Langendoen, E.J. 2021. Spatiotemporal patterns of fractional suspended sediment dynamics in small watersheds. Water Resources Research. 57(11): e2021WR030851. https://doi.org/10.1029/2021wr030851.
DOI: https://doi.org/10.1029/2021wr030851

Interpretive Summary: Suspended sediment is listed as one of the leading pollutants that adversely impact the quality of the Nation's waters. Effective management of suspended sediments at the watershed scale is difficult because of the spatial variability in sediment sources, land use and land management, and temporal lag effects caused by the event cycle of erosion and deposition. ARS researchers at Oxford, MS, in collaboration with scientists from the University of British Columbia, Canada, used novel techniques (wavelet transform and coherence) to analyze a comprehensive 20-year record of land use, rainfall, stream flow, and suspended sediment concentration collected by the USDA, ARS, National Sedimentation Laboratory in the Goodwin Creek Experimental Watershed at various spatial scales between 1982 and 2002. During the study period an overall decline in clay and silt suspended sediment load occurred across all time scales, which was primarily caused by land use change and in-channel stabilization. The spatiotemporal pattern of sand dynamics reflects both the state of channel stability and the availability of sand stored within the channel. The study shows that wavelet analysis can help evaluate the impact of land management practices on sediment transport processes, and therefore identify appropriate policies for future land use while mitigating environmental consequences.

Technical Abstract: Understanding suspended sediment dynamics and their driving factors is essential for effective watershed management. In this study, the spatiotemporal patterns of suspended sediment were identified for 12 sub-watersheds of the Goodwin Creek-Mississippi for the period 1982-2002 using wavelet transforms. The continuous wavelet transform (CWT) results of streamflow time series showed that a near-continuous annual periodicity influenced all stations over the study period, yet an overall decline occurred in wavelet spectral power of sediment load during the 1990s. This decline in sediment load is a result of the indirect effect of decreased cultivation on reducing the rate of peak flow and thus fluvial erosion processes. The CWTs of fine sediment (clays and silts) concentration (FSC) time series showed that factors other than land-use (e.g., ponds, ephemeral gullies, and bed composition) better explain the variations in FSC. A comparison of CWTs of FSC at two stations with different bed composition in their channels showed that sandy channels are less responsive to erosion control measures compared to gravely channels indicating that they are major sources of sediment. A change in phase relationship between flow and FSC for the gravel-dominated channel was associated with the transition in upland land-use indicating a change in sediment supply mechanisms. The CWTs of sand concentrations reflected the spatiotemporal impact of bank materials, the growth cycle of riparian vegetation, and in-channel sand availability on sand dynamics. While CWT-based analysis is sensitive to input conditions, the method appears to provide a powerful analysis solution helpful for parsing patterns in complex temporal datasets.