Sediment Source Assessment Tool (Sed_SAT)
A sound understanding of sources contributing to instream sediment flux in a watershed is important when developing total maximum daily load (TMDL) management strategies designed to reduce suspended sediment in streams. Sediment fingerprinting and sediment budget approaches are two techniques, that when used jointly, can qualify and quantify the major sources of sediment in a given watershed. The sediment fingerprinting approach uses trace element concentrations from samples in known potential source areas to determine a clear signature of each potential source. A mixing model is then used to determine the relative source contribution to the target suspended sediment samples.
The computational steps required to apportion sediment for each target sample are quite involved and time intensive, a problem the Sediment Source Assessment Tool (Sed_SAT) addresses. Sed_SAT is a user-friendly statistical model that guides the user through the necessary steps in order to quantify the relative contributions of sediment sources in a given watershed. The model is written using the statistical software R and utilizes Microsoft Access(TM) as a user interface but requires no prior knowledge of R or Microsoft Access(TM) to successfully run the model successfully. Sed_SAT identifies outliers, corrects for differences in size and organic content in the source samples relative to the target samples, evaluates the conservative behavior of tracers used in fingerprinting by applying a "Bracket Test," identifies tracers with the highest discriminatory power, and provides robust error analysis through a Monte Carlo simulation following the mixing model. Quantifying sediment source contributions using the sediment fingerprinting approach provides local, state, and federal land management agencies with important information needed to implement effective strategies to reduce sediment. Sed_SAT is designed to assist these agencies in applying the sediment fingerprinting approach to quantify sediment sources in the sediment TMDL framework.
Citation Information
Publication Year | 2017 |
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Title | Sediment Source Assessment Tool (Sed_SAT) |
DOI | 10.5066/F76Q1VBX |
Authors | Lillian E Gorman Sanisaca, Allen C Gellis, David Lorenz |
Product Type | Software Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | MD-DE-DC Water Science Center |