Science Serving Maryland's Coasts

Research Publications: UM-SG-RS-2006-09

Title: 

Uncertainties in sediment erodibility estimates due to a lack of standards for experimental protocols and data interpretation.

Year: 

2006

Authors: 

Sanford, LP

Source: 

Integrated environmental assessment and management 2(1):29-34

DOI: 

10.1897/1551-3793(2006)2[29:UISEED]2.0.CO;2

Abstract: 

Quantitative prediction of the erodibility of muds and mud-sand mixtures is, at present, seldom possible without resorting to direct measurements, preferably in situ. A variety of devices and protocols have been developed for erosion testing, but a considerable degree of uncertainty remains with regard to the accuracy and comparability of the resulting data. This paper argues that differences in experimental protocols and data analysis procedures are a major contributing factor to uncertainty in estimates of sediment erodibility. In particular, the likelihood of a time-dependent erosion rate response under typical erosion testing conditions means that the time history of applied forcing and the chosen protocols for analyzing and interpreting data directly affect derived erosion parameters. Several straightforward ways to address this problem are suggested, including standardization of experimental design and data analysis protocols, explicit recognition and adoption of appropriate erosion model(s), and allowing for potential time/depth changes in erodibility. Experimentalists should also archive and share erosion-test time series, not just derived parameters, so that data sets may be reanalyzed within a different framework if necessary. An example is presented from an intercomparison experiment between the Virginia Institute of Marine Sciences Sea Carousel and the University of Maryland Center for Environmental Science Microcosm System, carried out in the upper Chesapeake Bay (Maryland, USA) in May 2002. Derived parameters appear to be incompatible when the data are analyzed using different procedures, but real similarities and differences are readily apparent when the data are analyzed using the same procedures.

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