Home > Explore Data & Reports > Sediment Flux Modeling: Calibration and application for coastal systems

Citation:

Brady, D.C., J.M. Testa, D.M. Di Toro, W.R. Boynton, and W.M. Kemp. 2013. Sediment Flux Modeling: Calibration and application for coastal systems. Estuarine, Coastal, and Shelf Science, 117:107-124. https://doi.org/10.1016/j.ecss.2012.11.003

Data/Report Type:

Sponsored Research

Description

Benthic–pelagic coupling in shallow estuarine and coastal environments is an important mode of particle and solute exchange and can influence lag times in the recovery of eutrophic ecosystems. Links between the water column and sediments are mediated by particulate organic matter (POM) deposition to the sediment and its subsequent decomposition. Some fraction of the regenerated nutrients are returned to the water column. A critical component in modeling sediment fluxes is the organic matter flux to the sediment. A method is presented for estimating POM deposition, which is especially difficult to measure, by using a combination of long term time series of measured inorganic nutrient fluxes and a mechanistic sediment flux model (SFM). A Hooke–Jeeves pattern search algorithm is used to adjust organic matter deposition to fit ammonium () flux at 12 stations in Chesapeake Bay for up to 17 years. POM deposition estimates matched reasonably well with sediment trap estimates on average (within 10%) and were strongly correlated with previously published estimates based upon sediment chlorophyll-a (chl-a) distribution and degradation. Model versus field data comparisons of flux, sediment oxygen demand (SOD), sulfate () reduction rate, porewater , and sedimentary labile carbon concentrations further validated model results and demonstrated that SFM is a powerful tool to analyze solute fluxes. Monthly model-field data comparisons clearly revealed that POM decomposition in the original SFM calibration was too rapid, which has implications for lagged ecosystem responses to nutrient management efforts. Finally, parameter adjustments have been made that significantly improve model-field data comparisons and underscore the importance of revisiting and recalibrating models as long time series (>5 years) become available.

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