A 2-D process-based model for suspended sediment dynamics: A first step towards ecological modeling
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Abstract
In estuaries suspended sediment concentration (SSC) is one of the most important contributors to turbidity, which influences habitat conditions and ecological functions of the system. Sediment dynamics differs depending on sediment supply and hydrodynamic forcing conditions that vary over space and over time. A robust sediment transport model is a first step in developing a chain of models enabling simulations of contaminants, phytoplankton and habitat conditions.
This works aims to determine turbidity levels in the complex-geometry delta of the San Francisco estuary using a process-based approach (Delft3D Flexible Mesh software). Our approach includes a detailed calibration against measured SSC levels, a sensitivity analysis on model parameters and the determination of a yearly sediment budget as well as an assessment of model results in terms of turbidity levels for a single year, water year (WY) 2011.
Model results show that our process-based approach is a valuable tool in assessing sediment dynamics and their related ecological parameters over a range of spatial and temporal scales. The model may act as the base model for a chain of ecological models assessing the impact of climate change and management scenarios. Here we present a modeling approach that, with limited data, produces reliable predictions and can be useful for estuaries without a large amount of processes data.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A 2-D process-based model for suspended sediment dynamics: A first step towards ecological modeling |
| Series title | Hydrology and Earth System Sciences |
| DOI | 10.5194/hess-19-2837-2015 |
| Volume | 19 |
| Issue | 6 |
| Year Published | 2015 |
| Language | English |
| Publisher | European Geosciences Union |
| Contributing office(s) | Pacific Coastal and Marine Science Center |
| Description | 21 p. |
| First page | 2837 |
| Last page | 2857 |
| Google Analytic Metrics | Metrics page |