| one publication added to basket [108933] | Quantifying biologically and physically induced flow and tracer dynamics in permeable sediments
Meysman, F.J.R.; Galaktionov, O.S.; Cook, P.L.M.; Janssen, F.; Huettel, M.; Middelburg, J.J. (2006). Quantifying biologically and physically induced flow and tracer dynamics in permeable sediments. Biogeosci. Discuss. 3: 1809-1858 In: Biogeosciences Discussions. Copernicus Publications: Göttingen. ISSN 1810-6285, more |   |
| Authors | | Top | - Meysman, F.J.R., more
- Galaktionov, O.S.
- Cook, P.L.M.
| - Janssen, F.
- Huettel, M.
- Middelburg, J.J., more
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| Abstract | Insight in the biogeochemistry and ecology of sandy sediments crucially depends on a quantitative description of pore water flow and the associated transport of various solutes and particles. Here, we compare and analyse existing models of tracer dynamics in permeable sediments. We show that all models can be derived from a generic backbone, consisting of the same flow and tracer equations. The principal difference between model applications concerns the geometry of the sediment-water interface and the pressure conditions that are specified along this boundary. We illustrate this commonality with four different case studies. These include biologically and physicallyinduced pore water flows, as well as simplified laboratory set-ups versus more complex field-like conditions: [1] lugworm bio-irrigation in laboratory set-up, [2] interaction of bio-irrigation and groundwater seepage on a tidal flat, [3] pore water flow induced by rotational stirring in benthic chambers, and [4] pore water flow induced by unidirectional flow over a ripple sequence. To illustrate the potential of the generic model approach, the same two example simulations are performed in all four cases: (a) the time-dependent spreading of an inert tracer in the pore water, and (b) the computation of the steady-state distribution of oxygen in the sediment. Overall, our model comparison indicates that model development is promising, but within an early stage. Clear challenges remain in terms of model development, model validation, and model implementation. |
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