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Abstract
The study presented here found evidence for the presence and importance of convective flows and associated oxygen transport in a constructed wetland pond in southern Finland. These flows are triggered by nightly cooling of the water at the surface, which may then become denser than the water lower down in the pond. The resulting layering (heavier water overlying less dense one) is hydromechanically unstable and—almost immediately—starts driving convective motion. This flow takes oxygen enriched water from the surface to the bottom of the wetland, where a distinct rise in oxygen saturation is recorded after some time lag. The process described can be modelled successfully by means of so-called Multilayer Perceptrons (MLPs), a class of Artificial Neural Networks. As explored in this study, these models are well suited to “learn” the mechanism of convective transport, which results in their ability to forecast oxygen saturation near the wetland bottom at a satisfactory level of accuracy.
ACKNOWLEDGMENT
The research reported here was partly funded by the European Commission under contract no. EVK1-CT-2000-00065 (PRIMROSE). This support is gratefully acknowledged.