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Water Quality

The Water quality model simulates primary and secondary production, two key ecosystem processes and nutrient recycling. The forcing functions are temperature and light. In most studied cases light, which is influenced by phytoplankton self shading and suspended particles, and nutrient availability are clearly the most influencing factors.

Implementation - Water Quality Module

The Water Quality class was developed in terms of sinks and sources, which allows easy coupling to a transport module, both a Lagrangian and Eulerian formulations. Because of the properties interdependency a linear equation system is computed for each control volume and this system can be compute forward or backward in time.


The Water Quality class computes sinks and sources terms associate with the Carbon, Nitrogen and Phosphorous cycle. The properties that are change by this class are: phytoplankton, zooplankton and Nitrogen (Ammonia, Nitrate Nitrite, particulate organic nitrogen, dissolved refractory and non-refractory organic nitrogen), Phosphorus inorganic and organic, Dissolved Oxygen and BOD (Biochemical Demand of Oxygen). In this simplified approach only the Nitrogen and Phosphorus are found a limiting factor to the primary production, which is true to most coastal areas. Zooplankton is included in this model, enabling it to simulate accurately phenomena such as the spring bloom.


Analysis tools

Usually, to study a property’s temporal variation, we compute a time series in some point of the domain. Nevertheless, a question emerges, is the chosen point representative of the whole area?

 This creates a serious dilemma if we want to extrapolate conclusions about spatial variation. By the other hand the best way to look at spatial variation it’s plotting the property’s field at some time instant. By assembling different time instants maps in a sequence, it becomes possible to look at spatial and temporal variation together.

Although this “global” result can be quite satisfying, it may not be yet the ideal, if our goal is to look at characteristics of particular areas in the estuary. In that case the ideal solution must be one in which after defining the distinct areas, we integrate the results computed for each cell of that particular area.

This conclusion leads us to the concept of Integration Boxes. With this method it's possible, not only, to know the average property’s value in each area defined by the box, has well to compute the properties fluxes between boxes, which give us a great insight into the dynamical processes in the estuary.

With this method it becomes easy to determine and calibrate the energy fluxes between the subsystems, and to reach for more accurate answers from a quantitative point of view about the changes caused to the habitat by each simulation scenario.











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