**5. References**


Formoselha bridge (located 30 km upstream the estuary mouth), during a spring tide

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The development of integrated methodologies linking tracer experimental approach with hydroinformatic tools (based on 2D and 3D mathematical models) is of paramount interest because they can constitute a accurate and useful operational tool to establish better warning systems and to improve management practices for efficiently protecting water

The MONDEST model developed and applied in this work allowed the evaluation and ranking of potential mitigation measures (like nutrient loads reduction or dredging works for hydrodynamic circulation improvement). So, the proposed methodology, integrating hydrodynamics and water quality, constitutes a powerful hydroinformatic tool for enhancing estuarine eutrophication vulnerability assessment, in order to contribute for better water quality management practices and to achieve a true sustainable development.

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propagation.

**5. References** 

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Florida, USA.


**2** 

**Hydrodynamic Control of Plankton** 

**in Subtropical Shallow Lakes** 

*Universidade Federal do Rio Grande do Sul (UFRGS)* 

*2Instituto de Pesquisas Hidráulicas (IPH) 3Universidade Federal de Alagoas (UFAL)* 

*1Instituto de Biociências* 

*Centro de Tecnologia* 

*Brazil* 

**Spatial and Temporal Heterogeneity** 

Luciana de Souza Cardoso1, Carlos Ruberto Fragoso Jr.3, Rafael Siqueira Souza2 and David da Motta Marques2

During the last 200 years, many lakes have suffered from eutrophication, implying an increase of both nutrient loading and organic matter (Wetzel, 1996). An aspect that has often been neglected in freshwater systems is the fact that phytoplankton is often not evenly distributed horizontally in space in shallow lakes. Although the occurrence of phytoplankton patchiness in marine systems has been known for a long time (e.g., Platt et al., 1970; Steele, 1978; Steele & Henderson, 1992), phytoplankton in shallow lakes is often assumed to be homogeneously distributed. However, there are various mechanisms that may cause horizontal heterogeneity in shallow lakes. For example, grazing by aggregated zooplankton and other organisms may cause spatial heterogeneity in phytoplankton (Scheffer & De Boer, 1995). Submerged macrophyte beds may be another mechanism, through reduction of resuspension by wave action and allopathic effects on the algal community (Van den Berg et al., 1998). For large shallow lakes, wind can be a dominant factor leading to both spatial and temporal heterogeneity of phytoplankton (Carrick et al., 1993), either indirectly by affecting the local nutrient concentrations due to resuspended particles, or directly by resuspending algae from the sediment (Scheffer, 1998). In the management of large lakes, prediction of the phytoplankton distribution can assist the manager to decide on an optimal course of action, such as biomanipulation and regulation of the use of the lake for recreation activities or potable water supply (Reynolds, 1999). However, it is difficult to measure the spatial distribution of phytoplankton. Mathematical modeling of a phytoplankton can be an important alternative methodology in improving our knowledge regarding the physical, chemical and biological processes related to phytoplankton ecology (Scheffer, 1998; Edwards & Brindley, 1999; Mukhopadhyay &

Over the past decade there has been a concerted effort to increase the realism of ecosystem models that describe plankton production as a biological indicator of eutrophication. Most

**1. Introduction** 

Bhattacharyya, 2006).

