**1. Introduction**

26 Hydrodynamics – Natural Water Bodies

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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 & Bhattacharyya, 2006).

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

Hydrodynamic Control of Plankton Spatial and

South).

N

TAMAS, respectively.

**Patos Lagoon**

**Mirim Lake**

**Lake Mangueira**

**Santa Vitória do Palmar**

**ESEC Taim**

**Taim Wetland**

**Rio Grande**

Temporal Heterogeneity in Subtropical Shallow Lakes 29

Três Forquilhas Basin

Fig. 1. Itapeva Lake in southern Brazil, with the three sampling points (North, Center and

**Atlantic Ocean**

Fig. 2. Lake Mangueira in southern Brazil. The meteorological and sampling stations in the North, Center and South parts of Lake Mangueira are termed TAMAN, TAMAC and

6.5 m. Its trophic state ranges from oligotrophic to mesotrophic (annual mean PO4 concentration 35 mg m-3, varying from 5 to 51 mg m-3). This lake is surrounded by a variety of habitats including dunes, pinus forests, grasslands, and two wetlands. This heterogeneous landscape harbors an exceptional biological diversity, which motivated the Brazilian federal authorities to protect part of the entire hydrological system as the Taim Ecological Station in 1991 (Garcia et al., 2006). The watershed (ca. 415 km2) is primarily used

**Porto Alegre**

Lake Quadros

**Taim Wetland** 

**Lake Mangueira**

**TAMAS** 

**TAMAC** 

North Station

Center Station

South Station Lake Itapeva

Atlantic Ocean

**TAMAN** 

of this effort has been expended on the description of phytoplankton in temperate lakes; thus, multi-nutrient, photo-acclimation models are now not uncommon (e.g., Olsen & Willen, 1980; Edmondson & Lehman, 1981; Sas, 1989; Fasham et al., 2006; Mitra & Flynn, 2007; Mitra et al., 2007). In subtropical lakes, eutrophication has been intensively studied, but only with a focus on measuring changes in nutrient concentrations (e.g., Matveev & Matveeva, 2005; Kamenir et al., 2007). A wide variety of phytoplankton models have been developed. The simplest models are based on a steady state or on the assumption of complete mixing (Schindler, 1975; Smith, 1980; Thoman & Segna, 1980). Phytoplankton models based on more complex vertical 1-D hydrodynamic processes give a more realistic representation of the stratification and mixing processes in deep lakes (Imberger & Patterson, 1990; Hamilton et al., 1995a; Hamilton et al., 1995b). However, the vertical 1-D assumption might be too restrictive, especially in large shallow lakes that are poorly stratified and often characterized by significant differences between the pelagic and littoral zones. In these cases, a horizontal 2-D model with a complete description of the hydrodynamic and ecological processes can offer more insight into the factors determining local water quality.

Currently, computational power no longer limits the development of 2-D and 3-D models, and these models are being used more frequently. Of the wide diversity of 2-D and 3-D hydrodynamic models, most were designed to study deep-ocean circulation or coastal, estuarine and lagoon zones (Blumberg & Mellor, 1987; Casulli, 1990). However, only a few models are coupled with biological components (Rajar & Cetina, 1997; Bonnet & Wessen, 2001).

In this chapter, we present the results of comparative modeling of two subtropical shallow lakes where the wind, and derived hydrodynamics, and river flow act as the main factors controlling plankton dynamics on temporal and spatial scales. The basic hypothesis is that wind and wind derived-hydrodynamics are the main factor determining the spatial and temporal distribution of plankton communities (Cardoso et al. 2003; Cardoso & Motta Marques, 2003, 2004a, 2004b, 2004c, 2009), in association with point incoming river flows.

The spatial heterogeneity of phytoplankton in Lake Mangueira is influenced by hydrodynamic patterns, and identifying zones with a higher potential for eutrophication and phytoplankton patchiness (Fragoso Jr. et al., 2008). The spatial patterns of chlorophyll-a concentrations generated by the model were validated both with a field data set and with a cloud-free satellite image provided by a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 1.0 km.
