**3.2 Lake Mangueira**

36 Hydrodynamics – Natural Water Bodies

0 9.3 18.7 cm/s

**Hydrological** 

of the prevailing currents.

environmental variable tested.

monitoring stations: South, Center and North.

**Water Quality Variable** 

Fig. 5. Numerical simulation of the vertically averaged velocity field in Itapeva Lake during the combination of low-flow condition in the Três Forquilhas River and high wind speed from the Northeast quadrant (20/05/1999 - 21/05/1999). Red arrows indicate the direction

The hydrodynamic variable explained 70% to 95% of the environmental variations for each seasonal campaign, using mean values for four-hour periods. Considering the entire lake and all seasonal campaigns, this explained 68% of the variation for turbidity and 49% for suspended solids. The hydrodynamic and environmental study were capable of evaluating that the changes in the water level as a function of runoff occur slowly, compared with the changes in the water level and seiches created by the effect of the wind on the lake. These variations in water levels and wind speeds have significant effects on the variability of the

Turbidity 12 4 hours 0.68 0.42 0.47 - 12.54 Suspended Solids 12 4 hours 0.49 0.43 0.27 - 63.63 Legend: Hydrological Variable – 1. water level (N), 2. water velocity (V); 3. wave height (H); 12- N-V; 13. N-H; 23. V-H; 123. N-V-H; R – correlation factor; C1, C2, C3 – N, V and H coefficients, respectively. Table 2. Results of multiple regression between water quality variable (dependent variable) and hydrological variables (independent variable) in Itapeva Lake considering the three

**South, Center and North** 

**Variable Mean R C1 C2 C3 error** 

The simulated and observed values of water levels at two stations of Lake Mangueira during the calibration and validation period are shown in Fig. 7. An independent validation data set showed a good fit to the hydrodynamic module (R2 ≥ 0.92). The model was able to reproduce the water level well in both extremities of Lake Mangueira. Wind-induced currents can be considered the dominant factor controlling transport of substances and phytoplankton in Lake Mangueira, producing advective movement of superficial water masses in a downwind direction. For instance, a southwest wind, with magnitude approximately greater than 4ms−1, can causes a significant transport of water mass and substances from south to north of Lake Mangueira, leading to a almost instantaneous increase of the water level in the northeastern parts and, hence the decrease of water level in southwestern areas (Fig. 7).

Our model results showed two characteristic water motions in the lake: oscillatory (seiche) and circulatory. Lake Mangueira is particularly prone to wind-caused seiches because of its shallowness, length (ca. 90 km), and width (ca. 12 km). These peculiar morphological features lead to significant seiches of up to 1 m between the south and north ends, caused by moderate-intensity winds blowing constantly along the longitudinal axis of the lake (NE-SW). Depending on factors such as fetch length and the intensity and duration of the wind, areas dominated by downwelling and upwelling can be identified (Fig. 8). For instance, if northeast winds last longer than about 6 h, the surface water moves toward the south shore, where the water piles up and sinks. Subsequently a longitudinal pressure gradient is formed and produces a strong flow in the deepest layers (below 3 m) toward the north shore, where surface waters are replaced by water that wells up from below. Such horizontal and vertical circulatory water motions may develop if wind conditions remain stable for a day or longer.

The model was also used to determine the spatial distribution of chlorophyll-a and to identify locations with higher growth and phytoplankton biomass in Lake Mangueira. Fig. 9 shows the spatial distribution of the phytoplankton biomass for different times during the simulation period.

Specifically, in Lake Mangueira there is a strong gradient of phytoplankton productivity from the littoral to pelagic zones (Fig. 9). Moreover, the model outcome suggests that there

Hydrodynamic Control of Plankton Spatial and

Jr. et al. (2011).

modeled as a fixed reduction of PAR.

Vector scale (m/s) 0.1 1.81 m/s

1.81 m/s

Vector scale (m/s) 0.1

Temporal Heterogeneity in Subtropical Shallow Lakes 39

**Jan/2001 Apr/2001** 

Jan/2001 Apr/2001

1.78 m/s

1.78 m/s

**Jul/2001 Sep/2001** 

Fig. 8. Simulated instantaneous currents in the surface (black arrows) and bottom (gray arrows) layers of Lake Mangueira at four different instants. A wind sleeve (circle), in each frame, indicates the instantaneous direction and the intensity of the wind. Source: Fragoso

In addition, it was possible to identify zones with the highest productivity. There is a trend of phytoplankton aggregation in the southwest and northeast areas, as the prevailing wind directions coincide with the longitudinal axis of Lake Mangueira. The clear water in the Taim wetland north of Lake Mangueira was caused by shading of emergent macrophytes,

After 1,200 hours of simulation (50 days), the daily balance between the total primary production and loss was negative. That means that daily losses such as respiration, excretion and grazing by zooplankton exceeded the primary production in the photoperiod, leading to a significant reduction of the chlorophyll-a concentration for the whole system (Fig. 9d; 9e). We verified the modeled spatial distribution of chlorophyll-a with the distribution estimated by remote sensing (Fig. 10). The simulated patterns had a reasonably good similarity to the

Jul/2001 Sep/2001

is a significant transport of phytoplankton and nutrients from the littoral to the pelagic zones through hydrodynamic processes. This transport was intensified by several large sandbank formations that are formed perpendicularly to the shoreline of the lake, carrying nutrients and phytoplankton from the shallow to deeper areas.

Fig. 7. Time series of wind velocity and direction on Lake Mangueira, and water levels fitted for the North and South parts of Lake Mangueira into the calibration and validation periods (solid line - observed, dotted line - calculated). Source: Fragoso Jr. et al. (2008).

is a significant transport of phytoplankton and nutrients from the littoral to the pelagic zones through hydrodynamic processes. This transport was intensified by several large sandbank formations that are formed perpendicularly to the shoreline of the lake, carrying

― North - Obs ―•― North - Cal ― North - Obs ―•― North - Cal

― South - Obs ―•― South - Cal ― South - Obs ―•― South - Cal

Fig. 7. Time series of wind velocity and direction on Lake Mangueira, and water levels fitted for the North and South parts of Lake Mangueira into the calibration and validation periods

(solid line - observed, dotted line - calculated). Source: Fragoso Jr. et al. (2008).

nutrients and phytoplankton from the shallow to deeper areas.

wind (m/s) wind (m/s) Water level (m) Water level (m) Water level (m) Water level (m)

Fig. 8. Simulated instantaneous currents in the surface (black arrows) and bottom (gray arrows) layers of Lake Mangueira at four different instants. A wind sleeve (circle), in each frame, indicates the instantaneous direction and the intensity of the wind. Source: Fragoso Jr. et al. (2011).

In addition, it was possible to identify zones with the highest productivity. There is a trend of phytoplankton aggregation in the southwest and northeast areas, as the prevailing wind directions coincide with the longitudinal axis of Lake Mangueira. The clear water in the Taim wetland north of Lake Mangueira was caused by shading of emergent macrophytes, modeled as a fixed reduction of PAR.

After 1,200 hours of simulation (50 days), the daily balance between the total primary production and loss was negative. That means that daily losses such as respiration, excretion and grazing by zooplankton exceeded the primary production in the photoperiod, leading to a significant reduction of the chlorophyll-a concentration for the whole system (Fig. 9d; 9e).

We verified the modeled spatial distribution of chlorophyll-a with the distribution estimated by remote sensing (Fig. 10). The simulated patterns had a reasonably good similarity to the

Hydrodynamic Control of Plankton Spatial and

subtropical ecosystems.

same date.

**3.3 Hydrodynamic versus plankton** 

Temporal Heterogeneity in Subtropical Shallow Lakes 41

Unfortunately we did not have independent data for phytoplankton in the simulation period. Therefore we could only compare the median values of simulated and observed chlorophyll-a, total nitrogen and total phosphorus for three points in Lake Mangueira, assuming that the median values were comparable between the years. The fit of these variables was reasonable, considering that we did not calibrate the biological parameters of the phytoplankton module (see results in Fragoso Jr. et al., 2008). The lack of spatially and temporally distributed data for Lake Mangueira made it impossible to compare simulated and observed values in detail. However, the good fit in the median values of nutrients and phytoplankton indicated that the model is a promising step toward a management tool for

Fig. 10. Lake Mangueira: (a) MODIS-derived chlorophyll-a image with 1-km spatial

resolution, taken on February 8, 2003; and (b) simulated chlorophyll-a concentration for the

Hydrodynamic processes and biological changes occurred over different spatial and temporal scales in these two large and long subtropical lakes. Itapeva Lake (31 km long) is almost one-third the size of Lake Mangueira (90 km long), and therefore the hydrodynamic response is faster in Itapeva Lake. On a time scale of hours, we can see the water movement from one end of the lake to the other (e.g., from N to S during a NE wind and in the opposite direction during a SW wind). Because of this rapid response, the plankton communities showed correspondingly rapid changes in composition and abundance, especially the phytoplankton when the resources (light and nutrients) responded promptly to wind action. This interaction between wind on a daily scale (hours) and the shape of Itapeva Lake was a determining factor for the observed fluctuations in the rates of change for phytoplankton (Cardoso & Motta Marques, 2003) as well as for the spatial distribution of plankton

patterns estimated from the remote-sensing data (Fig. 10a, b). In both figures, large phytoplankton aggregations can be observed in both the southern and northern parts of Lake Mangueira, as well as in the littoral zones.

Fig. 9. Phytoplankton dry weight concentration fields in μg l-1, for the whole system at different times: (a) 14 days; (b) 28 days; (c) 43 days; (d) 57 days; (e) 71 days; and (f) 86 days. The color bar indicates the phytoplankton biomass values. A wind sock in each frame indicates the direction and intensity of the wind. The border between the Taim wetland and Lake Mangueira is shown as well.

patterns estimated from the remote-sensing data (Fig. 10a, b). In both figures, large phytoplankton aggregations can be observed in both the southern and northern parts of

Fig. 9. Phytoplankton dry weight concentration fields in μg l-1, for the whole system at different times: (a) 14 days; (b) 28 days; (c) 43 days; (d) 57 days; (e) 71 days; and (f) 86 days. The color bar indicates the phytoplankton biomass values. A wind sock in each frame indicates the direction and intensity of the wind. The border between the Taim wetland and

Lake Mangueira is shown as well.

Lake Mangueira, as well as in the littoral zones.

Unfortunately we did not have independent data for phytoplankton in the simulation period. Therefore we could only compare the median values of simulated and observed chlorophyll-a, total nitrogen and total phosphorus for three points in Lake Mangueira, assuming that the median values were comparable between the years. The fit of these variables was reasonable, considering that we did not calibrate the biological parameters of the phytoplankton module (see results in Fragoso Jr. et al., 2008). The lack of spatially and temporally distributed data for Lake Mangueira made it impossible to compare simulated and observed values in detail. However, the good fit in the median values of nutrients and phytoplankton indicated that the model is a promising step toward a management tool for subtropical ecosystems.

Fig. 10. Lake Mangueira: (a) MODIS-derived chlorophyll-a image with 1-km spatial resolution, taken on February 8, 2003; and (b) simulated chlorophyll-a concentration for the same date.
