Modelagem da dinâmica de algas e cianobactérias em um reservatório de abastecimento - Salgado, Aline de Arvelos (2018)
Shallow reservoirs, with large surface area and long hydraulic retention times, present conditions conducive to the development of cyanobacteria. Due to the potential risk that cyanobacteria can cause to public health, the importance of the management of water bodies to control the toxic blooms, especially in reservoirs destined to the public supply, is evident. Thus, there is a clear need to develop techniques that can help existing monitoring, with the possibility of extending the area of ​​coverage in monitoring programs. Thus, the present study aimed to combine several methods of data acquisition, mainly obtaining continuously, such as the use of sensors for in situ evaluation of phytoplankton, especially cyanobacteria, through the detection of chlorophyll-a and phycocyanin, as well as the use of remote sensing for phytoplankton monitoring and with this information to build water quality prediction scenarios through mathematical modeling. The procedure aimed to improve data acquisition, especially the spatialization of data. For this purpose, the dynamics and water quality of the João Leite reservoir was characterised over 30 months using physical, chemical and biological parameters and trophic status index, to know the seasonality of the quality of the of the water body and present the main parameters and factors of water quality change. A continuous and spatial phytoplankton monitoring method was proposed for mapping concentrations of chlorophyll- a and cyanobacteria from Sentinel-2 satellite images with in situ and inferential observations to evaluate the performance of these to provide information on cyanobacterial blooms. The CE-QUAL-W2 model was calibrated from field data to evaluate the water quality dynamics and hydrodynamics of the reservoir. As a result, João Leite reservoir presented characteristic and similar patterns of the variation of the parameters in hydrological periods, and the results showed that the use and the occupation of the soil reflect in the characteristics of the quality of the water. In the majority of the evaluated points and times, good oxygenation of the waters and low concentrations of nutrients were demonstrated, being proved by the trophic state indexes. Both chlorophyll-a and cyanobacteria showed correlation between data measured in situ and estimated. The results can considered satisfactory since with the data of reflectance it was possible to validate the atmospheric correction, and the maps of estimate of chlorophyll- a and cyanobacteria presented the places and the months more prone to the greater proliferation of cyanobacteria. Thus, these results demonstrate the importance of the use of remote sensing for the monitoring of water quality, since with the use of this tool there is greater possibility of point coverage, being able to have a synoptic visualisation of the studied environment, identifying the most subject areas to the proliferation of cyanobacteria. The results of the modeling indicate that the reservoir presents periodic stratification, and in the warmer months the stratification is more evident and that in the colder months a mixture of the water column is noticed, possibly due to in those months there are more incidental winds in the region. In general, the highest values ​​of algae and cyanobacteria occur in the warmer months, where there is a higher solar incidence and larger periods of thermal stratification occur. It was also presented that the amount of algae and cyanobacteria on the surface changes the amount of oxygen dissolved in the surface region while the demand for sediment oxygen may be associated with both the oxygen consumption in the sediments and can also be used as a way to simulate the consumption of oxygen by the submerged organic matter, which is visible in the shallower areas of the reservoir. The results of the CE-QUAL-W2 model provided a comprehensive understanding of the spatial and temporal dynamics of the parameters analyzed during the modeling periods. The application of this model with the integration of fluorometers and remote sensing for the evaluation of water quality, should be better suited to water resource managers, to help them better understand the dynamics of physicochemical and biological processes and to be used as a warning system in cases of cyanobacteria blooms and to propose the best mitigation strategies.