Large and mesoscale meteo-oceanographic patterns in local responses of biogeochemical concentrations

Investigations surrounding the variability of productivity in upwelling regions are necessary for a better understanding the physical-biological coupling in these regions by monitoring systems of environmental impacts according to the needs of the regional coastal management. Using a spatial and temporal database from National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric (NCAR) Research reanalysis, Quick Scatterometer vector wind, and surface stations from the Southeast coast of Brazil, we investigate the meteorological influences due to the large-scale systems in the variability of the nutrient and larvae concentration, and chlorophyll a, describing statistically relationships between them in upwelling regions. In addition, we used multivariate analysis, such as PCA and clustering to verify spatial and temporal variances and describe more clear the structure and composition of the ecosystem. Correlation matrix analyses were applied for different water masses present in the study area to identify the relations between physical and biogeochemical parameters in a region, where frequently upwelling occur. Statistical approaches and seasonal variability show that the period of November to March is more sensitive to nutrients (1.20?mg/m(3) for chlorophyll a, 2.20?μmol/l for total nitrogen and 5.5?ml/l for DO) and larvae concentrations (120 org/m(3) for most of the larvae, except for cirripedia that presented values around 370 org/m(3)) relating to the influence of large and mesoescale meteorological patterns. The spatial and temporal variables analyzed with multivariate approach show meaningful seasonality variance of the physical and biological samples, characterizing the principal components responsible for this variance in spring and summer (upwelling period), emphasizing the monitoring of species as crustaceans and mussels that are present in the local economy. Then, the spring and summer season are characterized by high productivity due to the occurrence of upwelling in this period.     

Fuzzy modelling of chlorophyll production in a Brazilian upwelling system

In the context of coastal management the aim of this paper is to present the development of a fuzzy model through the application of a Genetic algorithm in order to select the most appropriate set of variables and improve our understanding with a set of rules. The case studied is the chlorophyll response as bioindicator of ecological status in the Northeast coastal upwelling system of Rio de Janeiro state, Southeastern of Brazil. The prediction of the fuzzy model has shown an improved performance when compared to the traditional approaches as Multiple Regression modelling. The results show that the set of inferred rules can assess three different water masses. Despite the increased occurrence of upwelling is observed in spring–summer period and some instability of the model, it is able to forecast some chlorophyll peaks. We conclude that the sampling frequency is crucial to reach a better performance.