Environmental Monitoring and Assessment

, Volume 184, Issue 11, pp 6935–6956 | Cite as

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

  • Marilia Mitidieri F. de Oliveira
  • Gilberto C. Pereira
  • Jorge Luiz F. de Oliveira
  • Nelson Francisco F. Ebecken
Article

Abstract

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/m3 for chlorophyll a, 2.20 μmol/l for total nitrogen and 5.5 ml/l for DO) and larvae concentrations (120 org/m3 for most of the larvae, except for cirripedia that presented values around 370 org/m3) 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.

Keywords

Larvae Nutrient concentrations Coastal waters Brazil upwelling 

References

  1. Ahrens, C. D. (2000). Meteorology today: An introduction to weather climate and the environment (6th ed.). London: Brooks/Cole.Google Scholar
  2. Andrade, L., Gonzalez, A. M., Valentin, J. L., & Paranhos, R. (2004). Bacterial abundance and production in the southwest Atlantic Ocean. Hydrobiology, 511, 103–111.CrossRefGoogle Scholar
  3. Anneville, O., Souissi, S., Gammeter, S., & Straile, D. (2004). Seasonal and inter-annual scales of variability in phytoplankton assemblages: comparison of phytoplankton dynamics in three peri-alpine lakes over a period of 28 years. Freshwater Biology, 49, 98–115.CrossRefGoogle Scholar
  4. Atlas, R., Busalacchi, A. J., Ghil, M., Bloom, S., & Kalnay, E. (1987). Global surface wind and flux fields from model assimilation of Seasat data. Journal of Geophysical Research, 92, 6477–6487.CrossRefGoogle Scholar
  5. Breitburg, D. L., Sanders, J. G., Gilmour, C. C., Hatfield, C. A., Osman, R. W., Riedel, G. F., Seitzinger, S. P., & Sellner, K. G. (1999). Variability in responses to nutrients and trace elements, and transmission of stressor effects through an estuarine food web. Limnology and Oceanography, 44, 837–863.CrossRefGoogle Scholar
  6. Campos, E. D., Miller, J. L., Muller, T. J., & Peterson, R. G. (1995). Physical oceanography of the Southwest Atlantic Ocean. Oceanography, 8, 87–91.Google Scholar
  7. Chao, Y., Li, Z., Kindle, J. C., Paduan, J. D., & Chavez, F. P. (2003). A high-resolution surface vector wind product for coastal oceans: blending satellite scatterometer measurements with regional mesoscale atmospheric model simulations. Geophysical Research Letters, 30, 13.1–13.4.Google Scholar
  8. Dever, E. P., Dorman, C. E., & Largier, J. L. (2006). Surface boundary-layer variability off Northern California, USA, during upwelling. Deep-Sea Research II, 53, 2887–2905.CrossRefGoogle Scholar
  9. Franchito, S. H., Oda, T. O., Rao, V. B., & Kayano, M. T. (2008). Interaction between coastal upwelling and local winds at Cabo Frio, Brazil: an observational study. Journal of Applied Meteorology and Climatology, 47, 1590–1598.CrossRefGoogle Scholar
  10. Fulford, R. S., & Breitburg, D. L. (2011). Differences in relative predaction vulnerability between native and non-native oyster larvae and the influence on restoration planning in an estuarine and ecosystem. Estuaries and Coasts, 34, 618–629.CrossRefGoogle Scholar
  11. Gaeta, S. A., Lorenzetti, J. A., Miranda, L. B., Susimi-Ribeiro, S. M. M., Pompeu, M., & Araújo, C. E. S. (1999). The Vitória Eddy and its relation to the phytoplankton biomass and primary production during the austral fall of 1995. Archive of Fishery and Marine Research, 47, 253–270.Google Scholar
  12. Griffa, A., Lumpkin, R., & Veneziani, M. (2008). Cyclonic and anticyclonic motion in the upper ocean. Geophysical Research Letters, 35(L01608), 1–5.Google Scholar
  13. Guimaraens, M. A., & Coutinho, R. (1996). Spatial and temporal variation of benthic marine algae at the Cabo Frio upwelling region. Aquatic Botany, 52, 283–299.CrossRefGoogle Scholar
  14. Harrington, R., Woiwod, I., & Sparks, T. (1999). Climate change and trophic interactions. Trends in Ecology & Evolution, 14, 146–150.CrossRefGoogle Scholar
  15. Jones, N. L., & Monismith, S. G. (2008). The influence of whitecapping waves on the vertical structure of turbulence in a shallow estuarine embayment. Journal of Physical Oceanography, 38, 1563–1580.CrossRefGoogle Scholar
  16. Kalnay, et al. (1996). The NCEP/NCAR 40 year reanalysis project. Bulletin American Meteorological Society, 177, 437–471.CrossRefGoogle Scholar
  17. Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van der Dool, H., Jenne, R., & Fiorino, M. (2001). The NCEP/NCAR 50 year reanalysis: monthly means CD-ROM and documentation. Bulletin American Meteorological Society, 82, 247–267.CrossRefGoogle Scholar
  18. Matano, R. P., Schlax, M., & Chelton, D. B. (2001). Seasonal variability in the southwestern Atlantic. Journal of Geophysical Research, 98, 18027–18035.CrossRefGoogle Scholar
  19. Moore,T., Morris, K., Blackwell, G., & Gibson, S. (1997). Extraction of beach landforms from dems using a Coastal Management Expert System. Annual Conference of GeoComputation, 2, New Zealand. Proceedings.Google Scholar
  20. Morgan, R. P., & Kline, K. M. (2010). Nutrient concentrations in Maryland non-tidal streams. Environment Monitoring and Assessment, 178, 221–235.CrossRefGoogle Scholar
  21. Myrberg, K., Andrejev, O., & Lehmann, A. (2010). Dynamic features of successive upwelling events in the Baltic Sea—a numerical case study. Oceanologia, 52, 77–99.CrossRefGoogle Scholar
  22. Neves, D. R. C. B., Pinho, J. L. S., & Vielra, J. M. P. (2008). Análise de Dados de Satélite Adequados à Caracterização da Produção Primária na Superfície Oceânica da Zee Portuguesa. Engenharia Civil-UM, 33, 125–138.Google Scholar
  23. Pereira, G. C., Coutinho, R., & Ebecken, N. F. F. (2008). Data mining for environmental analysis and diagnostic: a case study of upwelling ecosystem of Arraial do Cabo. Brazilian Journal of Oceanography, 56, 1–18.CrossRefGoogle Scholar
  24. Pezzi, L. P., Souza, R. B., Dourado, M. S., Garcia, C. A. E., Mata, M. M., & Silva-Dias, M. A. F. (2005). Ocean–atmosphere in situ observations at the Brazil-Malvinas Confluence region. Geophysical Research Letters, 32(L22603), 1–4.Google Scholar
  25. Pezzi, L. P. (2006). Variabilidade do Sistema Oceano-Atmosfera no Oceano Atlântico Sudoeste. I Seminário sobre sensoriamento remoto aplicado à pesca. INPE, S: J: dos Campos, Brasil.Google Scholar
  26. Piontkovski, S. A., Landry, M. R., Finenko, Z. Z., Kovalev, A. V., Williams, R., Gallienne, C. P., Mishonov, A. V., Skryabin, V. A., Tokarev, Y. N., & Nikolsky, V. N. (2003). Plankton communities of the South Atlantic anticyclonic gyre: Communautés planctoniques du tourbillon anticyclonique de l’Atlantique Sud. Oceanologica Acta, 26, 255–268.CrossRefGoogle Scholar
  27. Richard, T. A., & Thompson, T. G. (1952). The estimation and characterization of plankton population by pigment analyses. A spectrophotometric method for the estimation of plankton pigments. Journal of Marine Research, 11, 156–172.Google Scholar
  28. Salomon, et al. (2011). Bridging the divide between fisheries and marine conservation science. Bulletin of Marine Science, 87(2), 251–274.CrossRefGoogle Scholar
  29. Santos, I. R., Friedrich, A. C., & Ivar do Sul, J. A. (2009). Marine debris contamination along undeveloped tropical beaches from northeast Brazil. Environmental Monitoring Assessment, 148, 455–462.CrossRefGoogle Scholar
  30. SCOR. (1996). Protocols for the Joint Global Ocean Flux Study (JGOFS) core measurements. Bergen, Norway: Scientific Committee on Ocean Research. International Council of Scientific Unions, 9, 170 p.Google Scholar
  31. Sharma, N., & D’Sa, E. (2008). Assessment and analysis of QuikSCAT vector wind products for the Gulf of Mexico: a long-term and hurricane analysis. Sensors, 8, 1927–1949.CrossRefGoogle Scholar
  32. Shlens, J. (2005). A tutorial on principal component analysis. Avaiable in: http://scholar.google.com.br/scholar, pdf. Accessed 18 September 2010.
  33. Silveira, I. C. A., Schimidt, A. C. K., Campos, E. J. D., Godoi, S. S., & Ikeda, Y. (2000). The Brazil current off the Eastern Brazilian coast. Brazilian Journal of Oceanography, 48, 171–183.CrossRefGoogle Scholar
  34. Stech, J. L., & Lorenzzetti, J. A. (1992). The response of the South Brazil Bight to the passage of wintertime cold fronts. Journal of Geophysics Research, 97, 9507–9520.CrossRefGoogle Scholar
  35. Stewart, R. H. (2007). Introduction to physical oceanography. Texas A & M University.Google Scholar
  36. Soares, I., & Moller, O., Jr. (2001). Low-frequency currents and water mass spatial distribution on the southern Brazilian shelf. Continental Shelf Research, 21, 1785–1814.CrossRefGoogle Scholar
  37. Tang, W., Liu, W. T., & Stiles, B. W. (2004). Evaluation of high-resolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions. IEEE Transactions on Geoscience and Remote Sensing, 42, 1762–1769.CrossRefGoogle Scholar
  38. Topcu, D., Behrendt, H., Brockmann, U., & Claussen, U. (2010). Natural background concentrations of nutrients in the German Bight area (North Sea). Environment Monitoring and Assessment, 174, 361–388.CrossRefGoogle Scholar
  39. Valentin, J.L. (1989). The dynamics of plankton in the Cabo Frio upwelling. In: F.P. Brandini editor. Memórias do III EBP. Caiobá-Curitiba, BR.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Marilia Mitidieri F. de Oliveira
    • 1
  • Gilberto C. Pereira
    • 1
  • Jorge Luiz F. de Oliveira
    • 2
  • Nelson Francisco F. Ebecken
    • 1
  1. 1.Civil Engineering Postgraduate, Program-COPPE/UFRJ, Center of TechnologyFederal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Geography Postgraduate Program, Geoscience InstituteFluminense Federal University (UFF)NiteróiBrazil

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