Using Remote Sensing to Study Phytoplankton Biomass and Its Influence on Herbivore Fishery in the South-Eastern Arabian Sea
In this chapter, satellite remote sensing data are analysed to study the physical forcing that favors coastal upwelling and the variability in phytoplankton biomass in the South-Eastern Arabian Sea for a time period of 14 years. Analysis of monthly binned measurements of Sea Surface Temperature (SST) coupled with reanalysis wind data clearly demonstrate the dominating influence of the strong southwest monsoon in cooling the SST, breaking down the surface water warm pool, and the subsequent development of wind induced upwelling off the southwest coast of India. During southwest monsoon (June–September), the depth of the 20 °C isotherm shoals from typically 140 m to about 80 m. The development of negative Sea Level Anomaly (SLA) along with cyclonic eddies during summer monsoon season prove the occurrence of divergence and upwelling. Ekman mass transport computed using the monthly reanalysis wind data show strong negative values during the southwest monsoon, which indicate strong upwelling along the coastal regions of southwest India, also discernible from the high chlorophyll-a (Chl-a) concentrations (≥1 mg m−3) during southwest monsoon season, which decrease to about 0.2–0.5 mg m−3 in non-monsoon months. Increased primary production triggers higher catches of Indian oil sardine ( Sardinella longiceps Valenciennes), and the analysis of fish landing data for the State of Kerala, India show that the sardine catch follows Chl-a peaks with a lag of one season with significant positive correlation.
KeywordsArabian Sea Coastal upwelling Chlorophyll-a Indian oil sardine Herbivore fishery Ekman mass transport
This work was initiated under the project INDO-European Research Facilities for Studies on MARine Ecosystem and CLIMate in India (INDO-MARECLIM, GA#295092) supported by the European Commission under the Seventh Framework Programme (INCO-LAB). Authors gratefully acknowledge the financial support provided by Nansen Environmental and Remote Sensing Center (NERSC) in Bergen, Norway. Authors acknowledge the use of OC CCI dataset, Version [3.1], ESA, available online at http://www.esa-oceancolour-cci.org/. The authors thank Frontier Research System for Global Change, Japan Marine Science and Technology Center/Japan. 2005 for the Subsurface Temperature Analyses by Ishii et al., Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, http://rda.ucar.edu/datasets/ds285.3/. Authors acknowledge CMFRI, Kochi for providing the fish landing data. Authors acknowledge Dr. Ajith K. Joseph for his comments during the final discussions of this paper.
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