Volume 8 Issue 1
Jan.  2015
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Michael HARTNETT, Stephen NASH. 2015: An integrated measurement and modeling methodology for estuarine water quality management. Water Science and Engineering, 8(1): 9-19. doi: 10.1016/j.wse.2014.10.001
Citation: Michael HARTNETT, Stephen NASH. 2015: An integrated measurement and modeling methodology for estuarine water quality management. Water Science and Engineering, 8(1): 9-19. doi: 10.1016/j.wse.2014.10.001

An integrated measurement and modeling methodology for estuarine water quality management

doi: 10.1016/j.wse.2014.10.001
Funds:  This work was supported by the Irish Environmental Protection Agency under the Environmental Monitoring, R&D Sub-Programme, Operational Programme for Environmental Sciences (Grant No. EPA_97_0151).
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  • Corresponding author: Michael HARTNETT
  • Received Date: 2014-07-30
  • Rev Recd Date: 2014-10-23
  • This paper describes research undertaken by the authors to develop an integrated measurement and modeling methodology for water quality management of estuaries. The approach developed utilizes modeling and measurement results in a synergistic manner. Modeling results were initially used to inform the field campaign of appropriate sampling locations and times, and field data were used to develop accurate models. Remote sensing techniques were used to capture data for both model development and model validation. Field surveys were undertaken to provide model initial conditions through data assimilation and determine nutrient fluxes into the model domain. From field data, salinity relationships were developed with various water quality parameters, and relationships between chlorophyll a concentrations, transparency, and light attenuation were also developed. These relationships proved to be invaluable in model development, particularly in modeling the growth and decay of chlorophyll a. Cork Harbour, an estuary that regularly experiences summer algal blooms due to anthropogenic sources of nutrients, was used as a case study to develop the methodology. The integration of remote sensing, conventional fieldwork, and modeling is one of the novel aspects of this research and the approach developed has widespread applicability.

     

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