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Water Resources Research

Optimizing an estuarine water quality monitoring program through an entropy-based hierarchical spatiotemporal Bayesian framework

Authors

  • Ibrahim Alameddine,

    Corresponding author
    1. Nicholas School of the Environment, Duke University, Durham, North Carolina
    2. Nicholas School is the Environmental Science and Policy Division, American University of Beirut, Beirut, Lebanon
    • Corresponding author: I. Alameddine, American University of Beirut, Department of Civil and Environmental Engineering, Bliss street, PO Box 11-0236, Beirut, NA, Lebanon. (ia04@aub.edu.lb)

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  • Subhankar Karmakar,

    1. Centre for Environmental Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India
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  • Song S. Qian,

    1. Nicholas School of the Environment, Duke University, Durham, North Carolina
    2. Department of Environmental Science, The University of Toledo, Ohio
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  • Hans W. Paerl,

    1. Institute of Marine Sciences, University of North Carolina at Chapel Hill, Morehead City, North Carolina
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  • Kenneth H. Reckhow

    1. Nicholas School of the Environment, Duke University, Durham, North Carolina
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Abstract

[1] The total maximum daily load program aims to monitor more than 40,000 standard violations in around 20,000 impaired water bodies across the United States. Given resource limitations, future monitoring efforts have to be hedged against the uncertainties in the monitored system, while taking into account existing knowledge. In that respect, we have developed a hierarchical spatiotemporal Bayesian model that can be used to optimize an existing monitoring network by retaining stations that provide the maximum amount of information, while identifying locations that would benefit from the addition of new stations. The model assumes the water quality parameters are adequately described by a joint matrix normal distribution. The adopted approach allows for a reduction in redundancies, while emphasizing information richness rather than data richness. The developed approach incorporates the concept of entropy to account for the associated uncertainties. Three different entropy-based criteria are adopted: total system entropy, chlorophyll-a standard violation entropy, and dissolved oxygen standard violation entropy. A multiple attribute decision making framework is adopted to integrate the competing design criteria and to generate a single optimal design. The approach is implemented on the water quality monitoring system of the Neuse River Estuary in North Carolina, USA. The model results indicate that the high priority monitoring areas identified by the total system entropy and the dissolved oxygen violation entropy criteria are largely coincident. The monitoring design based on the chlorophyll-a standard violation entropy proved to be less informative, given the low probabilities of violating the water quality standard in the estuary.

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