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  • Development of catchment water quality models within a realtime status and forecast system for the Great Barrier Reef

    Author(s)
    Khan, U
    Cook, FJ
    Laugesen, R
    Hasan, MM
    Plastow, K
    Amirthanathan, GE
    Bari, MA
    Tuteja, NK
    Griffith University Author(s)
    Cook, Freeman J.
    Year published
    2020
    Metadata
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    Abstract
    Realtime information on sediment and nutrients generated and transported from catchments is essential to inform management decisions aimed to improve the ecosystem health of the Great Barrier Reef (GBR). A water quality modelling methodology is developed to provide accurate and reliable estimates of sediments, dissolved and particulate nutrients, i.e., Nitrogen and Phosphorus for historical simulations, realtime status and forecasts. A water quality model is built for seven key water quality constituents at ten locations in eight GBR catchments using a non-linear multivariate regression technique. Covariates used in the ...
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    Realtime information on sediment and nutrients generated and transported from catchments is essential to inform management decisions aimed to improve the ecosystem health of the Great Barrier Reef (GBR). A water quality modelling methodology is developed to provide accurate and reliable estimates of sediments, dissolved and particulate nutrients, i.e., Nitrogen and Phosphorus for historical simulations, realtime status and forecasts. A water quality model is built for seven key water quality constituents at ten locations in eight GBR catchments using a non-linear multivariate regression technique. Covariates used in the multivariate regression models were derived from either streamflow, baseflow, or time-based cyclical processes. The performance of models varied by site location and constituent and out of 67 models developed here, 27 models have NSE values > 0.5 and 57 models have NSE values > 0.3. These 67 hourly models have been used to generate historical simulations and forecasts of concentration and load.
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    Journal Title
    Environmental Modelling and Software
    Volume
    132
    DOI
    https://doi.org/10.1016/j.envsoft.2020.104790
    Subject
    Environmental management
    Publication URI
    http://hdl.handle.net/10072/397438
    Collection
    • Journal articles

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