An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The swan-canning estuary virtual observatory

Peisheng Huang, Kerry Trayler, Benya Wang, Amina Saeed, Carolyn E. Oldham, Brendan Busch, Matthew R. Hipsey

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Effective short- and long-term estuarine water quality management decisions require a holistic view of estuarine response to multiple stressors that may be achieved through the integration of numerical modelling and observed data. Such an approach has been developed for the Swan-Canning Estuary system, a eutrophic urban estuary in Western Australia under threat from nutrient enrichment and a drying climate. Numerical modelling was integrated with long-term monitoring to develop the system Swan-Canning Estuary Virtual Observatory (SCEVO), which has been used to facilitate water quality management and streamline prediction workflows of hindcast, forecast, and environmental response functions. The system is based on a validated 3D water quality model, integrated within a data management system and related environmental models. A machine-learning method to improve the patchy and time-lagged catchment inputs is also highlighted. This work has identified that the key challenge associated with estuarine water quality prediction is the capability to (1) simulate internal physical and biogeochemical processes at suitable spatial resolution to resolve the gradients along the freshwater-ocean continuum; and (2) transition from using routine monitoring data as the basis for management decisions to using a diverse and integrated set of data streams as the basis for real-time operational decisions. Recommendations for high-frequency monitoring to support water quality modelling and dynamic integration between numerical and observed data for improved forecasting are discussed.

Original languageEnglish
Article number103218
JournalJournal of Marine Systems
Volume199
DOIs
Publication statusPublished - 1 Nov 2019

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