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

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

Fingerprint

canning
swans
estuaries
observatory
water quality
estuary
brackish water
modeling
environmental response
monitoring
nutrient enrichment
data management
prediction
environmental models
artificial intelligence
spatial resolution
hydrologic models
catchment
Western Australia
eutrophication

Cite this

@article{dea1fe54b0de48c0af4bb9a748a87c55,
title = "An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The swan-canning estuary virtual observatory",
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.",
keywords = "AED2, Decision support system, Ecosystem forecasting, Estuaries, Eutrophication, Real-time systems",
author = "Peisheng Huang and Kerry Trayler and Benya Wang and Amina Saeed and Oldham, {Carolyn E.} and Brendan Busch and Hipsey, {Matthew R.}",
year = "2019",
month = "11",
day = "1",
doi = "10.1016/j.jmarsys.2019.103218",
language = "English",
volume = "199",
journal = "Journal of Marine Systems",
issn = "0924-7963",
publisher = "Elsevier",

}

An integrated modelling system for water quality forecasting in an urban eutrophic estuary : The swan-canning estuary virtual observatory. / Huang, Peisheng; Trayler, Kerry; Wang, Benya; Saeed, Amina; Oldham, Carolyn E.; Busch, Brendan; Hipsey, Matthew R.

In: Journal of Marine Systems, Vol. 199, 103218, 01.11.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An integrated modelling system for water quality forecasting in an urban eutrophic estuary

T2 - The swan-canning estuary virtual observatory

AU - Huang, Peisheng

AU - Trayler, Kerry

AU - Wang, Benya

AU - Saeed, Amina

AU - Oldham, Carolyn E.

AU - Busch, Brendan

AU - Hipsey, Matthew R.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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.

KW - AED2

KW - Decision support system

KW - Ecosystem forecasting

KW - Estuaries

KW - Eutrophication

KW - Real-time systems

UR - http://www.scopus.com/inward/record.url?scp=85068891289&partnerID=8YFLogxK

U2 - 10.1016/j.jmarsys.2019.103218

DO - 10.1016/j.jmarsys.2019.103218

M3 - Article

VL - 199

JO - Journal of Marine Systems

JF - Journal of Marine Systems

SN - 0924-7963

M1 - 103218

ER -