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 language | English |
|---|---|
| Article number | 103218 |
| Journal | Journal of Marine Systems |
| Volume | 199 |
| DOIs | |
| Publication status | Published - 1 Nov 2019 |
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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 journal › Article
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 -