Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories
Date
2015
Authors
Hipsey, M.
Hamilton, D.
Hanson, P.
Carey, C.
Coletti, J.
Read, J.
Ibelings, B.
Valesini, F.
Brookes, J.
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Advisors
Journal Title
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Volume Title
Type:
Journal article
Citation
Water Resources Research, 2015; 51(9):7023-7043
Statement of Responsibility
Matthew R. Hipsey, David P. Hamilton, Paul C. Hanson, Cayelan C. Carey, Janaine Z. Coletti, Jordan S. Read, Bas W. Ibelings, Fiona J. Valesini, and Justin D. Brookes
Conference Name
Abstract
Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management.
School/Discipline
Dissertation Note
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Description
Version of Record online: 2 SEP 2015
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Rights
© 2015. American Geophysical Union. All Rights Reserved.