Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

dc.contributor.authorHipsey, Matthew R.en
dc.contributor.authorHamilton, David P.en
dc.contributor.authorHanson, Paul C.en
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorColetti, Janaine Z.en
dc.contributor.authorRead, Jordan S.en
dc.contributor.authorIbelings, Bas W.en
dc.contributor.authorValesini, Fiona J.en
dc.contributor.authorBrookes, Justin D.en
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2017-10-26T13:33:05Zen
dc.date.available2017-10-26T13:33:05Zen
dc.date.issued2015-09-02en
dc.description.abstractMaintaining 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.en
dc.description.sponsorshipThis work was funded by the Australian Research Council grant DP130104078.en
dc.identifier.doihttps://doi.org/10.1002/2015WR017175en
dc.identifier.urihttp://hdl.handle.net/10919/79794en
dc.identifier.volume51en
dc.language.isoen_USen
dc.publisherAmerican Geophysical Unionen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titlePredicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatoriesen
dc.title.serialWater Resources Researchen
dc.typeArticle - Refereeden
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