Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories
Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of ﬂexible model libraries and data from observationnetworks, 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 sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identiﬁed as a means to develop and inte-grate diverse model libraries and workﬂows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory 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.
|Publication Subtype||Journal Article|
|Title||Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories|
|Series title||Water Resources Research|
|Online Only (Y/N)||N|
|Additional Online Files (Y/N)||N|
|Google Analytic Metrics||Metrics page|