School of Agriculture, Food and Ecosystem Sciences - Research Publications

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    Towards a systems approach for river basin management-Lessons from Australia's largest river
    Thompson, RM ; Bond, N ; Poff, NL ; Byron, N (WILEY, 2019-06)
    Abstract Globally, large river systems have been extensively modified and are increasingly managed for a range of purposes including ecosystem services and ecological values. Key to managing rivers effectively are developing approaches that deal with uncertainty, are adaptive in nature, and can incorporate multiple stakeholders with dynamic feedbacks. Australia's largest river system, the Murray–Darling Basin (MDB), has been extensively developed for shipping passage, irrigation, hydroelectric development, and water supply. Water development in the MDB over the last century resulted in overallocation of water resources and large‐scale environmental degradation throughout the Basin. Under the pressure of a significant drought, there was insufficient water to supply critical human, environmental, and agricultural needs. In response, a massive programme of water reform was enacted that resulted in considerable institutional, social, and economic change. The underlying policy was required to be enacted in an absence of certainty around the scientific basis, with an adaptive management focus to incorporate new knowledge. The resulting institutional arrangements were challenged by a need to generate new governance arrangements within the constraints of existing state and national structures. The ongoing reform and management of the MDB continues to challenge all parties to achieve optimization for multiple outcomes, and to communicate that effectively. As large‐scale water reform gains pace globally, the MDB provides a window of insight into the types of systems that may emerge and the challenges in working within them. Most particularly, it illustrates the need for much more sophisticated systems thinking that runs counter to the much more linear approaches often adopted in government.
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    Assessment of environmental flow scenarios using state-and-transition models
    Bond, NR ; Grigg, N ; Roberts, J ; McGinness, H ; Nielsen, D ; O'Brien, M ; Overton, I ; Pollino, C ; Reid, JRW ; Stratford, D (WILEY, 2018-08)
    Abstract Numerous methods have been developed to support the assessment of environmental flow requirements for rivers. Most methods are based around models of hydrologic time series rather than models of the ecological endpoints of interest. Important limitations that arise from this include (1) an inability to represent the state dependency of response to future conditions (i.e. the effects of current ecosystem condition on future condition), (2) the inability to predict ecological states through time under alternative flow regimes and (3) limited sensitivity to compare the differences between flow regimes with similar return intervals of ecologically important events, but different sequencing of those events. Here we outline a simple state‐and‐transition modelling approach to assess differences in ecological responses to alternative sequences of floodplain inundation events in a lowland river system. Our approach explicitly incorporates the state dependency of biotic response to flooding, thereby representing the influences of both antecedent conditions and current condition (in this case population state; good > medium > poor > critical). Our approach thus captures the influence of the entire historical sequence of flow events via a first‐order Markov chain process. We use prior data and expert opinion to determine state transitions for a broad suite of ecological indicators. Despite being implemented with deterministic transitions, and drawing heavily on expert opinion, this approach greatly improves on existing methods used in environmental flows planning, particularly when comparing scenarios with the different sequencing of ecologically relevant flow events. The outputs from these models are testable, and the approach is readily extensible to incorporate probabilistic state transitions and uncertainty, mechanistic links (via increased model complexity) and quantitative measures of population state (e.g. measures of abundance or tree condition). Most importantly, the adoption of such a framework represents a fundamental shift to modelling ecological endpoints rather than relying on just quantifying hydrologic surrogates to compare environmental flow scenarios.