Infrastructure Engineering - Research Publications
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ItemQuantifying and predicting the benefits of environmental flows: Combining large-scale monitoring data and expert knowledge within hierarchical Bayesian models(WILEY, 2018-08)Abstract Despite large investments of public funds into environmental flows programs, we have little ability to make quantitative predictions of the ecological benefits of restored flow regimes. Rather, ecological predictions in environmental flow assessments typically have been qualitative and based largely upon expert opinion. Widely applicable, quantitative models would help to justify existing flow programs and to inform future planning. Here, we used a hierarchical Bayesian analysis of monitoring data coupled with expert‐derived prior distributions, to develop such a model. We quantified the relationship between the duration and frequency of inundation, and encroachment of terrestrial vegetation into regulated river channels. The analysis was informed by data from 27 sites on seven rivers. We found that longer inundation durations reduce terrestrial vegetation encroachment. For example, a 50‐day continuous inundation during winter reduced predicted vegetation cover to a median of 11% (95% CI: 7%–35%) of cover predicted under non‐inundated conditions. This effect varied among sites and rivers, and was moderated by the frequency of inundation events. The hierarchical structure improved precision of model predictions relative to simpler analysis structures. Informative prior distributions also improved precision relative to minimally informative priors. The hierarchical Bayesian analysis allows us to make quantitative predictions of ecological response under the full range of flow conditions, allowing us to assess the benefits of planned or delivered environmental flows. It can be used to make estimates of ecological effects at sites that have not been sampled, and also to scale up site‐level results to catchment and regional scales. Quantitative predictions of ecological effects provide a more objective risk‐based approach, allowing improved planning of environmental flows and building public confidence in these major investments of public funds.
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ItemInforming Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles(SPRINGER, 2018-03)One important aspect of adaptive management is the clear and transparent documentation of hypotheses, together with the use of predictive models (complete with any assumptions) to test those hypotheses. Documentation of such models can improve the ability to learn from management decisions and supports dialog between stakeholders. A key challenge is how best to represent the existing scientific knowledge to support decision-making. Such challenges are currently emerging in the field of environmental water management in Australia, where managers are required to prioritize the delivery of environmental water on an annual basis, using a transparent and evidence-based decision framework. We argue that the development of models of ecological responses to environmental water use needs to support both the planning and implementation cycles of adaptive management. Here we demonstrate an approach based on the use of Conditional Probability Networks to translate existing ecological knowledge into quantitative models that include temporal dynamics to support adaptive environmental flow management. It equally extends to other applications where knowledge is incomplete, but decisions must still be made.
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ItemMake the Most of the Data You've Got: Bayesian Models and a Surrogate Species Approach to Assessing Benefits of Upstream Migration Flows for the Endangered Australian Grayling(SPRINGER, 2018-03)Environmental water managers must make best use of allocations, and adaptive management is one means of improving effectiveness of environmental water delivery. Adaptive management relies on generation of new knowledge from monitoring and evaluation, but it is often difficult to make clear inferences from available monitoring data. Alternative approaches to assessment of flow benefits may offer an improved pathway to adaptive management. We developed Bayesian statistical models to inform adaptive management of the threatened Australian grayling (Prototroctes maraena) in the coastal Thomson River, South-East Victoria Australia. The models assessed the importance of flows in spring and early summer (migration flows) for upstream dispersal and colonization of juveniles of this diadromous species. However, Australian grayling young-of-year were recorded in low numbers, and models provided no indication of the benefit of migration flows. To overcome this limitation, we applied the same models to young-of-year of a surrogate species (tupong-Pseudaphritis urvilli)-a more common diadromous species expected to respond to flow similarly to Australian grayling-and found strong positive responses to migration flows. Our results suggest two complementary approaches to supporting adaptive management of Australian grayling. First, refine monitoring approaches to allow direct measurement of effects of migration flows, a process currently under way. Second, while waiting for improved data, further investigate the use of tupong as a surrogate species. More generally, alternative approaches to assessment can improve knowledge to inform adaptive management, and this can occur while monitoring is being revised to directly target environmental responses of interest.