Infrastructure Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 4 of 4
-
ItemThe Brisbane Declaration and Global Action Agenda on Environmental Flows (2018)(FRONTIERS MEDIA SA, 2018-07-02)
-
ItemEnvironmental Water Efficiency: Maximizing Benefits and Minimizing Costs of Environmental Water Use and Management(WILEY, 2018)Environmental water management is a relatively new discipline, with concepts, management practice and institutional mechanisms that are still emerging. The efficient and effective use of environmental water to maximize environmental benefits, or environmental water use efficiency, is one such emerging concept. Currently, much of the focus is on allocative efficiency, where the objective is to achieve a better balance between consumptive and environmental water uses in a cost‐effective way. However, this may not provide the most efficient and effective way to manage environmental water in the long term, where managers are seeking productive (or operational) efficiency. Here, the objective is to maximize environmental outcomes relative to the cost of managing the available resource. This paper explores the concept of water use efficiency in the context of environmental water.
-
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.
-
ItemAssessing the degree of hydrologic stress due to climate change(Springer (part of Springer Nature), 2019-09-01)Hydrologists are commonly involved in impact, adaption and vulnerability assessments for climate change projections. This paper presents a framework for how such assessments can better differentiate between the impacts of climate change and those of natural variability, an important differentiation as it relates to the vulnerability to water availability under change. The key concept involved is to characterize “hydrologic stress” relative to the range of behaviour encountered under baseline conditions, where the degree to which climate change causes the behaviour of a system to shift outside this baseline range provides a non-dimensional measure of stress. The concept is applicable to any system that is subject to climate forcings, though the approach is applied here to a range of examples illustrative of many environmental and engineering applications. These include hydrologic systems that are dependent on the frequency of flows above or below selected thresholds, those that are dominated by storage and those which are sensitive to the sequencing of selected flow components. The analyses illustrate that systems designed or adapted to accommodate high variability are less stressed by a given magnitude of climate impacts than those operating under more uniform conditions. The metrics characterize hydrologic stress in a manner that can facilitate comparison across different regions, or across different assets within a region. Adoption of the approach requires reliance on the use of climate ensembles that represent aleatory uncertainty under both baseline and impacted conditions, and this has implications for how the outputs of climate models are provided and utilized.