A sustainable Murray-Darling Basin : the legal challenges

The range of legal instruments informing how the Murray-Darling Basin (MDB)is managed is extensive. Some provide guidance; a number indicate strategies and policies; some assume the form of protectable rights and enforceable duties.What has emerged is a complicated and sophisticated web of interacting normative arrangements. These include: several international agreements including those concerning wetlands,biodiversity and climate change; the Constitution of the Commonwealth; the Water Act 2007 of the Commonwealth; the Murray-Darling Basin Agreement scheduled to the Act; State water entitlements stated in the Agreement; Commonwealth environmental water holdings under the Act; the Murray-Darling Basin Plan; water-resource plans under the Act or State or Territorial water legislation; State and Territorial water legislation; and water entitlements and water rights under State or Territorial water legislation.

Knowledge gain or system benefit in environmental decision making?

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Managing moose harvests by the seat of your pants

Moose populations are managed for sustainable yield balanced against costs caused by damage to forestry or agriculture and collisions with vehicles. Optimal harvests can be calculated based on a structured population model driven by data on abundance and the composition of bulls, cows, and calves obtained by aerial-survey monitoring during winter. Quotas are established by the respective government agency and licenses are issued to hunters to harvest an animal of specified age or sex during the following autumn. Because the cost of aerial monitoring is high, we use a Management Strategy Evaluation to evaluate the costs and benefits of periodic aerial surveys in the context of moose management. Our on-the-fly "seat of your pants" alternative to independent monitoring is management based solely on the kill of moose by hunters, which is usually sufficient to alert the manager to declines in moose abundance that warrant adjustments to harvest strategies. Harvests are relatively cheap to monitor; therefore, data can be obtained each year facilitating annual adjustments to quotas. Other sources of "cheap" monitoring data such as records of the number of moose seen by hunters while hunting also might be obtained, and may provide further useful insight into population abundance, structure and health. Because conservation dollars are usually limited, the high cost of aerial surveys is difficult to justify when alternative methods exist. © 2012 Elsevier Inc.

Optimizing search strategies for invasive pests: Learn before you leap

Strategic searching for invasive pests presents a formidable challenge for conservation managers. Limited funding can necessitate choosing between surveying many sites cursorily, or focussing intensively on fewer sites. While existing knowledge may help to target more likely sites, e.g. with species distribution models (maps), this knowledge is not flawless and improving it also requires management investment. 2.In a rare example of trading-off action against knowledge gain, we combine search coverage and accuracy, and its future improvement, within a single optimisation framework. More specifically we examine under which circumstances managers should adopt one of two search-and-control strategies (cursory or focussed), and when they should divert funding to improving knowledge, making better predictive maps that benefit future searches. 3.We use a family of Receiver Operating Characteristic curves to reflect the quality of maps that direct search efforts. We demonstrate our framework by linking these to a logistic model of invasive spread such as that for the red imported fire ant Solenopsis invicta in south-east Queensland, Australia. 4.Cursory widespread searching is only optimal if the pest is already widespread or knowledge is poor, otherwise focussed searching exploiting the map is preferable. For longer management timeframes, eradication is more likely if funds are initially devoted to improving knowledge, even if this results in a short-term explosion of the pest population. 5.Synthesis and applications. By combining trade-offs between knowledge acquisition and utilization, managers can better focus - and justify - their spending to achieve optimal results in invasive control efforts. This framework can improve the efficiency of any ecological management that relies on predicting occurrence. © 2010 The Authors. Journal of Applied Ecology © 2010 British Ecological Society.

Barriers, drivers and enablers for transitioning towards innovation in the Australian water sector

The Australian water sector needs to adapt to effectively deal with the impacts of climate change on its systems. Challenges as a result of climate change include increasingly extreme occurrences of weather events including flooding and droughts (Pittock, 2011). In response to such challenges, the National Water Commission in Australia has identified the need for the water sector to transition towards being readily adaptable and able to respond to complex needs for a variety of supply and demand scenarios (National Water Commission, 2013). To successfully make this transition, the sector will need to move away from business as usual, and proactively pursue and adopt innovative approaches and technologies as a means to successfully address the impacts of climate change on the Australian water sector. In order to effectively respond to specific innovation challenges related to the sector, including climate change, it is first necessary to possess a foundational understanding about the key elements related to innovation in the sector. This paper presents this base level understanding, identifying the key barriers, drivers and enablers, and elements for innovative practise in the water sector. After initially inspecting the literature around the challenges stemming from climate change faced by the sector, the paper then examines the findings from the initial two rounds of a modified Delphi study, conducted with experts from the Australian water sector, including participants from research, government and industry backgrounds. The key barriers, drivers and enablers for innovation in the sector identified during the initial phase of the study formed the basis for the remainder of the investigation. Key elements investigated were: barriers – scepticism, regulation systems, inconsistent policy; drivers – influence of policy, resource scarcity, thought leadership; enablers – framing the problem, effective regulations, community acceptance. There is a convincing argument for the water sector transitioning to a more flexible, adaptive and responsive system in the face of challenges resulting from climate change. However, without first understanding the challenges and opportunities around making this transition, the likelihood of success is limited. For that reason, this paper takes the first step in understanding the elements surrounding innovation in the Australian water sector.

Sustainable urban stormwater management : water sensitive urban design perceptions, drivers and barriers

Stormwater has been recognised as one of the main culprits of aquatic ecosystem pollution and as a significant threat to the goal of ecological sustainable development. Water sensitive urban design is one of the key responses to the need to better manage urban stormwater runoff, the objectives of which go beyond rapid and efficient conveyance. Underpinned by the concepts of sustainable urban development, water sensitive urban design has proven to be an efficient and environmentally-friendly approach to urban stormwater management, with the necessary technical know-how and skills already available. However, large-scale implementation of water sensitive urban design is still lacking in Australia due to significant impediments and negative perceptions. Identification of the issues, barriers and drivers that affect sustainability outcomes of urban stormwater management is one of the first steps towards encouraging the wide-scale uptake of water sensitive urban design features which integrate sustainable urban stormwater management. This chapter investigates key water sensitive urban design perceptions, drivers and barriers in order to improve sustainable urban stormwater management efforts.

A multivariate approach to the identification of surrogate parameters for heavy metals in stormwater

Stormwater is a potential and readily available alternative source for potable water in urban areas. However, its direct use is severely constrained by the presence of toxic pollutants, such as heavy metals (HMs). The presence of HMs in stormwater is of concern because of their chronic toxicity and persistent nature. In addition to human health impacts, metals can contribute to adverse ecosystem health impact on receiving waters. Therefore, the ability to predict the levels of HMs in stormwater is crucial for monitoring stormwater quality and for the design of effective treatment systems. Unfortunately, the current laboratory methods for determining HM concentrations are resource intensive and time consuming. In this paper, applications of multivariate data analysis techniques are presented to identify potential surrogate parameters which can be used to determine HM concentrations in stormwater. Accordingly, partial least squares was applied to identify a suite of physicochemical parameters which can serve as indicators of HMs. Datasets having varied characteristics, such as land use and particle size distribution of solids, were analyzed to validate the efficacy of the influencing parameters. Iron, manganese, total organic carbon, and inorganic carbon were identified as the predominant parameters that correlate with the HM concentrations. The practical extension of the study outcomes to urban stormwater management is also discussed.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Water end use consumption analysis study into Gold Coast dual reticulated households : pilot study

Australia is the driest inhabited continent in the world and persisting droughts have triggered a move toward sensible and sustainable water consumption. Understanding how and where water is consumed in households enables streamlined development of demand management programs and efficient engineering of water infrastructure. End use water consumption analysis is required to gain necessary empirical data of how and where water is consumed. Several end use water consumption studies have been conducted within Australia and around the world with varying results produced. This pilot study paper provides preliminary data from the Gold Coast Watersaver End Use Project which is currently underway. Specifically, the paper includes water end use category volumetric and percentage break downs for 18 single and 32 dual reticulated homes on the Gold Coast (i.e. 50 in total). Moreover, a comparitive analysis between each of the individual households water end use levels is discussed along with other national studies previously completed. The paper finishes with an overview of the greater 200 home end use study conducted on the Gold Coast and its key deliverables and research outcomes.

Role of land use and seasonal factors in water quality degradation

Surface water and groundwater are the most important water sources in the natural environment. Land use and seasonal factors play an important role in influencing the quality of these water sources. An in-depth understanding of the role of these two influential factors can help to implement an effective catchment management strategy for the protection of these water sources. This paper discusses the outcomes of an extensive research study which investigated the role of land use and seasonal factors on surface water and groundwater pollution in a mixed land use coastal catchment. The study confirmed that the influence exerted on the water environment by seasonal factors is secondary to that of land use. Furthermore, the influence of land use and seasonal factors on surface water and groundwater quality varies with the pollutant species. This highlights the need to specifically take into consideration the targeted pollutants and the key influential factors for the effective protection of vulnerable receiving water environments.

Assessing resilience of water resources systems under the impacts of climate change

This paper presents an approach to assess the resilience of a water supply system under the impacts of climate change. Changes to climate characteristics such as rainfall, evapotranspiration and temperature can result in changes to the global hydrological cycle and thereby adversely impact on the ability of water supply systems to meet service standards in the future. Changes to the frequency and characteristics of floods and droughts as well as the quality of water provided by groundwater and surface water resources are the other consequences of climate change that will affect water supply system functionality. The extent and significance of these changes underline the necessity for assessing the future functionality of water supply systems under the impacts of climate change. Resilience can be a tool for assessing the ability of a water supply system to meet service standards under the future climate conditions. The study approach is based on defining resilience as the ability of a system to absorb pressure without going into failure state as well as its ability to achieve an acceptable level of function quickly after failure. In order to present this definition in the form of a mathematical function, a surrogate measure of resilience has been proposed in this paper. In addition, a step-by-step approach to estimate resilience of water storage reservoirs is presented. This approach will enable a comprehensive understanding of the functioning of a water storage reservoir under future climate scenarios and can also be a robust tool to predict future challenges faced by water supply systems under the consequence of climate change.

An approach for identifying the limit states of resilience of a water supply system

This paper presents an approach for identifying the limit states of resilience in a water supply system when influenced by different types of pressure (disturbing) forces. Understanding of systemic resilience facilitates identification of the trigger points for early managerial action to avoid further loss of ability to provide satisfactory service availability when the ability to supply water is under pressure. The approach proposed here is to illustrate the usefulness of a surrogate measure of resilience depicted in a three dimensional space encompassing independent pressure factors. That enables visualisation of the transition of the system-state (resilience) between high to low resilience regions and acts as an early warning trigger for decision-making. The necessity of a surrogate measure arises as a means of linking resilience to the identified pressures as resilience cannot be measured directly. The basis for identifying the resilience surrogate and exploring the interconnected relationships within the complete system, is derived from a meta-system model consisting of three nested sub-systems representing the water catchment and reservoir; treatment plant; and the distribution system and end-users. This approach can be used as a framework for assessing levels of resilience in different infrastructure systems by identifying a surrogate measure and its relationship to relevant pressures acting on the system.

Atmospheric particles as a pollutant source in the urban water environment in Sri Lanka – current research trends and future directions

Human and ecosystem health impacts imposed by water pollution are a major problem in the urban areas of Sri Lanka. A primary source of pollutants to urban water sources is atmospheric particles. Hence, it is important to develop a detailed understanding of atmospheric particle characteristics, their sources of origin and the transport pathways. Several research studies have been conducted in Sri Lanka on atmospheric pollution and these studies have tended to differ in their scope, study region and the investigated pollutants. The objectives of this paper are: (1) to report the outcomes of a detailed state-of-art literature review of atmospheric pollution related studies in Sri Lanka to understand the current trends and (2) to discuss the future research activities necessary to generate the important knowledge required for the development of effective strategies to control the adverse impacts of atmospheric pollution on urban waterways.