Globally, many rivers are experiencing declining water quality, for example, with altered levels of sediments, salts, and nutrients. Effective water quality management requires a sound understanding of how and why water quality differs across space, both within and between river catchments. Land cover, land use, land management, atmospheric deposition, geology and soil type, climate, topography, and catchment hydrology are the key features of a catchment that affect:(1) the amount of suspended sediment, nutrient, and salt concentrations in catchments (i.e., the source), (2) the mobilization ,and (3) the delivery of these constituents to receiving waters. There are, however, complexities in the relationship between landscape characteristics and stream water quality. The strength of this relationship can be influenced by the distance and spatial arrangement of constituent sources within the catchment, cross correlations between landscape characteristics, and seasonality. A knowledge gap that should be addressed in future studies is that of interactions and cross correlations between landscape characteristics. There is currently limited understanding of how the relationships between landscape characteristics and water quality responses can shift based on the other characteristics of the catchment. Understanding the many forces driving stream water quality and the complexities and interactions in these forces is necessary for the development of successful water quality management strategies. This knowledge could be used to develop predictive models, which would aid in forecasting of riverine water quality.

The science and practice of environmental flows have advanced significantly over the last several decades. Most environmental flow approaches require quantifying the relationships between hydrologic change and biologic response, but this can be challenging to determine and implement due to high data requirements, limited transferability, and the abundance of hydrologic metrics available for evaluation. We sug-

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Climate change will increase water stress in many regions placing greater pressures on rivers to meet human and ecological water needs. Managing rivers experiencing water stress requires a fundamental understanding of how ecosystem processes and functions respond to natural and anthropogenic drivers of flow variability and change. The field of environmental flows meets this need by defining “flow-ecology” relationships—mathematical models linking ecological characteristics and dynamics to the underlying flow regime. However, because these relationships are most often based on historical hydrologic regimes, they implicitly assume climatic stationarity. A fundamental challenge in the Anthropocene is how to model flow-ecology relationships such that the effects of nonstationarity can be captured. In the present article, we introduce a novel approach that addresses these shortcomings and show its utility through a series of conceptual and empirical examples. The framework incorporates ecological dynamics and uncertain future hydrologic conditions, as well as nonstationarity itself, thereby providing a viable framework for modeling flow-ecology responses to inform water management in a rapidly changing climate.

Adaptive management enables managers to work with complexity and uncertainty, and to respond to changing biophysical and social conditions. Amid considerable uncertainty over the benefits of environmental flows, governments are embracing adaptive management as a means to inform decision making. This Special Issue of Environmental Management presents examples of adaptive management of environmental flows and addresses claims that there are few examples of its successful implementation. It arose from a session at the 11th International Symposium on Ecohydraulics held in Australia, and is consequently dominated by papers from Australia. We classified the papers according to the involvement of researchers, managers and the local community in adaptive management. Five papers report on approaches developed by researchers, and one paper on a community-led program; these case studies currently have little impact on decision making. Six papers provide examples involving water managers and researchers, and two papers provide examples involving water managers and the local community. There are no papers where researchers, managers and local communities all contribute equally to adaptive management. Successful adaptive management of environmental flows occurs more often than is perceived. The final paper explores why successes are rarely reported, suggesting a lack of emphasis on reflection on management practices. One major challenge is to increase the documentation of successful adaptive management, so that benefits of learning extend beyond the project where it takes place. Finally, moving towards greater involvement of all stakeholders is critical if we are to realize the benefits of adaptive management for improving outcomes from environmental flows.

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In order to assess how emerging science and new tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Sydney, Australia. This workshop aimed to explore the potential offered by new approaches to characterise stressor regimes, to explore stressor-response relationships among biota, to design better early-warning systems and to develop smart tools to support sustainable management of human activities, through more efficient regulation. In this paper we highlight the key issues regarding biological coverage, the complexity of multiply stressed environments, and our inability to predict the biological effects under such scenarios. To address these challenges, we provide an extension of the current Environmental Risk Assessment framework. Underpinning this extension is the harnessing of environmental-genomic data, which has the capacity to provide a broader view of diversity, and to express the ramifications of multiple stressors across multiple levels of biological organisation. We continue to consider how these and other emerging data sources may be combined and analysed using new statistical approaches for disentangling the effects of multiple stressors.

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