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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.

Interdecadal cycles in rainfall influence long-term hydrological variability, affecting water resource management, agriculture, and flood or drought preparedness across the globe. Previous studies have found evidence of cycles over limited regions but the global distribution and relationship to the major climate modes remain unclear. Using the global GPCC v2022 2.5° gridded dataset (1891–2020), we applied a Gaussian mixture model to detect significant clustering of cycles in rainfall, derived from wavelet analysis of individual grid points. Three Global Rainfall Cycles (GRCs) emerged at 12.9, 19.9, and 28.2 years, were widespread, and aligned in length and phase to previous research. Two longer cycles (35.9 and 45.9 years) were also significant but interpreted cautiously due to their period relative to the dataset's length. The 12.9 and 19.9 year GRCs showed strong phase coherence and spatial overlap with the El Niño-Southern Oscillation and Interdecadal Pacific Oscillation climate modes, but not with the Indian Ocean Dipole or North Atlantic Oscillation. Notably, GRCs explained more rainfall variance than expected from the effect of these climate modes alone, suggesting another driver may influence rainfall directly and via climate interactions. These findings are of significance to global water management and rainfall modelling, offering the potential to enhance flood and drought forecasting in strongly affected regions.

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.

Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

In this book, four leading experts critique methods used to manage flows in regulated streams and rivers to balance environmental (instream) and out-of-stream uses of water. Intended for managers as well as practitioners, the book dissects the shortcomings of commonly used approaches, and offers practical advice for selecting and implementing better ones. The authors argue that methods for environmental flow assessment (EFA) can be defensible as well as practicable only if they squarely address uncertainty, and provide guidance for doing so. Introductory chapters describe the scientific and social reasons that EFA is hard, and provide a brief history. Because management of regulated streams starts with understanding freshwater ecosystems, this book includes chapters on flow and organisms in streams. The following chapters assess standard and emerging methods, how they should be tested, and how they should (or should not) be applied. The book concludes with practical recommendations for implementing environmental flow assessment.

Environmental flows aim to influence river hydrology to provide appropriate physical conditions for ecological functioning within the restrictions of flow regulation. The hydrologic characteristics of flow events, however, may also lead to unintended morphologic effects in rivers, such as increases in riverbank erosion beyond natural rates. This may negatively impact habitat for biota, riparian infrastructure, and land use. Strategic environmental flow delivery linked to monitoring and adaptive management can help mitigate risks. We monitor riverbank condition (erosion and deposition) relative to environmental flows on the Goulburn River, Victoria, Australia. We describe the process of adaptive management aimed at reducing potential impacts of flow management on bank condition. Field measurements (erosion pins) quantify the hydrogeomorphic response of banks to the delivery of planned and natural flow events. Managed flows provide opportunities for monitoring riverbank response to flows, which in turn informs planning. The results demonstrate that environmental flows have little influence on bank erosion and visual perceptions in the absence of monitoring are an unreliable guide. This monitoring project represents a mutually beneficial, science-practice partnership demonstrating that a traditional ‘know then do’ approach can be foreshortened by close collaboration between researchers and managers. To do so requires transparent, often informal lines of communication. The benefits for researchers–a more strategic and targeted approach to monitoring activities; and benefits for the practitioners–reduced time between actions and understanding response; mean that a learn by doing approach is likely to have better outcomes for researchers, stakeholders, the public, and the environment.

Environmental water represents a key resource in managing freshwater ecosystems against pervasive threats. The impacts of climate change add further pressures to environmental water management, yet anticipating these impacts through modelling approaches remains challenging due to the complexities of the climate, hydrological and ecological systems. In this paper, we review the challenges posed by each of these three areas. Large uncertainties in predicting climatic changes and non-stationarities in hydrological and ecological responses make anticipating impacts difficult. In addition, a legacy of relying on modelling approaches informed by historic dependencies in environmental water science may confound the prediction of ecological responses when extrapolating under novel conditions. We also discuss applying ecohydrological methods to support decision-making and review applications of bottom-up climate impact assessments (specifically eco-engineering decision scaling) to freshwater ecosystems. These approaches offer a promising way of incorporating climatic uncertainty and balancing competing environmental objectives, but some practical challenges remain in their adoption for modelling environmental water outcomes under climate change.

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.

The decline in global freshwater biodiversity demands urgent action. Governments are attempting to use environmental management to partly restore degraded ecosystems through targeted interventions. Designing monitoring programs to assess the success of these large-scale management programs is challenging. There is much literature addressing the technical challenges of monitoring program design, and many of these studies acknowledge limitations in current implementation. In this study, we examine the perspectives of those managers and scientists involved in designing a large-scale monitoring program and their understanding of what makes a monitoring program successful. We focus on an environmental flow monitoring program (the Flow Monitoring, Evaluation and Research program—Flow-MER—in Australia). Through semi-structured interviews and surveys, we aimed to identify what those involved consider to be “success” for monitoring projects. The outcomes highlight that—consistent with literature—clear objectives are considered pivotal to project success. However, despite this recognition, challenges in establishing clear objectives were identified as a pressing concern for the Flow-MER program. The survey results included a recurring emphasis from participants on the importance of consistent, long-term datasets. There was less clarity around how to balance monitoring design to both demonstrate management success and address key scientific uncertainties as part of adaptive management and monitoring. The findings show that while there is broadly a common understanding of success for large monitoring design, major monitoring programs such as Flow-MER continue to fall short in successful design. The approach to surveying those involved in the monitoring program, along with their articulated understanding of program shortfalls, both provide insights on how to improve design and implementation of future large-scale monitoring programs. In particular, we highlight the need for managers to establish clear objectives and invest in effective communication strategies.