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Across the globe, environmental water has been allocated with the purpose of preserving the health and vitality of floodplain vegetation. However, the influences of environmental water volume and environmental water delivery strategies have not been studied widely because of shortage of on‐ground monitoring data. Remotely sensed data can bridge this gap by providing long‐term and continuous information; Landsat imagery from 1988 to 2020 was used in this research. We used the normalized difference vegetation index (NDVI) as an indicator of physiological condition of lake‐fringing trees on the Hattah Lakes floodplain, south‐east Australia. We employed the random forest (RF) regression method to model the relationship between NDVI and various climate and hydrological factors, such as the volume of water delivered to the connected lakes system as environmental water allocations or natural floods. The RF models performed well overall, with a mean R2 value of 0.73. The analysis identified the monthly total of environmental water delivered 3 months prior to the Landsat image date as a more crucial factor than natural floods over the same period for driving vegetation condition. Environmental water from 3 months previously exerts a positive influence on NDVI until the volume reaches a specific threshold. We have observed significant improvements in floodplain vegetation through the current environmental water strategy, particularly since the construction of pumping infrastructure in 2013. We suggest that managers aim to inundate the lake fringing area every 3 years, specifically from August to September, by delivering environmental water up to the modelled threshold volume. Finally, the use of infrastructure has proven to be an effective and efficient method for irrigating floodplain lakes, leading to improvements in vegetation condition while conserving water resources.

River flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, by dams suppressing multi‐day variability or vegetation attenuating flood peaks in some ecosystems. The relative importance of the physical, biological, and human factors influencing flow is an active area of research, as is the related question of finding a common language for describing overall flow regime. Here, we addressed both topics using a daily river discharge data set for over 3,000 stations across the globe from 1988 to 2016. We first studied similarities between common flow regime quantification methods, including traditional flow metrics, wavelets, and Fourier analysis. Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. One such pattern was that day‐to‐day variability was negatively correlated with year‐to‐year variability. Additionally, the low‐dimensional structure in river flow data correlated closely with only a small number of catchment characteristics, including catchment area, precipitation, and temperature—but notably not biome, dam surface area, or number of dams. We discuss these findings in a framework intended to be accessible to the many communities engaged in river research and management, while stressing the importance of letting structure in data guide both mechanistic inference and interdisciplinary discussion.

Environmental flows (e-flows) management takes place within a complex social-ecological system, necessitating the involvement of diverse stakeholders and an appreciation of a range of perspectives and knowledge types. It is widely accepted that incorporating participatory methods into environmental flows decision-making will allow stakeholders to become meaningfully involved, improving potential solutions, and fostering social legitimacy. However, due to substantial structural barriers, implementing participatory approaches can be difficult for water managers. This paper assesses the effectiveness of an e-flows methodology that combines elements of structured decision-making and participatory modeling, whilst constrained by project resources. Three process-based objectives were identified by the group at the start of the process: improving transparency, knowledge exchange, and community ownership. We evaluated the success of the approach according to those objectives using semi-structured interviews and thematic analysis. In evaluating how well the participatory approach achieved the process objectives, we found that at least 80% of respondents expressed positive sentiment in every category (n = 15). We demonstrate that the values-based process objectives defined by the participant group are an effective tool for evaluating participatory success. This paper highlights that participatory approaches can be effective even in resource-constrained environments when the process is adapted to fit the decision-making context.

In the face of mounting environmental and political challenges in river management, accurate and timely scientific information is required to inform policy development and guide effective management of waterways. The Murray–Darling Basin is Australia's largest river system by area and is the subject of a heavily contested series of water reforms relying comprehensively on river science. River scientists have specialised knowledge that is an important input into evidence‐based decision‐making for the management of the Murray–Darling Basin, but despite extensive literature on the interface between science and policy, there is little guidance on achieving policy relevance for practicing scientists. Here, we provide a set of important discussion points for water scientists to consider when engaging with policy‐makers and environmental water managers. We place our considerations in the context of a broader literature discussing the role of natural‐resource scientists engaging with policy and management. We then discuss the different roles for river scientists when engaging in this space, and the advantages and pitfalls of each. We illustrate the breadth of modes of engagement at the science‐policy‐management interface using the Murray–Darling Basin as an example. We emphasise the need for effective governance arrangements and data practices to protect scientists from accusations of operating as advocates when working to inform management and policy.

Rivers are dynamic social-ecological systems that support societies and ecosystems in a multitude of ways, giving rise to a variety of user groups and competing interests. Environmental flows (e-flows) programs developed to protect riverine environments are often conceived by water managers and researchers. This is despite continued calls for increased public participation to include local communities and Indigenous peoples in the development process. Failure to do so undermines social legitimacy and program effectiveness. In this paper, we describe how adaptive management of e-flows allows an opportunity to incorporate a diversity of stakeholder views through an iterative process. However, to achieve this, stakeholder engagement must be intentionally integrated into the adaptive management cycle. Stakeholder engagement in e-flows allows for the creation of a shared understanding of a river and opens collaborative and innovative management strategies that address multiple axes of uncertainty. Here, we describe a holistic framework that unifies current participatory engagement attempts and existing technical methods into a complete strategy. The framework identifies the primary steps in an e-flows adaptive management cycle, describes potential roles of various stakeholders, and proposes potential engagement tools. Restructuring e-flows methods to adequately include stakeholders requires a shift from being driven by deliverables, such as reports and flow recommendations, to focusing on people-oriented outcomes, such as continuous learning and fostering relationships. While our work has been placed in the context of e-flows, the intentional integration of stakeholder engagement in adaptive management is pertinent to natural resources management generally.

The numerous environmental flows assessment methods that exist typically assume a stationary climate. Adaptive management is commonly put forward as the preferred approach for managing uncertainty and change in environmental flows. However, we contend that a simple adaptive management loop falls short of meeting the challenges posed by climate change. Rather, a fundamental rethink is required to ensure both the structure of environmental flows assessments, along with each individual technical element, actively acknowledges the multiple dimensions of change, variability and complexity in socio-ecological systems. This paper outlines how environmental flow assessments can explicitly address the uncertainty and change inherent in adaptively managing multiple values for management of environmental flows. While non-stationarity and uncertainty are well recognised in the climate literature, these have not been addressed within the structure of environmental flows methodologies. Here, we present an environmental flow assessment that is structured to explicitly consider future change and uncertainty in climate and socio-ecological values, by examining scenarios using ecological models. The environmental flow assessment methodology further supports adaptive management through the intentional integration of participatory approaches and the inclusion of diverse stakeholders. We present a case study to demonstrate the feasibility of this approach, highlighting how this methodology facilitates adaptive management. Rethinking our approach to environmental flows assessments is an important step in ensuring that environmental flows continue to work effectively as a management tool under climate change.

River floodplains are among the most dynamic and diverse ecosystems on the planet. They are at risk of degradation due to river regulation and climate change. Environmental water has been delivered to floodplains to maintain environmental health by mimicking natural floods. It is important to understand the long-term effects of environmental water to floodplain vegetation to support its management. This study used Normalized Differences Vegetation index (NDVI) from the 30-year Landsat datasets of the Hattah Lakes floodplain in Australia to investigate the drivers of vegetation dynamics. We developed generalized additive mixed models (GAMM) to model responses of vegetation to environmental water, natural floods, precipitation, temperature, and distance to water across multiple spatial and temporal scales. We found the effect of environmental water on floodplain vegetation to be quite different from that of natural floods in both space and time. Vegetation in most areas of Hattah Lakes will respond to natural floods within one month of flooding, while positive responses to environmental water occur 1 to 3 months after inundation and are more restricted spatially. For environmental water planning, managers need to be aware of these differences. The implementation of new infrastructure to transport or retain environmental water on floodplains needs to be planned carefully, with continuous monitoring of rainfall and natural floods. Whilst environmental floods do not mimic the effect of natural floods, they do provide some positive benefits that can partially offset effects of reduced natural floods.

Climate change presents severe risks for the implementation and success of environmental flows worldwide. Current environmental flow assessments tend to assume climate stationarity, so there is an urgent need for robust environmental flow programs that allow adaptation to changing flow regimes due to climate change. Designing and implementing robust environmental flow programs means ensuring environmental objectives are achieved under a range of uncertain, but plausible climate futures. We apply stress testing concepts previously adopted in water supply management to environmental flows at a catchment scale. We do this by exploring vulnerabilities in different river management metrics for current environmental flow arrangements in the Goulburn River, Australia, under non-stationary climatic conditions. Given the limitations of current environmental flows in supporting ecological outcomes under climate change, we tested three different adaptation options individually and in combination. Stress testing adaptation results showed that increasing environmental entitlements yielded the largest benefits in drier climate futures, whereas relaxing river capacity constraints (allowing more targeted delivery of environmental water) offered more benefits for current and wetter climates. Combining both these options led to greater than additive improvements in allocation reliability and reductions in environmental water shortfalls, and these improvements were achieved across a wider range of climatic conditions than possible with either of the individual options. However, adaptation may present additional risks to some ecological outcomes for wetter climates. Ultimately, there was a degree of plausible climate change beyond which none of the adaptation options considered were effective at improving ecological outcomes. This study demonstrates an important step for environmental flow assessments: evaluating the feasibility of environmental outcomes under climate change, and the i

Many freshwater ecosystems are in decline because of anthropogenic disturbance including climate change, yet our understanding of ecological vulnerability to future conditions including climatic variation is limited. Understanding climate risks to freshwater ecosystems requires combining hydrological and ecological knowledge. While there have been significant advances in ecohydrological approaches when applied within the large array of methods available for undertaking impact assessments, the ecological and hydrological elements are often not well‐integrated. This results in a mismatch in their ability to accommodate the inherent uncertainty in both impacts and responses. We examine published literature that assesses climate change impacts on freshwater ecosystems using both hydrological and ecological models to better understand method choices. We identify four fundamentally distinct modeling approaches used to assess climate change risk. We discuss which approaches are less useful for predicting ecological impacts under climate change, and highlight approaches of comparable complexity that can maximize the utility of dynamic, process‐based modeling while capturing the effects of climate uncertainty and variability. Using an illustrative case study of riparian vegetation health under climate change, we show how the four alternate modeling approaches feature different degrees of information in their outcomes and inferences about future risk. Most current studies that examine climate change risks to freshwater ecosystems use simplified methods or inadequately combine key elements. However, unless the interactions between changing hydrologic variability and ecological responses are explicitly captured in scale‐sensitive modeling methods, the risks of climate change to freshwater ecosystems will likely be substantially misrepresented, with negative consequences for effective management responses. Capturing these interactions requires combining ecological and h

Riverine ecosystems provide important ecosystem services reflecting their unique forms and functions. While the effects of stressors such as land cover change, climate change and growing economies on riverine ecosystem services (RES) have been well researched, the effect of the structure of the river network itself is less understood. This paper compares the capacity of different river network topologies in the delivery of selected RES. For three contrasting synthetic river network topologies (Long Trellis Narrow; Coastal Dendritic; Inland Dendritic), we applied simple functional equations to model six RES: water supply, hydropower generation, sediment retention, nutrient uptake, flood attenuation and aquatic habitat provision. Results showed that the synthetic topologies deliver different levels of RES, driven by their differences in physical structure. For example, the Inland Dendritic network removed more nitrate and better attenuated flooding due to its relatively longer lower reaches but offered poorer prospects for water supply because the longer reaches were more susceptible to transmission losses (e.g., due to bed seepage). This study provides a valuable first step in understanding the effect of river network topology on RES delivery, and the relative strengths among network types. Understanding these effects can aid decision makers in the conservation and restoration of degraded river basins to preserve RES for future generations.

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.

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

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.

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 suggest that a functional flows approach, focusing on elements of the natural flow regime known to sustain important ecosystem processes, offers a pathway for linking understanding of ecosystem processes with discrete, quantifiable measures of the flow regime for a broad range of native taxa and assemblages. Functional flow components can be identified as distinct aspects of the annual hydrograph that support key biophysical processes, such as wet season flood flows or spring recession flows, and then quantified by flow metrics, such as 5% exceedance flow or daily percent decrease in flow, respectively. By selecting a discrete set of flow metrics that measure key functional flow components, the spatial and temporal complexity of flow regimes can be managed in a holistic manner supportive of multiple ecological processes and native aquatic species requirements. We provide an overview of the functional flows approach to selecting a defined set of flow metrics and illustrate its application in two seasonally variable stream systems. We further discuss how a functional flows approach can be utilized as a conceptual model both within and outside of existing environmental flow frameworks to guide consideration of ecological processes when designing prescribed flow regimes.

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 const

The Murray–Darling Basin in south‐eastern Australia contains over 70,000 km2 of wetlands and floodplains, many of which are in poor condition. In response, Australian governments have committed to a major restoration program, the Murray–Darling Basin Plan that includes management of 2,750 Gl of environmental water to protect and restore aquatic ecosystems. The restoration is being undertaken within an adaptive management framework that includes monitoring the outcomes of environmental flows in seven river valleys. This paper provides an overview of the 5‐year monitoring project and some preliminary results. Monitoring design considered the Basin Plan's environmental objectives, conceptual models of ecosystem responses to flow, and an outcomes framework linking flow responses to the environmental objectives. Monitoring indicators includes ecosystem type, vegetation, river metabolism, and fish. Responses are evaluated to identify the contribution of environmental flows to Basin Plan environmental objectives and continual improvements in management. The program is unique in that it seeks to monitor long‐term outcomes of environmental flows at the river basin scale. Despite many challenges, the monitoring has become a key part of the adaptive management of environmental flows in the Murray–Darling Basin.

Adaptive management is central to improving outcomes of environmental water delivery. The Australian Government's Murray−Darling Basin (MDB) Plan 2012 explicitly states that adaptive management should be applied in the planning, prioritisation and use of environmental water. A Long Term Intervention Monitoring (LTIM) program was established in 2014 to evaluate responses to environmental water delivery for seven Areas within the MDB, with evaluation also undertaken at the Basin scale. Adaptive management at the Area scale was assessed using two approaches: (a) through a reflective exercise undertaken by researchers, water managers and community members and (b) through an independent review and evaluation of the program, where relevant reports were reviewed and managers and researchers involved in the LTIM program were interviewed. Both assessment approaches revealed that the scale of management actions influenced the extent to which learnings were incorporated into subsequent actions. Although there were many examples where learnings within an Area had been used to adaptively manage subsequent environmental water deliveries within that Area, there was inconsistent documentation of the processes for incorporating learnings into decision making. Although this likely limited the sharing of learnings, there were also examples where learnings from one Area had influenced environmental water management in another, suggesting that sharing between concurrent projects can increase learning. The two assessments identified ways to improve and systematically document the adaptive management learnings. With improved processes to increase reflection, documentation and sharing of learnings across projects, there is an opportunity to improve management of environmental water and ecosystem outcomes.

Floods and droughts are key driving forces shaping aquatic ecosystems. Climate change may alter key attributes of these events and consequently health and distribution of aquatic species. Improved knowledge of biological responses to different types of floods and droughts in rivers should allow the better prediction of the ecological consequences of climate change‐induced flow alterations. This review highlights that in unmodified ecosystems, the intensity and direction of biological impacts of floods and droughts vary, but the overall consequence is an increase in biological diversity and ecosystem health. To predict the impact of climate change, metrics that allow the quantitative linking of physical disturbance attributes to the directions and intensities of biological impacts are needed. The link between habitat change and the character of biological response is provided by the frequency of occurrence of the river wave characteristic—that is the event's predictability. The severity of impacts of floods is largely related to the river wave amplitude (flood magnitude), while the impact of droughts is related to river wavelength (drought duration).

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.

Worldwide, fishways are increasingly criticized for failing to meet conservation goals. We argue that this is largely due to the dominance of diadromous species of the Northern Hemisphere (e.g., Salmonidae) in the research that underpins the concepts and methods of fishway science and management. With highly diverse life histories, swimming abilities and spatial ecologies, most freshwater fish species do not conform to the stereotype imposed by this framework. This is leading to a global proliferation of fishways that are often unsuitable for native species. The vast majority of fish populations do not undertake extensive migrations between clearly separated critical habitats, yet the movement of individuals and the genetic information they carry is critically important for population viability. We briefly review some of the latest advances in spatial ecological modelling for dendritic networks to better define what it means to achieve effective fish passage at a barrier. Through a combination of critical habitat assessment and the modelling of metapopulations, climate change-driven habitat shifts, and adaptive gene flow, we recommend a conceptual and methodological framework for fishway target-setting and monitoring suitable for a wide range of species. In the process, we raise a number of issues that should contribute to the ongoing debate about fish passage research and the design and monitoring of fishways.

Globally, surface water quality deterioration is an important issue exacerbated by increasing urbanisation, intensified agricultural activities and climate change. To mitigate this increase in waterway pollution there is a strong need for effective catchment management strategies. However, this is currently limited by: (1) our lack of understanding of the key processes and mechanisms driving water quality change in waterways, and (2) our inability to predict future water quality change. To address these, we need to improve our ability to understand and model water quality. This improved capacity will enable us to better identify important water quality changes under various land use, land management and climate scenarios. These improvements could thus assist waterway managers to better prioritise regions which require special management attention, as well as better assess the benefits of different management interventions and preventative actions to reduce pollution levels.

Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.

Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling 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). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemica

While the number of environmental flows and water science programmes continues to grow across the globe, there remains a critical need to better balance water availability in support of human and ecological needs and to recognise the environment as a legitimate user of water. In water‐stressed areas, this recognition has resulted in friction between water users in the public and private sectors. An opportunity exists for practitioners to be on the forefront of the science determining best practices for supporting environmental water regimes. This Special Issue brings together a collection of environmental flows science and water management papers organised around three major themes: (1) method development and testing; (2) application case studies; and (3) efficacy evaluation. Contents of this Special Issue are intended to foster collaboration and broaden transferability of the information, technical tools, models and methods needed to support environmental water management programmes. The technical sophistication of methods and modelling tools, while important to the advancement of environmental water science, may come at the expense of easily interpretable outcomes that positively influence management decisions. Researchers need to be more proactive in translating the results of advanced modelling methodologies into user‐friendly tools and methods. This will allow stakeholders and water managers to proactively test alternative water allocation scenarios to help address growing human water demands in the face of droughts and changes in climatic patterns. The application of environmental flows science and water management strategies cannot be done in isolation. Implementation involves a complex decision‐making process that integrates ecological, hydrologic and social science across diverse multifaceted governance systems and requires active stakeholder involvement. Scientists and managers must strengthen partnerships at multiple scales to develop sensible sc

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.

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.

The implementation of environmental flow regimes offers a promising means to protect and restore riverine, wetland and estuarine ecosystems, their critical environmental services and cultural/societal values. This Special Issue expands the scope of environmental flows and water science in theory and practice, offering 20 papers from academics, agency researchers and non‐governmental organisations, each with fresh perspectives on the science and management of environmental water allocations. Contributions confront the grand challenge for environmental flows and water management in the Anthropocene—the urgent need for innovations that will help to sustain the innate resilience of social–ecological systems under dynamic and uncertain environmental and societal futures. Basin‐scale and regional assessments of flow requirements mark a necessary advance in environmental water science in the face of rapid changes in water‐resource management activities worldwide (e.g. increases in dams, diversions, retention and reuse). Techniques for regional‐scale hydrological and ecohydrological modelling support ecological risk assessment and identification of priority flow management and river restoration actions. Changing flood–drought cycles, long‐term climatic shifts and associated effects on hydrological, thermal and water quality regimes add enormous uncertainty to the prediction of future ecological outcomes, regardless of environmental water allocations. An improved capacity to predict the trajectories of ecological change in rivers degraded by legacies of past impact interacting with current conditions and future climate change is essential. Otherwise, we risk unrealistic expectations from restoration of river and estuarine flow regimes. A more robust, dynamic and predictive approach to environmental water science is emerging. It encourages the measurement of process rates (e.g. birth rate, colonisation rate) and species traits (e.g. physiological requirements, morpho

This study uses water‐quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate/nitrite (NOx), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time‐averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site‐level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time‐averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water‐quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water‐quality responses to future land use, climate, or soil and land management changes.

Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.

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.

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. WIREs Water 2018, 5:e1260. doi: 10.1002/wat2.1260 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality

To support sound decision‐making in environmental management, we need rigorous, defensible, and transparent synthesis of scientific evidence. The Association for the Sciences of Limnology and Oceanography and associated aquatic science societies are leaders in applying science to decision‐making, and yet many environmental decisions are still at risk of having to be made without a comprehensive, well‐synthesized evidence base to support them. In this article, we discuss two synergistic approaches that can help science inform decision‐making: systematic review and evidence banking. Our aim is to promote the use of these approaches, and to enlist support and action from you, the aquatic science community. We propose that you can improve the use and uptake of science in decision‐making by making your research more compatible with synthesis efforts by: considering risk of bias when designing your study and reporting results; reporting all relevant contextual information; analyzing your data using standard effect size approaches; and publishing your raw data. Awareness of how primary research feeds into informing policies can help you broaden the impact of your research, making it more directly relevant to decision‐making and more likely to contribute to the protection of aquatic ecosystems.

Degraded water quality in rivers and streams can have large economical, societal and ecological impacts. Stream water quality can be highly variable both over space and time, so understanding and modelling these spatio-temporal variabilities is critical to developing management and mitigation strategies to improve riverine water quality. However, there is currently limited capacity to model stream water quality due to the lack of understanding of the key factors driving spatio-temporal variability in water quality. To address this, a Bayesian hierarchical statistical model has been developed to describe the spatio-temporal variability in stream water quality across multiple catchments in the state of Victoria, Australia. We used monthly water quality monitoring data collected at 102 sites over 20 years. The modelling focused on three key water quality indicators: total suspended solids (TSS), nitrate-nitrite (NOx) and salinity (EC). It was found that both human-influenced catchment characteristics (land use) and other natural characteristics such as climate or topography are important drivers of spatial variabilities. The key drivers of temporal variability are changes in streamflow, climate and vegetation cover. These key drivers have been integrated into a spatio-temporal modelling framwork. These models can be applied at different spatial and temporal scales, and explain a reasonable proportion of spatio-temporal variation in the different water quality constituents. The extension and adaption of these models is currently underway to create an operational tool to forecast stream water quality responses to potential land use and climatic changes.

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 construction of fishways for upstream and downstream connectivity is the preferred mitigation measure for hydropower dams and other riverine barriers. Yet empirical evidence for effective design criteria for many species is missing. We therefore assembled a group of international fishway designers and combined their knowledge with available empirical data using a formal expert elicitation protocol and Bayesian networks. The expert elicitation method we use minimizes biases typically associated with such approaches. Demonstrating our application with a case‐study on the temperate Southern Hemisphere, we use the resulting probabilistic models to predict the following, given alternative design parameters: (i) the effectiveness of technical fishways for upstream movement of migratory fish; (ii) habitat quality in nature‐like bypasses for resident fish; and (iii) rates of mortality during downstream passage of all fish through turbines and spillways. The Fish Passage Network (Fish‐Net) predicts that fishways for native species could be near 0% or near 100% efficient depending on their design, suggesting great scope for adequate mitigation. Sensitivity analyses revealed the most important parameters as follows: (i) design of attraction and entrance features of technical fishways for upstream migration; (ii) habitat preferences of resident fish in nature‐like bypasses; and (iii) susceptibility of fish to barotrauma and blade strike during turbine passage. Numerical modelling predicted that mortality rates of small‐bodied fish (50–100 mm TL) due to blade strike may be higher for Kaplan than Francis turbines. Our findings can be used to support environmentally sustainable decisions in the planning, design and monitoring stages of hydropower development.