Estuarine water quality is shaped by complex interactions between hydrodynamics, climate, and catchment inputs, making accurate prediction challenging. This study develops machine learning models to predict chlorophyll-a (chl a), total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS) in the Gold Coast Broadwater, a subtropical micro-tidal estuarine lagoon in Australia. Using a six-year dataset (2016-2021) from 18 monitoring sites, five algorithms were trained and tested, with Random Forest delivering the highest and most consistent predictive performance across all variables (chl a: R2 = 0.60, RMSE = 1.07 μg/L; TN: R2 = 0.72, RMSE = 0.043 mg/L; TP: R2 = 0.80, RMSE = 0.004 mg/L; TSS: R2 = 0.67, RMSE = 3.26 mg/L). Scenario-based simulations reveal that precipitation and temperature are more strongly associated with chl a dynamics than nutrient enrichment alone, while TN, TP, and TSS show pronounced sensitivity to spatial variability in rainfall. These results highlight the potential importance of climate-driven processes in shaping estuarine water quality responses under the conditions evaluated. The study provides a novel framework that integrates data-driven prediction with observation-based modelling, spatial mapping, and scenario analysis. Together, these components enable high-resolution environmental predictions and provide a practical tool for adaptive water-quality management in dynamic estuarine systems.

Coral reef ecosystems, including Australia's Great Barrier Reef, face intensifying pressure from diffuse agricultural pollution, particularly nutrient runoff. While emerging agricultural innovations, such as microalgae-based soil probiotics, offer potential to enhance nutrient-use efficiency and reduce environmental impacts, their large-scale adoption remains limited. This gap reflects not only technical or economic barriers, but also the complex socio-ecological systems in which such innovations are embedded. In this Perspective, we argue that a systems thinking approach is essential to understand and overcome the barriers to scaling agricultural innovations for reef protection. Integrating insights from soil microbiome engineering and interdisciplinary research, we show how systems thinking can serve as an analytical lens to understand adoption delays, ecological risks, and economic lock-ins, supporting transition toward long-term resilience in both farming systems and reef ecosystems.

Significant cyanobacterial proliferations dominated by Lyngbya have been increasingly reported since the 2000s, posing environmental, economic, and human-health risks. This review synthesizes their distribution, predictors, toxicity, and management strategies of all identified Lyngbya species, including species historically classified as Lyngbya despite later taxonomic changes. Research has focused mainly on Lyngbya majuscula and Lyngbya wollei. For L. majuscula, bloom initiation is driven by proximate abiotic factors such as nutrients, light, and temperature; while broader conditions, including bottom currents, sediment nutrients, rainfall, and land use, set the stage for proliferation. Toxin production appears related to nutrient levels and temperature, although mechanisms remain poorly understood. Management of L. wollei commonly relies on copper-based chelated algaecides, despite their risks to non-target organisms, highlighting the need for more sustainable tools used in managing other cyanobacteria. Existing predictive models for Lyngbya proliferation show limited accuracy, partly due to insufficient in situ data. This review argues that novel monitoring approaches could provide the data needed to strengthen predictive models, also offering insights into a new modeling approach, supporting more proactive and effective Lyngbya bloom management. It is particularly valuable for research in water resource management and environmental science, as it synthesizes current knowledge essential for advancing management strategies.

Human-driven nutrient enrichment is accelerating the spread of invasive aquatic macrophytes, generating substantial ecological and socio-economic impacts in freshwater ecosystems, including biodiversity loss, deterioration of drinking water quality, reduced fisheries productivity, constraints on recreational use as well as impaired waterborne transport. Consequently, the management of invasive aquatic weeds is now widely regarded as a priority for the conservation and sustainable use of freshwater lakes.This study critically reviews the main control strategies currently adopted to limit the expansion of highly invasive species such as Salvinia molesta, Eichhornia crassipes, Egeria densa, Pistia stratiotes, and Elodea nuttallii. Starting from 30,659 academic and grey literature articles matching our search criteria across multiple browsers (i.e. Google Scholar, ProQuest, Web of Science, and Scopus), we critically analysed 155 fully relevant articles, focusing on lake morphology, infestation details (year of detection and species involved), strategy characteristics and their effectiveness (reduction in surface coverage and containment in the event of reappearance), as well as the qualitative and quantitative advantages and disadvantages of each method.Our work also examines the global distribution of such management practices and integrates satellite-based remote sensing data to quantify macrophyte surface coverage in lake environments pre‑ and post‑control. Our results to date have identified three dominant approaches: mechanical removal (in 15.6% of the cases), chemical herbicide application (19.5%), and biological control (28.6%), alongside integrated management combining the former approaches (28.6%) and other treatments (7.8%).Mechanical harvesting and chemical treatments can rapidly reduce biomass, yet their long-term application is often constrained by high operational costs and, in the case of herbicides, potential environmental risks. Biological control, typically involving specialist insects or herbivorous fish, appears to offer a more sustainable and self-maintaining option (with a recurrence rate of 11.4% of the cases, compared with 33.3% for chemical approaches and 41.7% for mechanical treatments), although its effectiveness depends on predator–prey specificity and the suitability of local climatic conditions.In terms of geographical distribution, the case studies were unevenly distributed, with 40.8% located in North America (which shows a predominance of chemical treatment accounting for 36.7% of the total, particularly in the United States), 25.0% in Africa (where 70.0% of the cases involved biocontrol), and a smaller share in Oceania and Asia (representing 20.0% and 10.8% of the total, respectively), with an even smaller proportion in Europe and South America.By comparing the strengths, limitations, and context-dependent requirements of each method, this study supports the selection of appropriate management strategies for future case studies, taking into account ecological characteristics, invasion dynamics, geographic setting, and available economic resources.

The eutrophication of coastal waters is a primary environmental concern worldwide, with severe implications for marine biodiversity and ecosystem services. Effective monitoring and assessment of eutrophication are crucial for the sustainable management of coastal zones. However, traditional methods often fail in terms of accuracy and scalability. This study introduces a novel integrated methodology for evaluating eutrophication by combining statistical modeling, trophic indices, and Geographic Information System (GIS) tools. This methodology was applied over 12 months to assess the eutrophication status of Saïdia Bay, Morocco, a region of significant ecological and economic importance. Monthly water samples were collected from eight stations, and 13 water quality parameters were measured, including chlorophyll a, nutrients, and dissolved oxygen. Statistical techniques such as Principal Component Analysis, Cluster Analysis), and Factor Analysis were employed to identify the primary water quality parameters and categorize the stations based on pollution levels. Trophic indices, including the Eutrophication Index (EI), Carlson Trophic State Index (TSI), and Trophic Index (TRIX), were calculated and visualized using GIS-based modeling. The findings revealed that Saïdia Bay is currently in “Good” ecological condition according to the EI, with both Carlson TSI (< 47) and TRIX (2–4) classifying it as “Oligotrophic,” with seasonal variations reflecting localized mesotrophic conditions in autumn. This integrated approach represents a significant advancement in coastal water quality monitoring, provides a robust framework for assessing eutrophication, and supports targeted management strategies. This methodology is also transferable, offering a scalable model for eutrophication assessments in coastal regions worldwide.

Agricultural water trading is typically considered an effective water management mechanism, and decisions made by agricultural agents highly influence its effectiveness. Agent-based modelling (ABM) simulating agricultural agents in the water trading context has drawn attention due to its distinguishable features driven by interactions, heterogeneity, independence, and the evolving characteristics of the decisions of agents. Given its strengths and potential to simulate a complex water trading system, the objectives of this study are to (a) provide a comprehensive review of the status of ABM applications in agricultural water trading through a systematic review and (b) identify the primary trends of the empirical nature of ABM studies, approaches to modelling agricultural agent decisions, uncertainty assessments, and validation approaches in ABM studies. The results show that there is a relationship between the empirical nature of the ABM studies, selected decision models to describe agricultural agents, analysed uncertainties, and the validation approaches employed in ABM studies. This study also provides a future research agenda, including exploring attributes with a direct influence on agent trading decisions and integrating the effects of uncertain trading decisions, long-term water availability changes, and water quality into ABM outcomes.

Climate change is altering hydrology, land cover, and biogeochemistry in Alpine river systems, yet predictive understanding of dissolved organic carbon (DOC) and total suspended solids (TSS), across glacierised and lowland catchments remains limited. This knowledge gap constrains our ability to forecast impacts on carbon cycling and sediment management. We present a data-driven predictive model for Swiss streams from diverse catchments, spanning glacierised high-mountain basins to lowland agricultural and forested systems. The machine learning framework incorporates discharge, water quality, and land use and land cover changes to predict DOC and TSS, with high accuracy (RMSE=14% of standard deviation for DOC) following validation of the best performing algorithm. While its reliance on routinely measured parameters makes it adaptable for near real-time forecasting, the model was designed for climate change scenario analysis. Projections indicate that by 2090, under RCP8.5, DOC exports will rise by ∼50% in high-mountain catchments and ∼15% in lowland systems, primarily driven by discharge, not by land cover change. TSS responses vary seasonally and by catchment, with increases in many glacierised basins and decreases in most lowland streams. Seasonal DOC load peaks are projected to occur earlier in the year. By harmonising diverse datasets and quantifying site-specific climate, hydrology and land cover interactions, this approach provides a tool for managing carbon and sediment fluxes in rapidly changing Alpine environments.

Understanding the drivers of harmful cyanobacterial blooms is critical for safeguarding freshwater quality, particularly in reservoirs that serve as drinking water sources. This study presents a comparative, cross-continental analysis of five lakes and reservoirs located in distinct climatic and ecological regions (i.e., Myponga, Wivenhoe, Tingalpa - Australia; Suwa - Japan; and Kinneret - Israel), focusing on Microcystis spp. and co-occurring cyanobacteria. By applying Self-Organizing Maps, Regression Trees, and Principal Component Analysis, we identified key physicochemical and ecological factors influencing Microcystis dominance across these systems. Total phosphorus (TP) emerged as the most consistent predictor of Microcystis dominance, while nitrogen forms and processes such as internal phosphorus loading also played important roles in some sites. Although TP reflects phosphorus present in the water column, internal loading can drive short-term TP increases, highlighting the need to consider both external inputs and sediment release when interpreting bloom drivers. Temperature and water column stratification effects varied across sites, i.e., strong in some systems (e.g., Myponga, Tingalpa) but weak or inconsistent in others (e.g., Suwa, Wivenhoe, Kinneret). Genus-level dynamics indicated that Microcystis, Dolichospermum, and Aphanizomenon often dominated in isolation, leading to low community richness and higher biomass. In contrast, genera like Pseudanabaena, Planktolyngbya, and Raphidiopsis co-occurred more frequently under stratified conditions, forming more diverse but less biomass-dense blooms. Our findings offer valuable guidance for water resource managers by identifying both universal and lake-specific drivers of Microcystis dominance. While the strong influence of phosphorus reinforces the importance of nutrient reduction strategies, the role of interacting factors, such as nitrogen forms, thermal dynamics, and inter-genus competition, demonstrates the necessity of site-specific management approaches. This harmonized, multi-lake assessment provides a rare ecological framework that can inform predictive modelling and effective management strategies.

Ensuring the quality of recreational waters is critical for safeguarding public health and supporting tourism-driven economies. However, rising levels of Enterococci (ENT) present significant risks to aquatic ecosystems and human well-being. Predicting ENT concentrations and understanding their environmental and anthropogenic drivers are essential for effective water resource management and the mitigation of health risks. This systematic review explores the existing body of research on water quality modeling by analyzing various model types, their applications, and their effectiveness. It identifies rainfall and storms as primary drivers of elevated ENT concentrations, emphasizing the critical role of environmental factors in shaping water quality. Additionally, human and animal waste, particularly from sewage intrusion, are highlighted as significant sources of ENT, underscoring the need to address anthropogenic impacts on water contamination. Process-based and data-driven models emerge as prominent tools for forecasting ENT levels in recreational waters. While both approaches are widely utilized, the review notes the difficulty in directly comparing their performance due to methodological variations. By synthesizing findings from diverse studies, the review provides insights into the complex relationships between predictors such as rainfall, ENT levels, and associated health risks from human exposure. The review also addresses the health implications of ENT contamination by identifying its primary sources and associated diseases, enhancing understanding of its broader impacts on public health. Furthermore, it offers evidence-based recommendations for selecting appropriate models to predict ENT levels, empowering researchers and water resource managers to design more effective water quality management strategies. These insights may contribute to reducing the prevalence of waterborne diseases associated with recreational water use, ultimately promoting safer and more sustainable aquatic environments.

This paper proposes an adaptive nested robust multi-objective optimisation approach to support integrated energy-water system planning decision-making under uncertainty for sustainable urban precinct-scale infrastructure development. The proposed framework aims to enhance overall energy-water system performance by attaining desirable conflicting technoeconomic and environmental objectives, while strictly satisfying hard coupling nodal and operational constraints. Subject to these imposed constraints, the defined objectives ensure system reliability, balance cost-effectiveness, as well as promote resource sustainability. By exploiting the problem structure with nested energy-water system decisions, the proposed methodology derives robust counterparts of separable deterministic uncertain problems as mixed-integer disciplined convex programs. These optimisation programs address resource assignment and asset allocation as well as unit commitment, thereby tackling them for energy-water system capacity sizing and operation scheduling over the planning horizon, while incorporating adaptive recourse actions taken to handle uncertainties in realistic prospective scenarios. To validate the applicability of this approach, a decentralised energy-water distribution network was examined within a community microgrid as a precinct-scale case study. This evaluation determines integrated energy-water system plans by comparing the proposed method with existing related linear models and usual conservative formulations. The numerical results indicate that this mathematical optimisation modelling approach for integrated system planning can significantly enhance overall performance of using distributed energy-water resources, supporting decision intelligence to more beneficial investment strategies adopted by facility custodians or site managers in urban precincts. The findings demonstrate substantial energy-water resource savings (up to 18.2%), reduced overall system costs (up to 21.25%), and lower carbon emissions (up to 10.0%). Furthermore, the applied operations research methods facilitate greater penetration of renewable energy sources while harvesting utilisable nonpotable water sources. These methodological and practical advances contribute to the digital multi-utility transformation, fostering long-term and short-term economic as well as environmental benefits for sustainable development of resilient urban precinct-scale infrastructure.

The potential of fluorescent dissolved organic matter (fDOM) signal was investigated for online coagulant dose control at drinking water treatment plants (DWTPs). This included 1) development of an fDOM signal-based model for coagulant dose prediction, 2) model assessment and beta testing trials at six DWTPs, and 3) assessment of real-world deployment of the new model at a DWTP. Jar test experiments were conducted using water from 15 water sources across Australia to generate a dataset for development of the model. Model assigned enhanced alum doses (EnD) removed about 85 % of the coagulable fraction, or similar to 53 % of the total DOM, measured as fDOM. These jar test results served as a foundation for developing an fDOM signal-based model applicable to a wide range of raw water sources with varying DOM concentrations and characteristics. The fDOM model predictions for EnD were benchmarked against the jar test results and a previously established model using offline UV-Vis-based input data. Beta testing and field deployment trials demonstrated an average correlation coefficient of 0.98 between the online fDOM and offline mEnCo model predictions at six study sites. The fDOM model's application demonstrated potential for rapidly adjusting coagulant dosing in response to sudden, significant changes in water quality. This may enable improved cost-efficiencies in coagulant use and provision of consistent high quality treated waters where raw waters are variable in quality, potentially reducing health related risks. The findings of this study underscore the potential of the fDOM signal-based model for real-time coagulant dosing control at DWTPs.

Ensuring access to safe drinking water is a fundamental public health priority, yet the growing diversity of contaminants demands more human-relevant toxicity assessment frameworks. Conventional models based on immortalized cell lines or sentinel species, while informative, lack the tissue complexity and inter-individual variability required to capture realistic human responses. Organoids, three-dimensional epithelial structures derived from adult or pluripotent stem cells, retain the genomic, histological, and functional characteristics of their original tissue, enabling assessment of contaminant-induced toxicity, short-term peak exposures, and inter-donor variability within a single system. This study examined whether current international drinking water guidelines remain protective or if recent organoid-based findings reveal toxicity at differing concentrations. Comparative synthesis indicates that per- and polyfluoroalkyl substances (PFAS) often display organoid toxicity at concentrations above current thresholds, suggesting conservative guidelines, whereas most metals are properly regulated. However, some metals exhibit toxicity at concentrations that include levels below guideline values, highlighting the need for further investigation. Emerging contaminants, including pesticides, nanoparticles, microplastics, and endocrine disruptors, induce adverse effects at environmentally relevant concentrations, despite limited or absent regulatory limits. Integrating organoid-based toxicology with high-frequency monitoring and dynamic exposure modeling could refine water quality guidelines and support adaptive regulatory frameworks that better reflect real-world exposure patterns and human diversity.

The design of submerged breakwaters for coastal protection requires many iterations of process-based simulations to determine combinations of input variables' influence on the consequent shoreline response. By using machine learning, the complex non-linear multidimensional relation between input and modelled shoreline output can be modelled to predict shoreline change on unseen combinations of inputs with much faster simulations. To prove this, a neural network and Gaussian process regressor were trained on a dataset of 243 combinations of structural and wave forcing input parameters paired with their process-based modelled shoreline change output on a straight shoreline after one year. The models were capable of fitting the dataset of input-output relations comprising outputs of accretion and erosion, allowing for predictions on incremental input changes. The proposed hybrid modelling approach takes advantage of the computational advantages of machine learning to leverage physics-driven output data generated from process-based models to optimise the design options of submerged breakwaters relative to shoreline outcomes. Given the multidimensional multiparameter processes, the solution space is large, however, by focusing on key parameters of interest, resourceintensive processes can be optimised to collect high-quality data for important site parameters and using this machine learning method to develop a shoreline change model.

Traditionally, energy and water planning for new building and retrofit projects are undertaken almost always independently at the precinct scale, often overlooking possible synergies of integrated resource management. This paper addresses major knowledge and research gaps in integrated precinct-scale energy-water system planning. By employing a robust multi-objective optimisation approach, the proposed framework reduces resource outputs, system costs, and carbon emissions in urban precinct-built environments. In this engineering domain, the optimisation problem is formulated as a separable mixed-integer disciplined convex program, tackling critical energy-water system capacity sizing and operation scheduling tasks. By decomposing the posed problem into two exploitable substructures nested over the planning horizon, the energy-water system integration model supports decision-making under uncertainty for sustainable infrastructure development. Simulation results from a hypothetical case study demonstrate the model applicability to find solutions that reduce resource outputs (up to 18.2%), system costs (up to 18.3%) and carbon emissions (up to 10.0%). These synergistic savings increased renewable energy penetration and harvested nonpotable water, fostering beneficial investments by facility custodians or site managers. The developed model enables resilient optimised energy-water system integration, distributed alternative resource assignment to modern buildings and controllable infrastructure asset allocation across decentralised facilities in local energy-water distribution networks.

The health and resilience of the Great Barrier Reef ecosystems are affected by complex dynamics and interconnected processes. A systems thinking approach can aid to comprehensively understand the past, current and future system behaviour, thereby informing effective decision-making. A qualitative conceptual model was developed based on the theory of systems thinking approach and critical literature reviews, giving consideration to the complex non-linear feedbacks that determine the structure and behaviour of the system. The model indicates that the feedback structure of the system is governed by agricultural production, pesticide applications, pesticide residues, water quality improvement policies and climate change. The findings highlight the inherent challenges of achieving water quality goals in a complex and dynamic environment, constantly influenced by many factors (e.g., climate change). This underscores the critical need for ongoing research, adaptation and a shift towards a systems perspective, enabling decision makers to avoid the unintended consequences emerging from linear thinking, thereby initiating more flexible and adaptive management strategies for saving this iconic system.

The dynamics of dissolved organic matter (DOM) in two river waters were investigated after their catchments had been severely burnt in the 2019/2020 Australian wildfires. Shortly after these wildfires, dissolved organic carbon (DOC) concentrations were recorded at high levels (similar to 19 and 30 mg/L) and these became much lower (up to similar to 80% less) in the following winter when river flows had increased. Satellite imagery-based data indicated up to 95% of catchment areas burnt and up to similar to 50% subsequent vegetation recoveries after 2 years. Shifts in burn index values for the burnt areas coincided with DOC concentration variations. The specific colour of waters increased up to 40% as daily river flows increased, indicating higher input of humic content from the burnt catchments. Chlorophyll a was detected at the highest levels in the waters soon after the fires when river flows were lowest. Enhanced alum doses were predicted using two feed-forward models; one based on DOC and turbidity data and the other based on UV@254 nm, colour, and turbidity. The doses predicted using the two models showed high correlations (r > 0.9) and were highest for waters directly after the fires. These models were developed for diverse source waters including those impacted by extreme climate events.

The Royal Society of Chemistry is the world's

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

Addressing the Sustainable Development Goals (SDGs) has become of paramount importance for higher education institutions; however, a lack of a transparent, transferrable approach to identifying and quantifying the extent of SDG contributions hinders the ability to benchmark against past performance or other institutions. In this study, a four-level, quantitative SDG contribution classification system was developed and applied to a Bachelor of Architectural Design program of an Australian university, by analysing all learning contents and assessment tasks for all the course of the Program. Results show that overall, the highest contributions were towards targets from SDG11 and SDG7, though most SDGs were addressed at some level. Contributions were mainly associated to lower cognitive learning levels, though there was a shift to higher-level (e.g. applications) contributions towards the last year of the program. Results also show that convenor-driven contributions, especially for some specific SDGs, can be significant, though in most cases SDG contributions were deemed part of core assessment/learning contents. The proposed approach provides a numerical assessment of SDGs contributions of a university-level program, which could be refined in the future and used more systematically by other institutions to benchmark and track progress towards achieving these goals. HIGHLIGHTS Quantitative SDG contribution approach for universities programs Applied to a Bachelor in Architectural Design Highest contributions towards SDG 11 and SDG 7 Convenor- vs course-driven contributions assessed

IntroductionGroundwater in the Middle East and North Africa region is a critical component of the water supply budget due to a (semi-)arid climate and hence limited surface water resources. Despite the significance, factors affecting the groundwater balance and overall sustainability of the resource are often poorly understood. This often includes recharge and discharge characteristics, groundwater extraction and impacts of climate change. The present study investigates the groundwater balance in the Dead Sea Basin aquifer in Jordan using a groundwater flow model developed using the MODFLOW.MethodsThe study aimed to simulate groundwater balance components and their effect on estimation of the aquifer's safe yield, and to also undertake a preliminary analysis of the impact of climate change on groundwater levels in the aquifer. Model calibration and predictive analysis was undertaken using a probabilistic modeling workflow. Spatially heterogeneous groundwater recharge for the historical period was estimated as a function of rainfall by simultaneously calibrating the recharge and aquifer hydraulic property parameters.Results and discussionThe model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The effect of groundwater recharge and discharge components were evaluated in the context of estimation of safe yield of the aquifer. The average annual safe yield is estimated as ~8.0 mm corresponding to the 80% of the calibrated recharge value. Simulated groundwater levels matched well with the declining trends in observed water levels which are indicative of unsustainable use. Long-term simulation of groundwater levels indicated that current conditions would result in large drawdown in groundwater levels by the end of the century. Simulation of climate change scenarios using projected estimates of rainfall and evaporation indicates that climate change scenarios would further exacerbate groundwater levels by relatively small amounts. These findings highlight the need to simulate the groundwater balance to better understand the water availability and future sustainability.