Future water demand modeling is of crucial importance for stakeholders, particularly in the era of rapidly changing climate and socioeconomic conditions. The modeling results can be applied to develop effective adaptation strategies that ensure equitable and sustainable allocation of water to various economic sectors, including institutional, commercial, industrial (ICI), residential and agricultural. However, a comprehensive review of existing future water demand modeling methods that consider both climatic and socioeconomic factors as well as the major economic sectors is currently lacking. This review article contributes to fill this knowledge gap while introducing a more streamlined and comprehensive methodological approach for conducting literature reviews in the environmental sciences domain. At the core of this method is a new framework designed to support research questions formulation and literature search strategies named STAR (Systems, Trouble/Treatment, Alternative, Response). In addition, it presents a data-requirement-based metric as well as a new nomenclature for classification of surveyed methods and approaches to guide the selection process of future water demand modeling methods. Furthermore, it proposes a hybrid modeling approach made up of three components (computational intelligence, dynamic systems and probabilistic scenarios) in the form of a theoretical workflow for future water demand modeling. The proposed workflow ensures broad applicability, making it adaptable not only to water demand management but also to a wide range of challenges across the environmental sciences.

Environmental modeling is a powerful tool for facilitating discussions among stakeholders involved in decision-making for environmental planning. This article explores how models can be used to structure stakeholder discussions by visualizing the opportunity space – the range of interventions that are deemed to be technically and physically feasible – and how it overlaps (or does not) with the decision space – the set of interventions considered acceptable or desirable by stakeholders. Using the case study of the Aa of Weerijs catchment in the Netherlands and Belgium, we demonstrate how different model contributions (sensitivity analysis, impact assessment and scenario evaluations at catchment and local levels) can aid these discussions by providing structured insights into potential interventions, both within the opportunity and decision spaces. The findings highlight how models can bridge gaps between stakeholders and technical experts by facilitating more effective discussions and better-informed decision-making processes in environmental planning.

A stakeholder structured engagement process at the Sustainable Water Infrastructure Management (SWIM) conference and workshop was held in December 2024. The participants identified critical current and future issues facing the water sector that are synthesized in this paper. In particular, they highlighted issues of water systems’ vulnerability and lack of resilience to hazards and stressors; inequities associated with water scarcity; and water quality problems – all affected by natural or man-made influences. The Smart One Water (S1W) vision was the baseline for the SWIM 2024 conference. This paper expands the S1W vision with a synthesis of the conference discussions about S1W-related fundamental concepts, practices and implementation barriers. It includes initial recommendations – based on a digital, data-focused, stakeholder-driven approach – with expert representatives of the public and private water supply sectors, academia, government and policymakers tasked to generate real-world adaptable ideas and practical solutions. Specifically, S1W envisions a future where water management and governance silos are eliminated to provide the necessary collaboration to enable efficient, resilient, affordable and equitable water access capable of adapting to a changing environment. This would be a future where communities govern collaboratively through integrated decision-making on policy, management and funding of natural and engineered water systems at the river basin scale.

Extreme weather events, combined with human-induced factors, such as expanding impervious surfaces and inadequate drainage infrastructure, are driving escalating urban flood risks worldwide. In this study, we present a novel spatiotemporal Long Short-Term Memory (LSTM)-based surrogate of the U.S. Environmental Protection Agency (EPA)’s Storm Water Management Model (SWMM) to predict maximum water depth and inflow at the asset level within urban drainage networks. The high-resolution SWMM model, encompassing the full network of conduits and manholes, was first calibrated and validated using U.S. Geological Survey (USGS) observations. The LSTM surrogate was then trained on data from 5,000 rainfall events across seven Annual Recurrence Intervals (ARIs) ranging from 1 to 100 years. The SWMM-LSTM surrogate model consistently achieves high predictive performance for both water depth and inflow, highlighting its robustness across diverse storm scenarios and ARI conditions. Hyperparameter optimization via grid search revealed task-specific configurations: larger hidden layers with moderate dropout improved water depth predictions, while deeper network architectures with minimal dropout optimized inflow forecasts. By providing rapid, computationally efficient predictions without compromising accuracy, the SWMM-LSTM surrogate offers a practical tool for real-time flood risk assessment, scenario evaluation and actionable decision-making in complex urban drainage systems.

Massive open online courses (MOOCs) have emerged as powerful educational tools for disseminating complex scientific knowledge. This study examines the RECONECT MOOC, an innovative online learning platform designed to educate stakeholders about nature-based solutions (NbS) for hydrometeorological risk reduction. Developed by a multidisciplinary team of international experts, the course drew on diverse professional and geographical perspectives, across a range of national and sectoral contexts. Its primary aim to bridge the knowledge gap in climate adaptation strategies across diverse professional sectors. The research investigated the effectiveness of interactive, multimedia-based learning approaches in communicating advanced NbS concepts. Using the Sharable Content Object Reference Model (SCORM) framework, the course integrated multiple learning methods, including reading, listening, inquiry-based learning, discussion forums and practical application exercises. The MOOC was divided into six modules and targeted local authorities, policymakers, academics, postgraduate students and other stakeholders interested in climate resilience. Findings revealed that 90% of participants gained additional NbS knowledge, 69% reported a shift in perception and 62% felt confident applying what they learned. Participants valued the interactive resources and flexible, self-paced format. Overall, the study demonstrates that MOOCs can effectively support scientific knowledge dissemination and strengthen understanding of complex environmental solutions.

Sustainable Development Goal 6.1 seeks universal access to safe drinking water for all by 2030, yet persistent disparities remain even in high-income countries. Indigenous, remote and small communities are disproportionately affected by poor drinking water quality, but comparable evidence to evaluate performance across communities is very limited due to inconsistent monitoring and reporting. To this end, we constructed a community-level meta-panel dataset of 839 communities (4,137 observations) across 4 Australian jurisdictions (Northern Territory, South Australia, Victoria and Western Australia) and Ontario, Canada, over the period 2018–2022. Drinking water quality was assessed using the Australian Drinking Water Guidelines and Canadian Boil Water Advisories. Logistic regression was employed to estimate the probability of accessing good-quality drinking water, with Indigenous status, remoteness, population size and socio-economic condition as key explanatory variables. Results reveal systematic disparities: Indigenous and very remote communities are statistically significantly less likely to have good-quality drinking water than non-Indigenous and regional communities after controlling for other factors. Our findings indicate that structural inequities – rather than geographic or demographic variation alone – are critical determinants of poor drinking water outcomes in small, Indigenous communities in both Australia and Canada.

Ensuring easy access to clean and safe drinking water using low-cost technology is essential to mitigate the rising water scarcity in emerging economies. Commercial large-scale desalination technologies need significant investment, making them unsuitable for off-grid and small-scale applications. However, this operation can be carried out using a low-cost desalination technology based on renewable energy, known as the solar still. In this research work, a modified basin solar still (basin solar still + internal mirrors + 8 kg gravel + black ink (400 ppm per litre)) was developed and experimentally tested in Visakhapatnam (17.68°N, 83.22°E), India, to determine its appropriateness for sustainable seawater desalination. It produced 14% to 23% more desalinated water than a conventional basin solar still. In addition, its thermal efficiency was between 41% and 42%, which was significantly greater than other basin solar stills reported in literature. In addition, high-quality desalinated water was generated at a cost that was around three times less than the drinking water offered at Indian Railways kiosks. Moreover, the ability to mitigate significant CO2 emissions while also addressing water scarcity demonstrated that the modified basin solar still continues to contribute effectively to the United Nations Sustainable Development Goal 6 (Clean Water and Sanitation).

This study investigates the use of machine learning based image classification techniques to detect debris blocking of urban waterways. Using a dataset comprising 1089 labelled CCTV images of a trash screen located in Cardiff, UK and a comprehensive re-sampling approach, we investigate not only the ability of selected machine learning algorithms to correctly identify images, but also to evaluate the uncertainty of these algorithms conditional on the datasets presented to them. For each candidate model, we considered two datasets: an imbalanced dataset and an under-sampled dataset. The results demonstrate that the performance of a simple logistic regression model was broadly comparable to that of more advanced machine learning models such as vision transformers. The best performing models (vision transformers and logistic regression) achieved an accuracy of more than 80%, while the NetRes50 model achieved an accuracy in the low 70%. This is an important result that opens the possibility for implementing these techniques as part of an operational real-time flood warning system utilising already existing cameras.

Although Canada possesses abundant freshwater resources, uneven water distribution, rapid climate change, deteriorating source water quality, and insufficient water infrastructure put small, rural, and remote (SRR) communities at a risk of water advisories and waterborne disease that is 26 times higher than in cities. Approximately three-quarters of SRR communities are Indigenous, indicating that they are more likely to experience water insecurity than non-Indigenous communities. This review examines key factors exacerbating water insecurity in SRR communities, including: (i) the types and ranges of commonly detected contaminants in source water, (ii) contaminant pathways and associated health risks, and (iii) the performance and challenges of small water systems (SWSs) in Canadian SRR communities. Total coliforms and E. coli in the microbiological category, and arsenic, lead, and manganese in the heavy metals and trace minerals category, had the highest number of occurrences among contaminants reported in dedicated studies. In contrast, fewer studies have investigated contaminants of emerging concern (CECs) and the capacity of SWSs to remove them. Common SWSs such as, multistage sand filtration, roughing filtration, granular activated carbon, chlorination, and ozonation offer relatively simple and affordable decentralized options; however, their long-term performance, operation, maintenance, governance, and social acceptability remain challenging.

The development of instrumentation, control and automation (ICA) in water operations during half a century is reviewed, and new challenges are described. The ideal ICA system contains a quality team of people who feel a deep sense of ownership of the system and who are committed to the continuous improvement ethics; an instrumentation system that gathers adequate process variable information; a monitoring system to gather data, process and display the data, detect and isolate measurement faults or process abnormal situations, assist in diagnosis and advice; a control system to meet the goals of the operation.

• • ICA is not one scientific discipline; it combines a multitude of scientific and engineering disciplines, here called a “decathlon” combination.

• • ICA is a hidden technology. It is ubiquitous in most industrial processes, including urban water systems, and reveals how processes are connected. When everything works as intended, it is not noted, but if things go wrong, it will be observed.

• • ICA in the water industry has about 50 years of history and is now well recognized.

• • Computers had become more affordable in the late 1960s. It was recognized that wastewater treatment systems are truly dynamic. All the 14 ICA conferences, from 1973 to 2025, have addressed all aspects of ICA methodology and implementations. The author has had the privilege to participate in all the 14 conferences.

• • Technology push and demand pull not only has led to more advanced operations. The rapid development of process knowledge, machine learning, AI, computing power and communication can realize operation also in a system-wide perspective.

• • There is an increasing demand for water reuse and circular management of water, and ICA has the potential to play an important role. Systems thinking, involving the complete urban water system cycle, is a great issue today. To succeed here, it is necessary to expand cooperation between problem owners, the water industry and methodology researchers in academia.

The revised European Union (EU) drinking water directive has imposed a stricter norm for lead in drinking water. This has necessitated a reassessment of current and future assets maintained by Dutch drinking water utilities (such as brass-containing appurtenances), primarily from the perspective of lead exposure. Should such components result in a considerable exposure, a consequent widespread rehabilitation would require a significant investment. To quantify exposure to lead from brass components in premise plumbing systems, insights from numerical modeling and laboratory experiments are combined. An important insight is that a lead leaching component present in the kitchen tap poses a much higher exposure in contrast to it being present in the service line. This underlines the need to localize lead leaching components – where is it and who needs to take action to remove it. Subsequently, the robustness and vulnerabilities of profile sampling (a lengthy flushing followed by prolonged stagnation followed by collection of a series of water samples) in localizing lead leaching components are explored. This study has led collectively to two major conclusions for the Dutch drinking water sector: (i) there is no need for accelerated rehabilitation of brass-containing appurtenances such as water meters and shutoff valves and (ii) profile sampling is to be deployed upon measuring norm exceedance.

Riverine ecosystems face interacting environmental, climatic and human pressures that threaten freshwater sustainability. This study assessed the socio-ecological drivers, causal interactions and governance dynamics shaping freshwater vulnerability in Malawi’s Bua River Basin using integrated Drivers–Pressures–State–Impact–Response (DPSIR) and Socio-Ecological Systems (SES) frameworks. Physicochemical water-quality measurements across spatial and seasonal gradients were combined with qualitative field observations. Factorial ANOVA revealed significant seasonal and physicochemical variability (F₂,₅₂ = 21.84, p < 0.0001), with season–parameter interactions showing that hydrological variability affected indicators differently. Weak spatial interactions suggested catchment-wide influences. Key changes included elevated turbidity, riparian vegetation loss, exposed riverbanks and sediment mobilisation, driven by agricultural expansion, domestic activities and hydrological variability. The integrated analysis identified reinforcing feedbacks among livelihood dependence, hydropower-related problems, weak institutional coordination and declining freshwater resilience. Trade-offs emerged where intensified agriculture and freshwater-resource utilisation supported short-term livelihoods while accelerating ecological degradation, whereas riparian protection and local stewardship practices demonstrated potential resilience-supporting synergies. Overall, the findings show that freshwater degradation within the Bua River Basin is driven by interacting hydrological, ecological and socio-economic pressures, highlighting the value of integrated DPSIR and SES approaches for understanding freshwater vulnerability and guiding adaptive governance in tropical river systems.

England’s water system is under intensifying pressure from scarcity, flooding, pollution, biodiversity loss and climate change, yet the prevailing model of water cycle management remains fragmented, resource-intensive and too often focused on managing symptoms rather than causes. This paper outlines an approach to integrated water cycle management that aligns with the ideas set out by Kate Raworth, in her book “Doughnut economics: seven ways to think like a 21st century economist”. Raworth envisions a just and safe space for society where our economies are geared to ensuring that everyone has the essential resources required to fulfil their human rights but resource use is limited to a level that can be sustained within the limits imposed by "ecological ceilings". This aligns with the research by Johan Rockström and his colleagues at Stockholm Resilience Centre who have developed the concept of nine “Earth system boundaries”, the exceedance of which risks triggering large-scale, abrupt or irreversible environmental change. By reframing water management in England as the stewardship of social-ecological systems to deliver human wellbeing within environmental limits, this paper supports policy momentum created by the Cunliffe Review and the Government’s New Vision for Water, while pushing further towards a genuinely integrated, catchment-based and cross-sectoral approach. Its practical contribution is to translate doughnut thinking into the water context through clear operating principles, measurable outcomes and indicative targets. Doughnut economics, applied within the context of a whole water cycle approach to regulation and governance, has potential to centre human needs (affordability, reliability, amenity) and planetary limits (spills, nutrients, flows, biodiversity) within a single, practical framework. At a time when England needs both renewed public legitimacy and more effective delivery, the paper offers a strategic framework for achieving a safe and just water future that restores rivers, reduces demand, improves resilience and enables prosperity within planetary boundaries.