Whilst much research focusses on challenges related to achieving SDG 6.1 (universal and equitable access to safe and affordable drinking water), there has been less attention to challenges of safe transport, storage and use of collected water. In particular, there are relatively few high-quality datasets quantifying the number and volume of water containers used by households for such purposes. This paper reports results from the application of machine learning (ML) techniques to a database of images of domestic water storage collected during 2022 as part of an initiative to improve water supply in southern Bangladesh. Because the number of different water container types was relatively small, it was possible to train an ML algorithm to identify water containers and estimate water storage with greater than 90% accuracy. These results have allowed the rapid creation of a unique high-quality, high-resolution dataset describing water storage quantitatively in a study community. This dataset includes data quantifying the number of vessels as well as their individual and aggregated water storage volumes. The paper discusses policy implications for the study location specifically before concluding with suggestions for the inclusion of this sort of analysis in ongoing studies of household and community scale water insecurity.

Understanding the factors that shape household water and energy use is essential for designing targeted conservation interventions that promote both sustainability and well-being. While studies in this area often rely on traditional “frequentist” statistical methods, which can struggle to capture the complex interdependencies among demographic, behavioural, psychological, and material influences. This paper introduces Bayesian network (BN) analysis as a novel and adaptable method with useful applications in water and energy studies and a wide variety of other social sciences. The paper offers a primer on how to conduct BN analysis, including underlying logic and range of choice of software platforms, before presenting a brief worked example based on the authors’ current research into household water and energy consumption in a UK city. The paper shows how Bayesian networks can generate valuable insights from relatively small and complex datasets, capture non-linear relationships, and support scenario-based reasoning, making them well-suited for exploratory studies, “what if?” scenario-testing and policy effectiveness review. The findings contribute to a more nuanced understanding of domestic water and energy consumption and offer a practical framework that can inform the design of targeted, evidence-based interventions to encourage sustainable water and energy use in households. We argue that there is much to be gained by proliferation of this analytical approach throughout the social sciences.

In areas without direct piped water access to homes, one of the main challenges in meeting Sustainable Development Goals (SDG) 6.1 (ensuring universal access to safe and sufficient drinking water) is the availability of containers that can securely store enough water for daily household use. This study presents an innovative approach for deriving valuable water storage information from images captured during standard household WASH (Water, Sanitation, and Hygiene) surveys in Southern Bangladesh. Given that the number and type of water containers is relatively fixed it was possible to train an AI algorithm (specifically object detector) to extract information about quantities of water stored and storage vessel types with high degree of accuracy (90%) and F-score (80%). These results have allowed creation of the first high-quality high-resolution dataset describing water storage quantitatively in a study community.

Progress toward safe water for all is predominantly tracked using directly observable, resource-based indicators, including primary water source and water collection travel time. There is growing interest in complementing these indicators with experiential data about water access, use, and reliability, but there is limited evidence about their value for evaluating water service interventions. We therefore compared findings from observable and experiential water measures that were used to evaluate the impact of two multilevel interventions among households in Nepal (n = 83) and Sierra Leone (n = 981). We used t -tests, chi-square tests, and multivariable models to determine whether drinking water services (classified using the Joint Monitoring Programme’s drinking water service ladder) and water insecurity experiences (measured using the Household Water Insecurity Experiences Scale) changed following intervention. Additionally, we assessed for potential differential impacts on water insecurity by sociodemographic characteristics to understand if any groups were being left behind. In both settings, access to at-least-basic drinking water services among sampled households increased, from 60.8% to 100% in Nepal and from 33.0% to 48.2% in Sierra Leone. The percentage of households experiencing moderate-to-high water insecurity declined from 18.3% to 1.4% in Nepal and from 66.3% to 24.8% in Sierra Leone. Affirmation and reported frequency of being unable to wash clothes due to water problems, worrying about water insufficiency, and feeling angry about one’s water situation decreased but remained salient issues in both sites. There were no observed differences in project impact on water insecurity by respondent gender or age. In Nepal, project impact varied by districts, suggesting opportunities to better tailor interventions to local needs. These findings provide empirical evidence that experiential data complement traditional resource-based indicators and offer actionable information to address water insecurity.

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. It is essential to explore how AI enhances understanding of legal frameworks and environmental management, while also examining how legal and environmental factors may limit AI's role in society. From a co-production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasize environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony. Although emerging AI approaches can be powerful tools for disaster management, they must be implemented with ethical considerations and safeguards to address concerns about bias, transparency, and privacy. The responsible execution of AI approaches, based on the dynamic interplay between AI, law, and environmental risk, promotes sustainable and equitable practices in data mining.

Background:The Water Insecurity Experiences Scales are validated tools for reliably and comparably assessing experiences with water access and use in low- and middle-income countries. Although theoretically applicable in high-income countries, their performance in these settings has not been assessed. This study therefore examined whether the Water Insecurity Experiences Scales function similarly in high-income countries, and if they generated measures comparable to those in low- and middle-income countries. Methods:We conducted cognitive interviews with 73 adults from 4 high-income countries (Bulgaria, the Netherlands, the United Kingdom, and the United States) to assess whether participants understood the items in the Individual Water Insecurity Experiences Scale as intended. We then used nationally representative Gallup World Poll data from two high-income countries (Australia, the United States) and three low- and middle-income countries (Bangladesh, Brazil, and Uganda) to evaluate internal consistency, unidimensionality, and measurement invariance (n = 4,928).Construct validity was assessed by testing hypothesized associations between water insecurity scores and wealth, household size, self-reported stress, and satisfaction with water quality within Australia and the United States. Results:Cognitive interviews revealed no major issues with item translation or comprehension, supporting construct equivalence. The prevalence of moderate-to-high water insecurity was low in Australia (3.7%) and the United States (1.0%). In both countries, the scale was internally consistent, conformed to the unidimensional structure, and demonstrated good model fit based on criteria established a priori. Configural and scalar measurement invariance were supported across all five countries. As for validity, water insecurity scores were associated with different sociodemographic characteristics (wealth, household size), self-reported stress, and satisfaction with water quality in the directions hypothesized. For example, the percentage of participants with moderate-to-high water insecurity reporting stress during the previous day or water quality dissatisfaction was 1.80 times (95% CI: 1.50, 2.17) and 4.12 times (95% CI: 2.87, 5.93) higher, respectively, than among those with no-to-mild water insecurity. Conclusions:The Individual Water Insecurity Experiences Scale performs well in high-income countries and yields cross-country comparable measures, supporting its use for global monitoring of water insecurity.

While the literature on the design and operation of safe water sources in low-income communities is huge, little attention has been paid to the design of systems for the safe transportation and storage of water by households between source and point of use. The design of water containers like the near-ubiquitous "jerry can" in relation to how they are used and the potential risks incurred has received little attention. This is despite, as we explain, the strong influence that water container design has on hazards associated with fetching and storing water. This paper advances the argument that MAD ("modular, adaptive and decentralised") approaches to rethinking water containers are possible and points to examples that have been trialled in different locations around the world. Placed in a broader theoretical framework, the objects that are used as water containers can even be viewed as "engines of history" through which human communities interact with the (water) environment and can create off-grid infrastructures. Key suggestions for design improvement include recognizing the role of water containers in heterogenous networks and in wider socio-technical systems that can reinforce marginalization, and the critical need for localized, community-collaborative co-production.

Perceptions of drinking water safety shape numerous health-related behaviors and attitudes, including water use and valuation, but they are not typically measured. We therefore characterize self-reported anticipated harm from drinking water in 141 countries using nationally representative survey data from the World Risk Poll (n = 148,585 individuals) and identify national- and individual-level predictors. We find that more than half (52.3%) of adults across sampled countries anticipate serious harm from drinking water in the next two years. The prevalence of self-reported anticipated harm is higher among women (relative to men), urban (relative to rural) residents, individuals with self-reported financial difficulties (relative to those getting by on their present income), and individuals with more years of education. In a country-level multivariable model, the percentage of the population reporting recent harm from drinking water, percentage of deaths attributable to unsafe water, and perceptions of public-sector corruption are associated with the prevalence of self-reported anticipated harm. Consideration of users’ perspectives, particularly with respect to trust in the safety and governance of water services, is critical for promoting effective water resource management and ensuring the use, safety, and sustainability of water services. Perceptions of drinking water safety influence how people use and value water. Here, the authors find that 52.3% of adults across 141 countries self-report anticipating harm from drinking water.

Urban flooding has made it necessary to gain a better understanding of how well gully pots perform when overwhelmed by solids deposition due to various climatic and anthropogenic variables. This study investigates solids deposition in gully pots through the review of eight models, comprising of four deterministic models, two hybrid models, a statistical model, and a conceptual model, representing a wide spectrum of solid depositional processes. Traditional models understand and manage the impact of climatic and anthropogenic variables on solid deposition but they are prone to uncertainties due inadequate handling of complex and non-linear variables, restricted applicability, inflexibility and data bias. Hybrid models which integrate traditional models with data-driven approaches, have proved to improve predictions and guarantee the development of uncertainty-proof models. Despite their effectiveness, hybrid models lack explainability. Hence, this study explores the significance of eXplainable Artificial Intelligence (XAI) tools in addressing the challenges associated with hybrid models. Finally, crossovers between various models and a representative workflow for the approach to solids deposition modelling in gully pots is suggested. The paper concludes that the application of explainable hybrid modelling can serve as a valuable tool for gully pot management as it can address key limitations present in existing models.

Alleviating water insecurity is not solely or even primarily about physical infrastructure. The capabilities enjoyed by people related to their perceptions of water security need to be understood multidimensionally. This study investigates the relationship between socio-economic structures (household income and asset ownership, caste and gender), physical access (distance to a water source) and household water insecurity (measured through the HWISE scale) amongst rural communities in an arid region of Rajasthan state, India. Source data were derived from a survey of 565 families in 5 villages in Dudu block, analysed using hierarchical cluster analysis to determine how items in the scale related with each other and their relative importance, with random forest approaches applied to explain the driving variables for water security. Distance to water source and income were found to be the two most important variables shaping household water insecurity: those living closer to water sources and having high income were likely to be the most water-secure. Caste is also a strong predictor, with those in lower caste categories experiencing higher water insecurity. Focusing on gender, more female members per household correlates with greater water security, suggested as related to the regional cultural roles of women as principal natural resource stewards including water carrying. These insights into household water insecurity in Dudu block of Rajasthan are transferrable with geographical and cultural adaptation to other arid and semi-arid regions, including the targeting of water management advice.

The launch of many new water journals in recent years is a testament to the growth and importance of water research as a problematique, that is, as both a problem in and of itself and as an important correlate of other global challenges. As entire regions start to run dry or suffer repeated flooding due to climate change, it is more important than ever to understand water availability, quality, use and governance. And as the burgeoning industry of ‘nexus’ studies shows, researchers and policymakers have discovered that, indeed, most elements of society are linked to water. This is a great time to be a water scholar with exciting new opportunities to collaborate with researchers from across the natural and social sciences, engineering, and humanities. Water scholars also have initiated many new journals, book series, etc., that clamour for our insights and academic production. But there are tensions too, linked to the perhaps too-rapid proliferation of journals, their transition to open access (OA) business models, and the unhealthy ways in which these are linked to career prospects for water scholars. In this viewpoint, we explore some of the challenges associated with the recent launch of several new water journals and the concomitant shift to OA publication models. The OA movement offers tremendous upsides in terms of expanding readership, access to scientific knowledge, transboundary collaboration and funding to improve regional equity. But there are downsides too, such as everincreasing demand for free peer-review services, the continued ‘metrification’ of scholarship, dilution of journals managed by professional associations and the monetization by private publishing companies of publicly funded scholarship. There are also other unintended consequences that may reshape the publishing landscape in yet-unknown ways. While these issues affect most scientific disciplines, they are particularly salient for the water sector due to greatly accelerated change in the water-related academic journal landscape over the past five years.

This paper introduces a novel approach to estimate domestic water storage within households by leveraging the classical computer vision technique of object detection. Ensuring universal access to safe drinking water is a critical component of achieving the Sustainable Development Goals (SDG). In recent years, research priorities related to the SDG have evolved to encompass household-scale infrastructure and the real-world experiences of water insecurity. Climate change is dangerously affecting safe drinking water. While robust survey instruments have been developed for acquiring data on many crucial aspects of household water insecurity, such as the distance to water sources, the number of trips made, and experiences of water-related illnesses or injuries, methods for estimating household water storage still rely on manual inspection and estimation. Our proposed methodology involves the collection of a dataset from the Rohingya refugee camp, which is home to one million refugees. Initially, a subset of data is gathered from 900 households. This data is then meticulously cleaned and labeled with different classes, such as buckets, jugs, drums, and more, along with their respective storage volumes. Subsequently, the labeled dataset is used to train an object detection model, capable of identifying objects within images and precisely locating them. The detected objects are then associated with their respective storage containers, and their cumulative volumes are summed to provide a final estimated value within an image. We conducted experiments using five distinct object detection models, which yielded promising results.

In this study of the Andean town of Chuschi and its surrounding district of the same name, we consider the impacts of the proliferation of fencing on once open land. The paper contributes to a growing body of literature on the practice and impact of land fragmentation through fencing around the world, with positive and negative impacts having been noted. The analysis is based on 23 semi-structured interviews with community members and community leaders of Chuschi and the surrounding towns of Yanaccocha, Huaracco, Chaquiccocha, Pucruhuasi, Wacraccocha, Lerqona and Yupana. Some of the interviewees considered the fencing off of parcels of the communal land to be beneficial for land management, while others felt the practice was not ecologically or socially beneficial overall and created tensions in the community. In particular, some interviewees noted resentment towards those perceived to be ‘ambitious’ in terms of acquiring exclusive use of additional land. In conclusion, it appears that fencing, as practiced in Chuschi, may be a calculated approach to land management that some perceive to have overall collective benefits but, if not well governed, it also has the potential to be invasive and disruptive for communal Andean life. The paper addresses a gap in the literature on the motivations for, and impacts of, fencing in rural communities in Peru and contributes to wider debates on the social justice implications of enclosures.

In 2018, Albrecht et al. published a review of water-energy-food (WEF) nexus literature, coming to five main criticisms in nexus research. The five central conclusions of that review together with a consideration of on-going projects and recent nexus research insights form the basis for this critical review. The current state of nexus research, and in particular modelling research, is examined and updated to reflect recent advances and correct misperceptions, and put them in the context of larger epistemological issues. The main conclusions are: 1) The considerable and growing diversity in nexus studies precludes a one-size-fits-all approach. Indeed, it has never been an objective to develop a ‘grand unified nexus theory or model’; 2) A lack of ‘fundamental equations’ between many nexus parameters hinders full quantification of nexus linkages, though data-driven, stochastic and agent-based approaches offer avenues for development; 3) The use of qualitative and social science methods in nexus studies is rapidly gaining traction, especially when blended with quantitative modelling outcomes; 4) Progress has been made in attempting to break disciplinary siloes, especially when considering integrated assessment models and system dynamics models.

Climate change, ageing infrastructure, and funding shortfalls threaten the sustainability of modern, 20th century centralised water systems by increasing drinking water costs and undermining water security, particularly for underserved populations. Modular, adaptive, and decentralised (MAD) water infrastructures can address this by using novel technologies, institutions, and practices to produce, transport, and store clean water in the absence of — or integrated alongside — existing centralised water infrastructure. Examples of MAD water systems include: next-generation ultrafiltration systems, atmospheric water capture systems, mobile water treatment stations, and innovative container-based systems. These decentralised models require a justice-oriented framework to unlock the promise of sustainable access to safe, reliable, affordable water supply for a more mobile, just, and resilient world. We propose a model for advancing justice-oriented MAD water.

In Chinese mythology, the Dragon King has a very special place. Particularly powerful, and appearing in many different guises from the Shang Dynasty (1600–1046 BC) onwards, the Dragon King’s essent...

Very large marine protected areas are in danger of becoming 'paper parks'. This paper uses an interdisciplinary team to investigate the use of remote sensing technologies to provide sufficient evidence for effective fisheries management. It uses the intended marine protected area around Ascension Island as a case study. Satellite technology provides opportunities to detect the presence of fishing vessels but because of difficulties with data interpretation, it is unlikely to be a sole source of evidence for prosecutions. Developing drone technology and traditional over-flights by aerial surveillance may supplement satellite technology with 'eyewitness' evidence. Well-crafted regulations will be able to make some use of this data, but the evidential requirements of criminal courts make prosecutions difficult to pursue. There is some scope to expand management opportunities through vesting the fishery in a public body and pursuing offenders through civil law, this approach having a different suite of remedies. Other opportunities lie in giving very large marine protected areas legal personality which has similar advantages and additional reputational benefits. Using remote sensing data in the civil court poses evidential problems. An alternative approach is to collate data around frequent infringers and, by negatively impacting on their reputation, restrict their ability to obtain insurance, finance, access to fisheries and market access. This is exemplified in port state measures by fisheries authorities and chain of custody requirements by labelling bodies. Data sharing raises challenges with intellectual property and coordination. The paper demonstrates that there are opportunities to make VLMPAs work more effectively.

Scholars and practitioners have been working on methodologies to measure water security at a variety of scale and focus. In this paper, we critically examine the landscape of water security metrics, discussing the progress and gaps of this rich scholarship. We reviewed a total of 107 publications consisting of 17 conceptual papers and 90 methodological papers that propose 80 metrics to measure water security and observed that there are two dominant research clusters in this field: experiential scale-based metrics and resource-based metrics. The former mainly focus on measuring the water experiences of households and its impact on human well-being, while the majority of the latter assess freshwater availability or water resources security. We compare their approaches and the arguments used to develop them. We posit that the more local the scale and the more specific the water domain, the more meaningful results that the metrics can provide. Acknowledging the interrelationship between different water domains (e.g., water resources and water hazards) is important, but their aggregation for measurement may be problematic. We offer our views on future work in this field relating to topics beyond water, the need to conduct validation tests, and collaboration among academics and with other stakeholders.

Poor drinking water quality is a global crisis that affects billions of individuals. Understanding who is most impacted is necessary to develop programs that ensure sustainable, reliable, and resilient access to safe water. But current water indicators do not capture people’s experienced and anticipated harm from drinking water, which means we have had limited understanding of how individuals conceptualize, navigate, and are affected by their water environment. Here, we analyzed data from nationally representative surveys undertaken in 142 countries in which people reported their recent experiences and future expectations of harm from drinking water. Prevalence of reported harm from drinking water in the prior two years was 14.5% (range: 0.8%–54.3%). More than half of the world’s population (54.4%) anticipated that they would experience serious harm from their drinking water in the next two years. Greater public sector corruption was associated with greater anticipated harm from drinking water, even when adjusting for indicators of water infrastructure and economic development. Disparities in anticipated harm across countries and by gender and household location indicate that targeted policies are required to address risk perceptions, equitably improve access to safe drinking water, and increase trust in institutions that supply and regulate water services. The addition of experiential survey data to global data collection efforts will complement objective water quality data and provide novel insights about which strategies will most effectively advance progress toward safe drinking water for all.