Abstract: The reuse of drinking water (direct and indirect; DPR/IPR) is increasingly being proposed as a strategy to strengthen urban water security in the face of climate variability and increasing demand. Although technological barriers have decreased considerably, many projects continue to face intense social and political conflicts. This article examines why technically viable reuse initiatives thrive in some contexts while failing in others, by developing a conceptual framework for analysing the conflicts associated with DPR/IPR. The study proposes three complementary typological matrices: Justification × Acceptance (J×A), Justification × Urgency (J×U) and Demands × Repertoires (D×R), which integrate the structural conditions of the projects with the strategic dynamics of the actors involved. The framework is illustrated by an empirical corpus of 25 global DPR/IPR cases, compiled through a realistic synthesis of academic literature, technical reports and contextual sources. The analysis shows that project trajectories do not depend solely on technological maturity or water scarcity, but on the interaction between technical justification, social acceptance, perceived urgency and, especially, the strategy and agency capacity of actors to mobilise demands, narratives and repertoires of action. Consequently, the advancement, transformation or blocking of potable reuse projects is mainly explained by how these strategies shape the legitimacy of water risk governance.

Abstract: Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through the multi-criteria analysis using GIS and hydrogeological field exploration, water balance modelling. The suitability testing was preliminarily performed in the Google Earth Engine environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests, produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could be recharged into the suitable sites is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that there are recharge rates of between 174 and 5,282 m³/day, with a high degree of spatial variability which is caused by local hydrogeological circumstances. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies.

Abstract: This study presents an integrated assessment of surface water and groundwater quality in the Tula River basin, Mexico, encompassing the Endhó Dam and its associated aquifer. Water quality index (WQI) analysis revealed severe contamination along the Tula River (WQI >300), driven primarily by untreated sewage discharges from Mexico City and inadequate regional sanitation infrastructure. Elevated concentrations of COD, BOD, and nutrients indicate significant organic loading and eutrophication risk across aquatic ecosystems. Near Tula City, heavy metals including arsenic, copper, and zinc were detected at levels posing direct risks to human health. Groundwater quality was com-paratively favorable, with 71% of sampled wells recording WQI < 100; however, arsenic concentrations exceeding permissible limits by more than twentyfold were identified in select wells, attributed to geological sources. Semi-volatile organic compounds (SVOCs) were detected in both hydrological compartments, confirming cross-compartment con-tamination and highlighting the need for contaminant transport and fate modelling. The inertial contamination trajectory of the aquifer indicates that point-source reduction alone is insufficient for remediation. Comprehensive sanitation strategies, including pre-discharge treatment of Mexico City effluents, alongside proactive long-term aquifer monitoring and remediation programs, are urgently required to safeguard water sup-plies, public health, and ecological integrity in the Tula Valley.

Abstract: Environmental planning is essential for climate action, as cities face air pollution, flooding, drought, and other environmental stresses. Yet research on these challenges remains limited in the state of Amazonas, Brazil. Building on a prior participatory study of 1,242 residents, this article advances a long-term research agenda focused on urban water management. The study pursues three goals: update, identify, and classify Benchmark Smart Sustainable Cities (BSSCs), with priority given to the Gold tier (GBSSCs); map ECO AI and Non-AI initiatives, AI techniques, and digital enablers used by GBSSCs to address urban water challenges (Manaus main concern); propose and disseminate Gate4EcoAI, an interactive platform with GBSSCs' urban water initiatives. This applied research triangulated ten international city rankings using a mixed-methods approach (systematic literature review, bibliometric and documentary analyses, statistical methods, open-science practices, and AI-assisted tools) to collect, clean, analyze, and visualize data. Of 265 cities assessed, 99 were classified as BSSCs (20 as Gold). GBSSCs applied 76 regulatory instruments and adopted at least 243 distinct initiatives to address water challenges. Gate4EcoAI is a multidimensional, queryable evidence-to-policy bridge, with valuable initiatives to support the development of roadmaps for urban water challenges. By linking proven initiative types (e.g., Network Process Control, Quality Monitoring), dominant AI techniques (ML-Anomaly Detection, Supervised ML), and enablers (IoT, Cloud) to real-world statuses, it empowers AI developers, policymakers, utilities, and researchers with evidence-based benchmarking to adapt the best practices locally. For Manaus and analog cities, this structured knowledge base bridges science-to-policy gaps, accelerating resilient water governance amid Amazonian vulnerabilities.

Abstract: The global water situation is deteriorating not only due to the progressing climate change but also to irrational consumer behaviors which are driven by attitudes. Given the above, it seemed essential to identify attitudes toward the issue of access to freshwater in households considering sustainable development guidelines. The aim of this study was, therefore, to develop a research tool for the identification of respondents’ attitudes toward this problem and then to determine its validity and reliability. The object of the study was an original questionnaire serving as a research tool for identifying the specified attitudes. The data required for this study were acquired through a critical review of the literature and a questionnaire survey method. The study was conducted in one of the Polish urban agglomerations using the Paper-and-Pencil Interviewing (PAPI) technique. An in-depth analysis of the validity and reliability of the tool, carried out using a statistical procedure, confirmed it to be a viable means to identify these attitudes in Poland. Therefore, there are reasonable grounds to assume that, following the application of the procedure presented in this manuscript, the developed tool may also be used to identify the specified attitudes when implemented in a different population.

Abstract: Drinking water quality is essential for public health and requires monitoring approaches able to capture both regulatory compliance and short-term variability. This study presents a high-frequency IoT-based comparative physicochemical assessment of two drinking-water sources in Bragança, NE Portugal: treated municipal water derived from surface water and groundwater abstracted from a decentralized supply system. A low-cost IoT monitoring system was used to measure pH, electrical conductivity, temperature, oxidation-reduction potential, and total dissolved solids. Monitoring campaigns were conducted between January and March 2026 at two treated-water points within the public supply system and three groundwater points, complemented by municipal records from 2023 to 2025. The treated municipal supply showed a more stable physicochemical profile and lower variability, whereas groundwater was associated with higher mineralization and stronger temporal fluctuations. Significant differences were found for electrical conductivity, total dissolved solids, oxidation-reduction potential, temperature, and pH. High-frequency monitoring enabled the identification of dynamic patterns and transient fluctuations that would be difficult to detect through discrete sampling alone.

Abstract: Electric discharge cavitation is an effective method for water treatment that combines physical and chemical effects within a single process. It enables water disinfection, extraction acceleration, dispersion of solid particles, and enhancement of porous material permeability. Compared to conventional chemical treatment, it reduces the demand for reagents and minimizes secondary pollution. This new and developing technology significantly contributes to the preservation of natural aquatic ecosystems by providing a sustainable alternative to traditional decontamination methods, thereby reducing the overall anthropogenic pressure on the environment. This study focuses on developing a reliable method for assessing electric discharge cavitation intensity and controlling water purification processes. The proposed approach is based on the oxidation of iodide ions to molecular iodine by reactive species generated during electric discharge cavitation. The adapted iodometric method is sensitive, reproducible, and does not require complex optical or acoustic equipment. Experimental results confirmed that iodometry provides accurate evaluation of cavitation intensity, allowing control of specific energy consumption and optimization of treatment parameters. Optimal operating conditions were established to control the water processing by electric discharge cavitation: stainless-steel electrodes, specific input energy not exceeding 280 kJ·L-1, the presence of a free liquid surface in the working chamber, and a discharge pulse frequency below 10 Hz. The proposed method supports the development of energy-efficient, low-waste technologies for wastewater and natural water treatment and facilitates the integration of electric discharge systems into existing water treatment infrastructure, particularly under resource-limited conditions.

Abstract: Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors and the frontend electronics with its pulse shape analysis effectively separates neutron signals from background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments has been validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, cost-effective and reliable alternative for the next generation of CRNS monitoring networks.

Abstract: Today, the reuse of treated wastewater is considered an important and strategic driver for integrated and sustainable water and soil management in extremely arid desert regions, where significant constraints due to water scarcity, soil salinization, and the fragility of agricultural ecosystems within palm oases place a strain on all sustainable development policies. Through this study, we conducted a comprehensive evaluation of the performance of the treatment, as well as the constraints related to salinity and the implications for the land management of the activated sludge wastewater treatment plant located in the Timimoun desert oasis in southern Algeria. Through monthly monitoring over a 12-month period, we were able to perform an analysis of physicochemical, nutritional and microbiological parameters, as well as a seasonal analysis, in addition to calculating irrigation suitability indicators using first-order kinetic modeling of COD degradation. The results obtained showed high reduction rates for COD (90%), BOD5 (90,5%), and TSS (93.8%), confirming the resilience and effectiveness of biological treatment under very difficult and hostile climatic conditions. Furthermore, the ultraviolet disinfection process ensures microbiological quality that allows for reuse of treated water in agriculture. However, the residual salinity of this water remains a significant limiting factor for sustainable reuse, highlighting the need to integrate soil management strategies, crop selection, and irrigation management into regulatory frameworks for wastewater reuse. Therefore, this study provides us with important and useful scientific data for developing sound and sustainable water and land management policies in the harsh climate of Saharan oases.

Abstract: We describe a harmonic analysis system for predicting annual peak snow water equivalent (SWE) at SNOTEL monitoring stations operated by the Natural Resources Conservation Service (NRCS) across the western United States. The algorithm, frqsrchX, performs greedy harmonic regression on historical SWE records, identifying persistent periodic climate signals and superimposing volcanic impulse functions to account for episodic radiative forcing from major eruptions. A rigorous five-phase characterization pipeline applies distinct band-limited search strategies per site, and a two-winner selection system identifies optimal configurations by both maximum pass rate and a reliability score that balances accuracy with period stability. Validation uses out-of-sample holdout testing across 15–18 years (2008–2025), graded by an asymmetric scale that penalizes over-prediction more harshly than under-prediction. We report results for 771 SNOTEL and SNOW SENSOR stations across eight western states. Average pass rates range from 88.4% (Montana, 94 sites) to 49.3% (California, 122 sites, including 87 SNOW SENSOR stations). The three commercially targeted states—Colorado (113 sites), Montana (94 sites), and Wyoming (87 sites)—achieve average pass rates of 86.4%, 88.4%, and 84.2% respectively, with 84–90% of sites meeting the ≥80% operational pass-rate threshold using identical universal parameter search procedures and no state-specific tuning. Idaho (85 sites) and Washington (76 sites) show strong intermediate performance at 83.3% and 81.5%. Utah and Oregon show mixed results, while California falls well below operational thresholds. Period stability analysis indicates that 55–62% of qualifying sites in the five strongest states achieve stable signal detection, demonstrating consistent identification of physical climate periodicities. These results demonstrate that periodic climate signals—principally in the ENSO band (2,700–2,900 mY), a mid-range band (~6,000–7,500 mY), and an extended long-period band (10,500–17,000 mY)—carry actionable predictive information about annual peak snowpack at individual station scale.

Abstract: Suitable forecasting of streamflow plays a fundamental role in the sustainable management of water resources. This is especially important in regions with high hydroclimatic variability, such as Colombia. Reliable predictions of water availability are essential to support decision-making on water allocation, risk mitigation, and the long-term resilience of supply systems, including urban aqueduct services. However, traditional deterministic models often require extensive parameterization. They may lack flexibility. Data-driven approaches such as Long Short-Term Memory networks typically fail to provide meaningful uncertainty quantification. In this work, we introduce the Chained Correlated Gaussian Process. This is a scalable probabilistic framework for jointly forecasting multiple hydrological time series. The proposed model represents likelihood parameters through latent Gaussian Processes. This enables the use of non-Gaussian likelihoods to enforce physical constraints on the outputs. In particular, a Gamma likelihood is integrated into a Linear Model of Coregionalization. This approach captures interdependencies among multiple time series while ensuring non-negativity. To achieve computational scalability, we employ a variational inference framework with shared inducing points. This reduces computational complexity from cubic to linear with respect to the number of observations. Additionally, a hybrid optimization strategy is adopted. It combines Adam and Natural Gradient methods to improve numerical stability and avoid poor local optima. The proposed methodology is validated using twelve years of daily observations from 23 reservoirs in Colombia. These reservoirs serve as proxies for water availability dynamics within interconnected resource systems. Experimental results demonstrate that the ChdGP framework consistently outperforms baseline models across multiple forecasting horizons. The Gamma-likelihood configuration shows superior performance. It provides physically consistent predictions, improves uncertainty quantification, and effectively captures asymmetric behaviors and extreme hydrological events. Performance evaluation based on the Negative Log Predictive Density metric confirms the robustness of the proposed approach under non-Gaussian conditions. The results highlight the potential of the proposed framework as a reliable tool for probabilistic forecasting and risk-aware decision-making in sustainable water resource management. It offers a flexible, scalable methodology for modeling complex hydrological systems under uncertainty.

Abstract: Conventional clustering techniques for urban water consumption profiling treat each household as an independent entity, thereby disregarding the spatial, socioeconomic, and infrastructural contexts that jointly govern demand behavior. This structural limitation prevents the extraction of contextually coherent consumption profiles—a critical shortcoming for utility managers who must design spatially targeted conservation interventions. To overcome this, we propose Simple GLAC, a novel graph clustering framework that leverages graph neural networks with an adaptive attention mechanism to dynamically model these complex interdependencies. The model’s end-to-end training jointly optimizes a latent representation for cluster cohesion, separation, and spatial homogeneity, where each household’s multi-month consumption record serves as the node feature vector encoding temporal consumption patterns. Evaluated on a large-scale real-world dataset of 4590 residential households across four distinct graph topologies, Simple GLAC consistently achieves superior multi-metric performance over both traditional and graph-based benchmarks, yielding interpretable and operationally actionable consumption profiles aligned with the spatial, administrative, socioeconomic, and infrastructural dimensions of urban water governance. This work provides a powerful, data-driven tool for utility managers to deploy targeted water conservation strategies and optimize urban resource distribution.

Abstract:

Katse Dam(KD), a strategic raw water source to South Africa, is exposed to pollution from mining and aquaculture production. The organic pollution index (OPI), the modified pollution index (MPI), and Carlson's trophic state index (CTSI) have not been previously applied to KD. The current study applies these indices to assess the trophic status of KD in the first decade (FD) (2003-2013), when the intensity of mining and aquaculture activities was minimal, and compares with the second decade (SD) (2014-2024) when production was higher. The Pollution Index of KD revealed that it transitioned from contaminated during the FD to greatly contaminated during SD. KD shifted from eutrophic status to hypereutrophic status in the lacustrine zone during the SD. The cyanobacteria Radiocystis sp. replaced Asterionella sp. and became the most pollution-tolerant algae in the SD, followed by the diatom Flagilaria sp. The pollution index (PI) values of physico-chemical parameters increased from 65 in the FD to 160 in the SD. OPI classifies KD as extremely polluted, with values above the threshold of 5 OPI in the SD. Application of the different indices, attribute mining, and aquaculture as influential to the transition of KD from mesotrophic to eutrophic in the transitional zone. The findings provide environmental managers with a basis to mitigate pollution at source to secure good water quality.

Abstract: Flood inundation modelling in low-gradient monsoon floodplains requires a physically consistent representation of rainfall–runoff–inundation processes. This study develops a hybrid modelling framework that integrates a coupled hydrological–hydraulic model (HEC-HMS–HEC-RAS) with a deep learning–based LSTM–U-Net surrogate to represent temporal hydrological memory and spatial inundation patterns. The framework is applied to the Upper Songkhram River Basin in northeastern Thailand, a storage-dominated floodplain strongly influenced by monsoon hydrology. The hydrological model demonstrated strong validation performance (NSE = 0.896, KGE = 0.827, R² = 0.909), while hydraulic simulations showed high spatial agreement with satellite-derived inundation maps (F1 = 0.876, Kappa = 0.873). Trained on hydraulically simulated discharge–inundation pairs, the LSTM–U-Net model successfully reproduced two-dimensional flood patterns across independent flood events (mean F1 = 0.838, IoU = 0.721), with prediction errors mainly occurring along shallow floodplain margins. Future projections under CMIP6 SSP2-4.5 and SSP5-8.5 indicate substantial increases in flood-season discharge (up to ~80%), whereas maximum inundation extent expands more moderately (≤21%), reflecting nonlinear floodplain response in low-gradient systems. The proposed framework preserves hydrological–hydraulic consistency while supporting future flood inundation projection, climate-informed flood risk assessment, and adaptation planning.

Abstract: Since 1970 there has been sustained growth in Kuwait’s water demand, met by supply-side management associated with adverse economic and environmental implications. Demand side management is critical to a sustainable water future but removing subsidies is constrained by political realities. Therefore, this study explores the potential of technology-based conservation measures, addressing seven household water uses, in reducing demand. A population microsimulation coupled with a water micro-components model is used to forecast demand to 2050, which with its growing population, increases 32%. Demand is then ‘backcast’ against a 2050 ‘no new water’ target, and two progressively more challenging targets. The technology transition pathways that emerge reveal the potential of technology measures, including that the ‘no new water’ target could be achieved just through targeting household showering, so long as near universal uptake of water efficient showers can be achieved. Doing so would deliver major reduction in water production cost and carbon emission from desalination. Although technology-based measures are likely to be more politically acceptable than pricing measures, the social acceptability of efficient devices, and the risk of rebound effect is not known, particularly with respect to the relatively small group of citizens in villa dwellings that account for 70% of demand.

Abstract: Saudi Arabia faces extreme water scarcity with renewable freshwater levels well below water poverty line, circa 71 m3 per capita yr-1. In contrast, water demand in domestic sector has grown rapidly in the last four decades. This study forecasts domestic water demand to 2060 under a Business As Usual (BAU) scenario to provide a baseline for water conservation policy. A second-order polynomial regression model was developed using historical demand (1995–2020). Domestic water demand has been used as the dependent variable whilst population growth as the independent variable. The model demonstrated high predictive reliability with an R2 (>0.93) and a Mean Absolute Percentage Error (MAPE) of 3.6%. Results indicate that aggregate domestic demand will nearly double, rising from 3,556 million cubic meters (MCM) in 2020 to 6,853 MCM by 2060. This 92.7% increase significantly outpaces the projected 66.3% population growth, driven by a 20.5% rise in per capita consumption from 297 l/d to 385 l/d. We conclude that current consumption patterns are unsustainable, requiring a doubling of desalination capacity that threatens national net-zero carbon goals. Policy must shift from supply-side expansion to strict demand-side management, including smart infrastructure and mandatory efficiency standards, to ensure long-term water security.

Abstract: Quantitative analytical methods for measuring concentrations of chemical substances in aquatic systems typically have acceptable accuracy and precision only for an intermediate range of analyte concentrations. Outside this range, the uncertainty of concentration estimates is too high to justify reporting them as valid measurements for use in statistical analyses. Therefore, concentration estimates falling below the lower reporting limit (LRL) typically are reported as the LRL, along with a code indicating that the measured values fell below the LRL. Such data are called left-censored data. Similarly, concentration estimates falling above the upper reporting limit (URL) typically are reported as the URL, along with a code indicating that the measured values exceeded the URL. Such data are known as right-censored data. Censored data violate assumptions underlying most parametric statistical methods, such as t-tests, regression analysis, and analysis of variance. We briefly review various statistical methods that have been employed for analyzing censored concentration data, then review in greater detail some modern statistical survival-analysis methods that have become available in standard software within the last 10 years and can be applied to concentration data with both left- and right-censored values. Methods are illustrated with real data.

Abstract: Groundwater is a key strategic resource underpinning water security, and its effective management requires reliable, high-frequency monitoring data. In mountainous regions such as the flysch Carpathians in southern Poland, natural springs are particularly sensitive indicators of aquifer system dynamics. This study analyzes the role of springs in the national groundwater observation and research network and identifies barriers to the implementation of automated monitoring of spring discharge. The research covered 28 springs operating within the regional monitoring network of the Polish Geological Institute – National Research Institute in the Carpathian region. Classical hydrogeological spring classifications were applied and complemented with proprietary criteria addressing formal-legal, technical, and environmental conditions affecting the feasibility of automation. The results show that most analyzed springs exhibit high discharge variability and rapid responses to precipitation, indicating that weekly manual measurements are insufficient to capture flow dynamics. The main barriers to telemetry implementation are non-technological and related primarily to ownership, administrative, and environmental constraints. The proposed spring classification framework supports rational planning of monitoring network automation and may be applicable in other mountainous regions with similar hydrogeological conditions.

Abstract: The increasing demand for sustainable water management has prompted the search for efficient domestic wastewater treatment technologies. Electrocoagulation (EC) has emerged as a promising alternative owing to its simplicity, efficiency, and potential for decentralized applications. This study investigated EC for treating domestic wastewater, focusing on optimizing operational parameters via the design of experiments (DoE). Initially, raw wastewater was characterized, followed by a fractional factorial design to screen for significant variables: operating time, current density, initial pH, and NaCl dosage. Results revealed that current density and pH were the most influential parameters on Chemical Oxygen Demand (COD) removal. Subsequently, a Central Composite Rotational Design (CCRD) optimized these key parameters. Optimal conditions were a current density of 85 A/m² and pH of 5.5, achieving COD removal efficiencies up to 78.2%. A cost analysis indicated the economic feasibility of EC for smaller effluent volumes, with an estimated operational cost of US$1.23 per cubic meter treated. Applying this methodology to real sewage matrices (Federal University of Sergipe WWTP and a residential condominium) showed variations in Biochemical Oxygen Demand (BOD), COD, and turbidity removal. These findings confirm EC's potential as a sustainable solution for domestic wastewater treatment in isolated communities.

Abstract: Textile dyeing industry is a significant contributor of complicated and extremely polluting wastewater. This wastewater has intermittent loads of chemical oxygen demand (COD), stains and other pollutants which puts dangerous effects on the sustainability of the environment and human beings in general. The traditional operation of wastewater treatment plants is reactive and rule-based to a large extent. These methods are ineffective in dealing with the non-linear dynamic character of the effluent of the textile business, resulting in low efficacy and recurring regulatory breach. To overcome these shortcomings, this paper will suggest a new hybrid architecture SAGE-GBTCN (Shock-Aware Gated Ensemble with Gradient Boosting and Temporal Correction Network) to be used in the effective prediction of wastewater pollution. This model combines a gradient boosting ensemble to produce baseline predictions and a parallel temporal network with a residual correction. A shock-sensitive gating system is used to dynamically modify the correction process to consider any sudden, non-stationary changes in the nature of the effluents. This design makes the model very useful in capturing the long-term trends as well as abrupt disruptions within textile wastewater. The suggested SAGE-GBTCN model was tested with the help of data on a full-scale wastewater treatment facility. The findings are shown to be more accurate in prediction and better resistant to abnormal operating condition. The model also demonstrates high possibilities to facilitate active and energy saving management of textile wastewater treatment processes, which will result in an R2 predictive value of 0.942 and a RMSE of 30.30 of COD. Although validated on full-scale industrial WWTP data, the proposed framework targets operational characteristics typical of textile effluent treatment plants, including batch-wise COD loading, abrupt shock events, and chemically driven variability.