Sustainable Water Resources Management Research Articles

Roles of oxygen vacancies in layered double hydroxides-based catalysts for wastewater remediation: fundamentals and prospects.

Roles of oxygen vacancies in layered double hydroxides-based catalysts for wastewater remediation: fundamentals and prospects.

Activity-based mathematical modeling of household water consumption across regions: proposing efficient fixtures for sustainable water management in Saudi Arabia

Abstract This study presents an activity-based mathematical model to assess daily per capita and regional water consumption in Saudi Arabian households (8.17 million, as per the Saudi Census Statistics, 2022). The model compares two scenarios: (a) the current scenario with standard fixtures and (b) the proposed scenario with efficient fixtures. Water consumption is analyzed based on daily activities and house zones. Under the current scenario, showering is the highest water-consuming activity, using 209-551 liters (23–29%), while the bathroom is the highest water-consuming house zone, using 436-1151 liters (48–60%) with current scenario. Regional water consumption is assessed based on average household size, with Riyadh, Makkah, and the Eastern Province exhibiting the highest daily per capita water consumption of 328-330 liters. These three regions contribute a cumulative daily water demand of 6.763 million $${m}^{3}$$ m 3 , more than double the water demand of the remaining ten regions, which totals 3.171 million $${m}^{3}$$ m 3 . This variation highlights the limitations of nationwide assessments and emphasizes region-specific water management strategies to address water security challenges in Saudi Arabia. The study further analyzes water savings under the proposed efficient fixtures scenario, achieving a reduction of 83-93 liters per capita, resulting in a nationwide daily water saving of 2.806 million $${m}^{3}$$ m 3 . The mathematical model developed is flexible, enabling forecasts of water consumption based on hybrid scenarios combining standard and efficient fixtures. The findings have significant implications for the sustainable management of water resources, contributing directly to Clean Water and Sanitation (Sustainable Development Goal 6) by informing strategies for efficient water use and conservation in Saudi Arabia.

Citizen science approach for springshed management: A comprehensive community-driven mapping and dataset of spring sources in Kavre, Nepal.

Citizen science approach for springshed management: A comprehensive community-driven mapping and dataset of spring sources in Kavre, Nepal.

Nutrient and manganese pollution in groundwater of the northeastern Nile Delta: distribution, sources, and health risks

Groundwater pollution poses significant health and environmental risks, particularly in regions heavily reliant on this resource. This study assessed nutrient and manganese pollution in groundwater of the northeastern Nile Delta, identifying pollution sources and evaluating associated health risks. An integrated methodology was employed, incorporating water pollution indices, health risk modeling, satellite imagery analysis, and statistical analysis. Thirty-one groundwater samples were analyzed for nitrate, nitrite, ammonia, phosphate, and manganese. Concentrations ranged from 0.3 to 52 mg/L, 0.2 to 11.3 mg/L, 0.02 to 25 mg/L, 0.1 to 2.3 mg/L, and 0.01 to 1.94 mg/L, respectively. The nitrate pollution index (NI) classified 80.6% of samples as polluted to varying degrees, while the nutrient pollution index (NPI) indicated that 67.7% were moderately to highly polluted. The heavy metal toxicity load for Mn (MTL) suggested that 61.3% of samples surpassed the manganese toxic load of 239.4 mg/L, and groundwater in the western regions needs treatment to remove over 60% of manganese to mitigate potential health risks. The correlation coefficient analysis revealed strong correlations between satellite-derived land-use and pollutant concentrations. It showed a strong positive correlation between agricultural areas derived from the Normalized Difference Vegetation Index (NDVI) and ammonia and manganese levels, and a strong positive correlation between urban areas derived from the Normalized Difference Built-up Index (NDBI) and nitrate concentrations. Moreover, surface irrigation water displayed severe nutrient pollution, highlighting its role as a major pollution source alongside land use. The total health risk (HItotal) for nitrates and manganese ranged from 0.016 to 1.07 and 0.04 to 1.633, respectively, with 9.6% and 16.1% of samples indicating non-carcinogenic risk (HItotal > 1) for adults and children, respectively. This comprehensive study provides crucial insights for groundwater management in the region, identifying areas requiring urgent mitigation measures to protect public health and ensure sustainable water resource management.

Groundwater potential zone mapping and sustainable management in Chennai, Kancheepuram, and Tiruvallur districts using GIS-based multi-criteria decision analysis

This study uses GIS-based multi-criteria decision analysis integrated with statistical techniques to investigate groundwater potential zones (GWPZs) in Chennai, Kancheepuram, and Tiruvallur districts. The study emphasizes regional-specific assessments, identifying the coastal plain regions of Kancheepuram and Tiruvallur as areas with high groundwater potential. Lithology was identified as the most critical factor influencing groundwater availability, followed by soil and drainage density. The variability of rainfall patterns further highlighted the significance of traditional weightage methods combined with modern GIS-based modelling for accurate assessments. This integrated approach provides a robust sustainable water resource management framework, especially in urban and peri-urban areas. Future research directions include applying machine learning, enhanced geospatial techniques, and utilizing diverse datasets such as remote sensing and geophysical surveys for improved GWPZ mapping.Graphical

Water Temperatures Across All Compartments of the Critical Zone in the Context of Global Change: Application to the Seine Hydrosystem

Temperature is a critical factor at the interface between water and energy stakeholders. It plays a vital role in enabling them to sustain and develop their activities without competing for resources, particularly during periods of crisis. Both surface water bodies and subsurface compartments (<200 m), essential for maintaining aquatic ecosystems and supporting human adaptation to global changes, are utilized for a range of purposes. These include low-impact thermal energy production (e.g., river uses and shallow geothermal energy), drinking water supply, irrigation, and industrial applications. However, these diverse uses by water and energy stakeholders, along with their associated infrastructures, lead to thermal interferences. These interferences are superimposed on broader climatic variations and trends, further complicating resource management. In the Seine basin, observed trends are projected to persist and intensify. These include rising average temperatures, decreasing summer rainfall, and the increasing frequency and severity of extreme events such as floods, droughts, and heatwaves. The sustainable management of water resources will hinge on our collective ability to anticipate and mitigate the effects of these changes. To better predict the Seine basin’s responses to climate change, it is crucial to deepen our understanding of heat transfers between the atmosphere and the various compartments of the hydrosystem. This knowledge will be key to developing strategies that balance the needs of all stakeholders while preserving vital ecosystems and ensuring resilience against global change. In this presentation, we will present data collection, the development of numerical tools, and the evaluation of the evolution of the Seine's temperatures over 100 years. Physical models and simulations help quantify thermal fluxes, highlighting the main sources of heat input and heat losses. The use of machine learning models in projecting the Seine's temperatures in Paris by 2100 adds a predictive dimension. Future developments to achieve modeling that allow us to produce numerical simulations necessary for the integrated management of surface and groundwater in quantitative, qualitative, and thermal terms will be presented.

Seasonal Variations of Hydraulic Exchange Between Surface Water and Groundwater in an Alluvial Plain Setting Using 222Rn

Understanding dynamic groundwater–surface water interactions in alluvial plains is critical for sustainable water resource management, yet seasonal variability and spatial heterogeneity of these exchanges remain imprecisely quantified. Here, we present an improved 222Rn mass balance model for evaluating the seasonal hydraulic exchange between groundwater and the Xintongyang Canal in the Taizhou alluvial plain over the course of a hydrologic year. To reduce the model uncertainty, the “background” 222Rn for non-groundwater sources was incorporated into the model to replace the influence of hyporheic exchange. The results indicate that the hydraulic exchange process of surface water and groundwater has significant spatiotemporal differences. Based on the calculations from the 222Rn mass balance model, the canal leakage flux follows the order of summer > autumn > winter > spring over the course of a hydrologic year. In contrast, the groundwater discharge flux follows the order of summer > spring > autumn > winter. During a hydrological year, summer demonstrated the most intense water exchange dynamics, with peak fluxes reaching 0.0455 m3/(s·m) for surface water leakage and 0.0013 m3/(s·m) for groundwater discharge, revealing pronounced spatial heterogeneity in dominant exchange processes. 222Rn activity in canal and groundwater varies significantly across different regions, with canal leakage being the dominant mode of hydraulic exchange within the study area. The change of the hydraulic exchange process was mainly affected by factors such as rainfall. In the process of promoting surface water leakage, precipitation will also strengthen the supplement of groundwater and contribute to the groundwater discharge in most of the canal sections. This study offers insight into the seasonal variations of groundwater and surface water interaction within an alluvial plain.

Crop water requirements assessment in the irrigation-scarce Terai plain: a case study of Rohini Rural Municipality, Nepal

ABSTRACT Agriculture in Nepal's Terai plain, particularly in Rohini Rural Municipality, faces growing challenges from seasonal water scarcity, erratic rainfall, and inadequate irrigation infrastructure. This study evaluates crop water requirements (CWRs), irrigation water requirements, and irrigation scheduling for key crops – rice, wheat, potato, mustard, pulses, and winter vegetables – using the CROPWAT 8.0 model and FAO Penman–Monteith method. Local climate data, crop characteristics, and loam soil conditions were used to estimate reference evapotranspiration (ETo), crop evapotranspiration (ETc), and effective rainfall. Results indicate high seasonal variation in irrigation demand, peaking in December and declining during the monsoon. Rice had the highest CWR (740.3 mm), followed by wheat (357.3 mm). The study highlights the significance of site-specific irrigation planning, as local factors significantly influence water needs. Spatial analysis via ArcGIS identified dominant rain-fed areas and zones with water deficits, underscoring the urgency for improved irrigation strategies. The study validates CROPWAT's utility in data-scarce regions and provides practical irrigation schedules suited to local agro-climatic conditions. These insights are valuable for enhancing irrigation efficiency, conserving water, and boosting crop productivity – contributing to informed agricultural planning and sustainable water resource management in Nepal's Terai region.

Exploring the Potential of Pressure Retarded Osmosis: A Review on Case-Based Analysis of Energy Recovery Technologies

Pressure Retarded Osmosis (PRO) is a promising salinity gradient energy technology that harnesses the osmotic pressure difference between freshwater and saline sources to generate renewable energy. This paper provides a comprehensive review of the theoretical basis, thermodynamic principles, membrane technologies, and real-world applications of PRO systems. It examines case studies of hybrid implementations with seawater reverse osmosis (SWRO) and wastewater treatment facilities, focusing on energy efficiency, brine dilution, and environmental benefits. Additionally, the paper addresses critical challenges such as membrane fouling, internal concentration polarization, and system scalability. Through a systematic analysis of Scopus-indexed studies, this work identifies key technological advancements and future research directions essential for commercializing PRO technology. The findings suggest that with continued innovation and interdisciplinary collaboration, PRO can become a vital contributor to the sustainable energy mix and water resource management strategies.

Granular computing for monthly inflow prediction to Alavian Dam, Iran

ABSTRACT Accurate river flow prediction is critical for sustainable water resource management, particularly in arid and semi-arid regions. However, balancing model accuracy, computational efficiency, and interpretability remains a significant challenge due to the complex and nonlinear nature of hydrological systems. This study employs a granular computing (GRC) model to predict monthly inflows to the Alavian Dam in Iran. principal component analysis (PCA) was used to reduce input dimensionality, identifying six key variables to enhance computational performance. The predictive performance of GRC was compared with artificial neural networks (ANNs) and support vector machines (SVMs), using coefficient of determination (R2), root mean square error, and mean absolute error as evaluation metrics. The GRC model achieved R2 values of 0.93 during calibration and 0.94 during validation, outperforming both ANN and SVM. Notably, GRC demonstrated superior accuracy in capturing extreme flow events, which are crucial for flood and drought management. This advantage is attributed to its rule-based structure and local learning approach, which enable effective modeling of nonlinearities and sparse data. Furthermore, the interpretability of the GRC model – facilitated by its use of granules and transparent if-then rules – offers valuable insights into variable influence. These strengths highlight GRC as a reliable and efficient tool for hydrological forecasting and climate-adaptive water resource planning.

Smart water leak detection using wireless sensor networks

This study explores the use of remote sensor systems to detect leaks in underground water pipelines, aiming to tackle the problem of water loss in distribution networks. Identifying and preventing such leaks is essential for the sustainable management of natural water resources. To address this challenge and improve leak detection, the authors designed a remote system that utilizes flexible wireless sensors to identify leaks while minimizing energy consumption. This system also evaluates the time and cost efficiency of smart water leakage detection (SWLD) in pipelines, monitors water levels in storage tanks, and automatically activates controls when water levels drop below a set threshold. The proposed approach is built around two main components. The first utilizes GSM (Global System for Mobile Communications) technology to send SMS alerts to the owner. Core elements of this system include sensors, a GSM module, an Arduino board, and a switch for device control. The second component features an Android-based application that allows for remote management of the system. By enabling early leak detection, the system aims to lower the costs and time involved in pipeline maintenance, enhance operational efficiency, and reduce long-term repair efforts.

Sustainable Management of Water Resources in Ngesrepbalong Village, Kendal

Water plays an essential role in human life. However, destructive behavior and a lack of awareness about its preservation have contributed to an crisis. Proper management is key to ensuring the sustainability and continued availability of water. Ngesrepbalong village, located on the slopes of Mount Ungaran in the Limbangan District of Kendal Regency, Indonesia, is home to abundant water sources. This study explores the village’s water resource management model and its conservation efforts. A qualitative, descriptive research approach was used, employing source triangulation for analysis. Data collection was carried out by in-depth interviews, document analysis, and field observations. The findings reveal a conflict in the utilization of water resources between the community and the Kendal Region Water Company(PDAM), affecting the availability of water for the lower hamlet. Additionally, problems arise in the agricultural sector, which has limited sources of irrigation. Various efforts have been made to control water usage by the community, including the Community-Based Drinking Water and Sanitation Provision Program (PAMSIMAS).

Enhancing Snow Detection through Deep Learning: Evaluating CNN Performance Against Machine Learning and Unsupervised Classification Methods

Abstract Snow cover is a crucial natural water resource that provides water storage. Monitoring snow cover is essential for sustainable water resource management. Accurate and efficient snow cover mapping is vital for water resource planning, flood control, and climate impact assessments. Remote sensing provides a cost-effective approach for monitoring snow cover dynamics, but classification accuracy remains a challenge. This study introduces a deep learning-based 1D convolutional neural network (1D CNN) approach for improved snow cover classification in mountainous regions utilizing Landsat 5 and 8 satellite images. The effectiveness of 1D CNN was compared with unsupervised classification (UC) methods (Normalized Difference Snow Index (NDSI), band ratio-based Red/Short wave infrared (R/SWIR), and Near infrared/Short wave infrared (NIR/SWIR)) and supervised machine learning classification methods (support vector machines (SVM) and classification and regression trees (CART)) in this research. The study was performed on Erciyes Mountain, Turkey, across four different years. Classifier performances were assessed using various quantitative (precision, recall, F-score, overall accuracy, kappa) and qualitative (mean squared error, Pearson correlation coefficient, structural similarity index measure, peak signal-to-noise ratio) evaluation metrics. Pairwise statistical analysis using McNemar’s test revealed significant differences between 1D CNN and the other classifiers. The findings demonstrated that the 1D CNN approach outperformed all other methods in achieving the highest accuracy in snow cover classification. These results indicate the potential of deep learning for enhancing snow cover assessment and supporting more effective water resource management strategies and policy-making.

Sustainable water resource management and ecological restoration in the Syr Darya River basin: assessment of the impact of channel regulation near the Saryshyganak dam

ABSTRACT The purpose of this study was to analyse different scenarios of water resources use to assess the variability of water levels in the aquatic environment of the Small Aral Sea. The following methods were employed during the study: method of mathematical modelling; method of hydrological calculations; comparative analysis; analysis of hydrological data. The methods were chosen for their reliability and applicability to hydrological studies, enabling accurate water level predictions, assessment of resource management options, and analysis of hydraulic structures impact. Based on the results of the calculation of the filling of the Small Aral Sea, it was established that the time required to reach water levels of 50 and 44 metres was between 2 and 15 years. Using the method of mathematical modelling used for forecasting, it was found that the period of water level restoration in the Small Aral Sea under the application of the single-level option could be 26 years on average. The most effective approach for the Northern Aral Sea is the two-tier approach with the marks of the normal headwater level of 42 m and for the Saryshyganak Bay − 50 m, which helped to optimise the use of water resources and reduce water consumption for evaporation. The findings of this study can be applied in practice by hydrologists, hydraulic engineers, resource scientists, environmental engineers, ecologists, ecologists-hydrobiologists, specialists in water management and environmental monitoring.

Delineating groundwater recharge zones: semi-arid NW Algeria case study

In arid and semi-arid regions, groundwater is a vital resource increasingly impacted by population growth and agricultural demands. This study identifies potential groundwater recharge zones in the Oued Isser-Sikkak Basin, northwestern Algeria, using the Analytic Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS). Six thematic layers—land use, slope, geology, drainage density, lineament density, and rainfall—were weighted and combined to produce a recharge potential map. The basin was classified into five categories: very high (4%), high (33.7%), moderate (45.6%), low (12.5%), and very low (1.9%) recharge potential. The highest potential zones were found in the south and east, where limestone and dolomite formations prevail. Validation with field observations and previous research showed an 83.7% match, confirming the model’s reliability. The study demonstrates the effectiveness of integrating AHP and GIS in assessing groundwater potential, offering a valuable tool for sustainable water resource management in data-limited, dryland areas.

Hydrological Assessment of Uttarakhand Using the Budyko Framework and Geospatial Analysis: Insights for Sustainable Water Resource Management

Hydrological Assessment of Uttarakhand Using the Budyko Framework and Geospatial Analysis: Insights for Sustainable Water Resource Management

Paddy Field Scale Evapotranspiration Estimation Based on Two-Source Energy Balance Model with Energy Flux Constraints and UAV Multimodal Data

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R2 > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R2 values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management.

Spatiotemporal Dynamics and Attribution Analysis of Multitemporal Runoff Patterns for Water Resources and Climate Security in Huaihe River Basin

ABSTRACTUnderstanding the spatiotemporal evolution and attribution of streamflow is critical for effective water resource management and climate security. This study conducts a comprehensive analysis of runoff dynamics across multiple temporal scales in the Huaihe River basin, which is a major hydrological region in China. By examining annual, interannual, and interdecadal trends, the research delineates tendencies, abrupt changes, and periodicity in runoff patterns. The double mass curve (DMC) method is applied to quantify runoff variations, incorporating influences of vegetation dynamics, anthropogenic water withdrawals, and climate change. The results reveal a highly uneven annual runoff distribution in the Huaihe River basin. Most hydrometric stations show statistically insignificant declining trends in annual, flood‐season, and non‐flood‐season runoff, except for the Hengpaitou section. Significant mutation points are detected around 1990 and 2000 across all runoff series, along with periodic fluctuations featuring dominant about 30‐year cycles. Attribution analysis indicates that human activities account for over 80% of the observed streamflow variations. In the upper basin, indirect anthropogenic factors (e.g., land use changes and vegetation dynamics) dominate, whereas direct human interventions have a stronger influence in the middle reaches. These findings enhance understanding of the interactions between natural processes and anthropogenic impacts on hydrology, providing a scientific basis for sustainable water resource management under climate change in the Huaihe River basin and similar regions.

Environmental Challenges and Vanishing Archaeological Landscapes: Remotely Sensed Insights into the Climate–Water–Agriculture–Heritage Nexus in Southern Iraq

Iraq faces significant challenges in sustainable water resource management, due to intensive agriculture and climate change. Modern irrigation leads to depleted natural springs and abandoned traditional canal systems, creating a nexus between climate, water availability, agriculture, and cultural heritage. This work unveils this nexus holistically, from the regional to the local scale, and by considering all the components of the nexus. This is achieved by combining five decades (1974–2024) of satellite data—including declassified HEXAGON KH-9, Copernicus Sentinel-1/2/3, COSMO-SkyMed radar, and PlanetScope’s Dove optical imagery—and on-the-ground observations (photographic and drone surveying). The observed landscape changes are categorised as “proxies” to infer the presence of the given land processes that they correlate to. The whole of southern Iraq is afflicted by dust storms and intense evapotranspiration; new areas are desertifying and thus becoming local sources of dust in the southwest of the Euphrates floodplain and close to the boundary with the western desert. The most severe transformations happened around springs between Najaf Sea and Hammar Lake, where centre-pivot and herringbone irrigation systems fed by pumped groundwater have densified. While several instances of run-off and discharge highlight the loss of water in the western side of the study area, ~5 km2 wide clusters of crops in the eastern side suffer from water scarcity and are abandoned. Here, new industrial activities and modern infrastructure have already damaged tens of archaeological sites. Future monitoring based on the identified proxies could help to assess improvements or deterioration, in light of mitigation measures.

Establishing a periodic SM model with Fourier analysis for enhancing global soil moisture forecasting

Accurately predicting global soil moisture (SM) is crucial for sustainable agriculture and water resource management. Recognizing the challenges posed by the heterogeneity of SM’s spatiotemporal variability, this study proposes a novel approach that leverages Fourier analysis to decompose the periodic fluctuations in SM, revealing underlying trends and cycles. This approach is integrated with Long Short Term Memory (LSTM) networks to enhance the accuracy of global SM prediction. Fourier analysis transforms time series data of SM into frequencies and amplitudes, capturing its intrinsic periodic characteristics. This transformation reveals both variable and invariant features representing changes within and between periods. By integrating these periodic features with sequence data and leveraging the memory and sequence learning capabilities of LSTM neural networks, the accuracy and reliability of global SM prediction can be enhanced. Our experiments on the LandBench1.0 dataset demonstrate that the proposed model reduces the root mean square error by to compared to the state-of-the-art methods. This study underscores that the LSTM with periodic features of SM can adapt to the inherent complex spatial-temporal patterns in SM dynamics, especially in scenarios characterized by rapid environmental changes and subtle temporal dynamics.