Water Conservation and Management (WCM)

Agricultural drought poses an escalating threat to crop production and on-farm water management across the semi-arid Mediterranean basin, yet Al-driven spatially explicit prediction frameworks remain scarce for North African drylands. This study develops an explainable artificial intelligence (XAI) framework for agricultural drought prediction across five semi-arid governorates of central Tunisia over the 2001-2022 period. A multi-source dataset of 154,704 pixel-month observations (586 pixels at 0.05° spatial resolution) was assembled by integrating 15 predictor variables spanning meteorological, topographic, edaphic, and socioeconomic domains relevant to crop stress assessment. The drought target variable (Standardized Soil Moisture Index, SSMI) was derived exclusively from GLEAM v4.2a, while soil moisture predictors were drawn from the independent NASA POWER MERRA-2 atmospheric reanalysis to ensure methodological rigor and preclude mathematical circularity. Six machine learning models were evaluated (XGBoost, LightGBM, CatBoost, RF, BPNN, and LSTM) using a strict temporal split (training: 2001-2014; testing: 2015-2020; validation: 2021-2022) focused on an unprecedented multi-year drought episode. Results show that BPNN achieved the highest predictive performance on the test set ( R ^ 2 = 0.86 , SDI = 0.626), whereas XGBoost demonstrated superior generalization during the extreme 2021-2022 validation period (R ^ 2 = 0.696, SDI = 0.448) establishing it as the most robust architecture for drought-stress prediction under extreme climate conditions. TreeSHAP interpretability analysis identifies MERRA-2 soil moisture as the dominant predictor (26.0%), followed by temperature (14.2%), sand content (10.0%), and precipitation (8.6%), revealing that edaphic conditions strongly modulate drought severity and crop water stress in the Tunisian interior. These findings provide an operational smart decision-support tool for drought early-warning and precision irrigation planning in semi-arid agricultural regions.

Improving energy dissipation and efficiency in hydraulic structures such as spillways, sluices, and weirs, is important to prevent downstream erosion and structural damage under high velocity (supercritical) flows. However, conventional stilling basin designs often fail to optimize hydraulic jump characteristics, particularly under strong hydraulic jump conditions. This study experimentally evaluates the hydraulic performance of single sill and double sill configurations of stilling basin in a laboratory-scale stilling basin under strong hydraulic jump conditions (11 < F_{1} < 13.2) A series of physical model experiments was conducted by varying sill geometry, height, and spacing. The results showed that the double sill configuration in Series DS-5, combination of an ogee sill (Z_{1} = 6cm) and a trapezoidal prism sill (Z_{2} = 4.5cm) spaced at L_{1} = 80 cm, L2 = 0.5L1 (40 cm), provides superior hydraulic performance compared to single sill and horizontal apron configurations. Series DS-5 achieved the lowest y2 and yj, the highest relative energy dissipation ((Eo-E2)/Eo = 81.42%), and average energy efficiency (E_{2} / E_{1} = 51.31%) The enhanced performance is attributed to intensified turbulence interaction and improved hydraulic jump control induced by combined sill geometry. Regression based relationships between dimensionless hydraulic variables were also developed to support predictive design. This research contributes a practical and compact stilling basin design for high energy flow conditions, offering improved efficiency and potential application in hydraulic structure design.

Reservoir sedimentation is continuing to be an essential obstacle to the long-term performance of dams and water infrastructure, with consequences for hydropower generation, flood control, and environmental integrity. This bibliometric study analyzes 118 peer-reviewed publications from 2014 to 2024, retrieved from Scopus, focusing on sediment flushing and reservoir sedimentation. Using Excel, VOSviewer, RStudio, and MapChart, the study maps publication trends, methodological diversity, co-authorship networks, and institutional contributions. The dataset reveals a marked increase in publication activity over the study period, with numerical modeling consistently leading in frequency and application. This upward trend reflects growing global attention to sediment-related challenges and the expanding role of computational strategies in addressing complex hydraulic phenomena. Further efforts could emphasize integrating field-based validation with existing models to enhance reliability. Expanding comparative studies across diverse regions may also improve the adaptability of sediment management strategies.

This study examined the factors influencing household access to domestic water across four Local Government Areas (LGAs) in Oyo Zone, Oyo State, Nigeria, using exploratory factor analysis. Data were collected through structured questionnaires administered to residents and analysed using principal component extraction with varimax rotation. Results showed that five to six dominant factors explained more than 60% of the total variance in water accessibility in each LGA, indicating strong explanatory power. Key shared determinants across the zone included proximity to water sources, reliability of supply, and water quality, reflecting the importance of availability, consistency, and safety in household water access. Distance was the most influential factor in Atiba (Rotated Component Matrix value = 90.0), while water quality had the strongest effect across Oyo Zone (RCM = 87.2) Household preferences also significantly shaped access patterns, particularly in Oyo Zone (RCM = 92.3) driven by affordability, convenience, and cultural considerations. Distinct local challenges were identified, including high patronage levels in Afijio, physical access constraints in Atiba and Oyo Zone, and limited piped water connectivity in Oyo Zone. The findings reveal considerable spatial variation in water access conditions within the same geopolitical region. The study concludes that improving equity in water access requires location-specific interventions. Recommended actions include expanding piped water infrastructure, improving supply reliability and quality, reducing travel distance to water points, and addressing physical access barriers. Incorporating household preferences into water planning can enhance sustainability and user acceptance while strengthening water governance and equitable domestic water provision.

Various anthropogenic activities result in a continuous discharge of contaminants of emerging concern (CEC) into the natural environment. The remediation of these substances is an emerging concern to safeguard life on Earth. CECs and their removal have become a growing concern, which is being investigated in different wastewater treatment processes. Therefore, a comprehensive study is required to find appropriate low-cost, eco-friendly, and efficient technology to remediate different kinds of CECs from wastewater. The partial removal of CECs, such as endocrine-disrupting compounds, pharmaceuticals, personal care products, and heavy metals have been discussed in this review. The results of the review show that the majority of the research in recent years has focused on using phase-changing processes, including adsorption in different solid matrices and membrane processes, followed by biological treatment and advanced oxidation processes. This paper focuses on the type of CEC being removed, the conditions of the process, and the outcomes achieved. The main trends in the field are also highlighted along with perceptive comments and recommendations for further developments, as well as the identification of the current knowledge gaps and future research directions related to the application of these technologies for water treatment and restoration.

Quantitative water balance assessments of traditional irrigation systems remain scarce, particularly for systems experiencing chronic water deficits driven by institutional rather than climatic factors. This study applied the F.J. Mock rainfall-runoff model to Subak Balangan, which has experienced a 20-year-long continuous water shortage due to an inter-district allocation dispute. Using 10-year climate data (2015-2024) and independent field validation measurements (March-July 2025, 10 half-monthly periods), the model achieved moderate-to-good performance (NSE = 0.521, R ^ 2 = 0.833 RMSE = 0.01 m³/s). Water availability at 80% reliability ranged from 0.001 to 0.012 m³/s across 24 half-monthly periods. A comparative water balance analysis of two crop rotations revealed that the existing flower-cassava rotation experienced deficits in 75% of periods (maximum 0.017 m³/s), while a hypothetical paddy-paddy-maize rotation would produce deficits in 91.7% of periods, with 2.96-fold higher peak demand (maximum 0.074 m³/s). These results quantify the magnitude of water stress under existing institutional constraints and demonstrate the limitations of paddy-based crop intensification as a water management strategy in this specific context. Beyond empirical findings, the study advances two theoretical contributions: Institutional Hydro-Decoupling (IHD), extending Ostrom’s common-pool resource theory to conceptualise conditions where governance failure structurally severs the link between catchment hydrology and effective irrigation supply; and Hydro-Adaptive Cropping Moderation (HACM), extending autonomous adaptation theory to identify farmer-led rotation adjustment as an endogenous demand-side mechanism that partially compensates for institutionally-suppressed water availability. The findings provide a baseline water balance reference for ongoing allocation dispute resolution and highlight the need for multi-Subak comparative studies to establish broader patterns.

Water quality typically determines diarrhea incidence rates, but findings vary widely across studies. This study want to synthesize those findings to find the relationship between water quality and diarrhea incidence. Method which utilized in this meta-analysis following PRISMA guidelines. The included study is observational studies (both case-control and cross-sectional studies) from databases including PubMed, ScienceDirect, Scopus, and Google Scholar. Only studies that report adjusted odds ratios (aOR) are included. Random-effects model was applied due to high heterogeneity. The results showed that water quality and diarrhea incidence are not significant ( a0R = 0.63; 95% CI: 0.23-1.74; I ^ 2 = 91.4% ) Same results also found in subgroup analyses. This means that the relationship between water quality and diarrhea is not simple as it seems. Other factors such as sanitation, hygiene habits, and food contamination also influence diarrhea. Therefore, improving only on water quality to reduce diarrhea may not be sufficient. More integrated approach is needed to address diarrhea sustainably.

The study investigated how suspended sediment concentration (SSC) and environmental factors relate in the coastal waters of Lhok Paroy in Aceh Besar, Indonesia. Samples were taken from ten sampling locations at high and low tide. SSC varied from 150 mg/1 to 220 mg/l when the tides went out and had a similar range of 110 mg/l to 220 mg/l when it came in. Higher SSC were found near the mouth of the river as well as in areas of high human activity. The other environmental variables also had considerable variability that was dependent on tidal phase as well as location. Luminosity ranged from 4.6 m to 8.9 m; salinity ranged from 28 ppt to 31 ppt; temperature ranged from 30 °C to 33 °C; dissolved oxygen (DO) ranged from 5.5 mg/l to 11 mg/l; and pH ranged from 6.5 to 7.9. Through Principal Component Analysis (PCA), PCA identified the strongest relationships between SSC and pH (Station 2) and SSC and temperature (Stations 3 and 4). And, negative correlations were also found between SSC and DO, and SSC and luminosity. As a result, the amount, or concentration of SSC is affected by tidal hydrodynamics, sediment resuspension and mineral interactions. The work provides insight into the complex interaction between physicochemical factors that impact water quality in tropical coastal ecosystems and helps form a scientific basis for future sediment management and environmental monitoring plans for the Lhok Paroy area.

Part of the hydrological cycle, soil infiltration serves as a pivotal indicator for evaluating water availability potential within the Kalisari Sub-watershed. By employing modeling techniques to analyze its spatial distribution, this study aims to forecast the watershed’s capacity to sustain agriculture by 2030. The research area spans approximately 5,000 hectares, encompassing five distinct land uses: P1 (mahogany-coffee agroforestry), P2 (pine-coffee agroforestry), P3 (shrub), P4 (dryland), and P5 (paddy fields). A comprehensive investigation, involving 43 observation points, was conducted to facilitate modeling and accuracy assessment through techniques such as Kappa Accuracy Assessment, implemented using QGIS 2.18 and ArcGIS 10.4. Precipitation data obtained from a rainfall simulator were utilized to simulate rainfall intensity and measure soil infiltration rates. Correlation-regression analyses were performed using R Studio, employing the Pearson correlation method to ascertain the relationship between soil parameters and infiltration. Projections indicate that by 2030, the soil infiltration rate within the Kalisari Sub-watershed is expected to be classified as “very fast” (>250 mm h-¹). Kappa accuracy modeling demonstrated an accuracy rate of approximately 82.74%, validating the reliability of the modeling approach. Furthermore, the study identified a significant interaction between land use patterns and soil properties, resulting in a dynamic decrease in infiltration rates of approximately 13.47% during 2021 to 2030 within the Kalisari Sub-watershed. However, the decrease is not significant because the lowest infiltration rate is around 328.51 cm h¹, thus indicating that the hydrological capacity of the entire watershed land is likely to remain stable under the projected land use scenario and is able to support agricultural systems until 2030.

The purpose of this study is to examine the current state of water quality in the Nura-Sarysu river basin and predict its future trends. To achieve this purpose, a comprehensive monitoring approach has been used, including satellite imagery and machine learning. For instance, Sentinel-2 (10 m resolution) and Landsat 8/9 (30 m resolution) satellite imagery have been used for estimating the Normalized Difference Water Index (NDWI). This index has helped in identifying water bodies in the study area. In addition, the study has used critical water quality parameters, including heavy metals (Cd 0,15 mg/L, Pb 0,10 mg/L), biochemical oxygen demand (BOD5 10-15 mg/L), and salinity (0,5 g/L). It has been found that there are significant changes in the water quality parameters in the Nura-Sarysu river basin compared with the natural state. A significant correlation (R2 ≈ 0,82-0,87) has been found between satellite imagery and sensor-based estimations. In addition, a machine learning model has also been used for estimating temporal changes in water quality parameters. This model has shown high accuracy (R2 ≈ 0,88, RMSE ≈ 0,09-0,12). This study has shown the potential of the proposed approach for improving the assessment of water quality parameters in the Nura-Sarysu river basin in Kazakhstan.