Freshwater degradation in agro-industrial basins has intensified worldwide, demanding robust analytical frameworks capable of disentangling complex pollutant interactions. This study evaluates the spatiotemporal dynamics of water quality in the Rio Dourados Basin, Mato Grosso do Sul, Brazil, one of the most intensively cultivated agricultural regions in the country, using four years of monitoring data (2016–2019) obtained from IMASUL. Twenty physicochemical and microbial parameters were pre-processed through outlier detection using the interquartile range (IQR) method, followed by robust scaling and normalization. Multivariate techniques, including principal component analysis (PCA), Kendall rank correlation, and hierarchical cluster analysis, were applied to identify the dominant hydrochemical processes, while ensemble machine-learning regressors (XGBoost, Random Forest, CatBoost, and SVR) were used to model and predict water-quality variability. PCA revealed that mineral–solid enrichment, oxidized-nitrogen inputs, and organic–microbial loading explained more than 75% of the total variance. Among the evaluated models, XGBoost achieved the best predictive performance (R² = 0.789; RMSE = 3.04), with total microbial count, turbidity, and total phosphorus identified as the most influential predictors. The results indicate that agricultural runoff diffusion, sediment resuspension, and microbial proliferation are the principal mechanisms driving hydrochemical fluctuations, particularly in downstream areas. By integrating multivariate interpretation with artificial-intelligence-based prediction, this study proposes a replicable methodological framework that improves diagnostic precision and supports adaptive, data-driven management of freshwater systems under increasing agricultural and climatic pressures.
This study examines long‑term trends in electricity consumption in Japanese water supply utilities, focusing on two groups: Utilities of Consolidation (U of C) and Utilities with a Stable Business Environment (U‑SBE). Using 20 years of nationwide data on electricity usage and pump characteristics, the research evaluates how structural and operational factors influence CO₂‑related energy reductions. For U of C, utilities that reduced electricity consumption typically achieved decreases in prime mover output and pump numbers, mainly through restructuring of intake, transmission, and distribution systems. Larger utilities benefited more from consolidation, whereas smaller ones often experienced increased consumption. For U‑SBE, significant reductions in electricity and unit electricity consumption were associated with decreases in prime mover output and pumped volume, supported by effective management and a higher proportion of technical staff. Comparative analysis revealed that U of C reductions were driven largely by operational changes such as altered water routing, while U‑SBE reductions involved both structural and operational improvements. Overall, the results clarify how facility configurations, pump system design, and management capacity influence electricity consumption, offering practical insights for achieving carbon‑neutral water supply operations.
This study evaluates the hydraulic performance of four structural flood-mitigation alternatives using a two-dimensional HEC-RAS model for a 50 km reach of the Koshi River between Chatara and the Koshi Barrage, Nepal. A 100-year design flood (27,401 m³ s⁻¹), estimated via Generalized Extreme Value flood-frequency analysis, was applied as the upstream boundary condition for existing-condition and alternative scenarios. The alternatives comprise progressive embankment raising (A1, passive), diversion through an abandoned Koshi channel (A2, active), an off-stream flood storage basin (A3, active), and an upstream flood detention dam (A4, active). Modelling results were compared in terms of water surface elevation, flood depth, velocity, and hydrographs. Results show the existing embankments can contain the 100-year flood without overtopping, though several reaches remain vulnerable to erosion and channel migration. Diversion reduced peak discharge by approximately 42% (to 16,000 m³ s⁻¹) and the storage basin by approximately 20% (to 22,000 m³ s⁻¹); embankment raising produced no reduction. The detention dam attenuated peak outflow by approximately 49% (to 14,000 m³ s⁻¹), indicating strong potential for flood-peak reduction through temporary storage. This framework supports integrated flood-risk management planning for the Koshi and other sediment-laden alluvial rivers, pending complementary economic, environmental, social, and institutional feasibility assessment.
High-mountain river basins are critical for downstream water security, yet climate change is altering snow storage, precipitation, and runoff regimes across their steep elevation gradients. The Koshi River Basin (KRB), a large transboundary Himalayan system, exemplifies this challenge, but how CMIP6-driven forcing reorganizes tributary-scale water-balance partitioning and hydrological resilience across the KRB remains poorly understood, since existing studies mostly rely on pre-CMIP6 scenarios or basin-outlet responses, with little attention to internal partitioning or rainfall-versus-snow tributary contrasts. This study quantifies baseline water-balance structure and evaluates how future climate change modifies the partitioning of precipitation into evapotranspiration, snowmelt, and water yield across six major KRB tributaries and the basin outlet, using a multisite-calibrated ArcSWAT model at seven gauging stations forced with bias-corrected CMIP6 projections from ACCESS-CM2 and MPI-ESM1–2-LR (SSP2–4.5, SSP5–8.5; 2035–2060 relative to 1984–2009). Baseline conditions show a runoff-dominated, energy-limited regime, with water yield consistently exceeding evapotranspiration and seasonal snowmelt contribution in high-elevation tributaries. Future projections indicate robust warming and scenario-dependent precipitation responses, producing spatially heterogeneous changes in water yield, declining snow buffering, and increasing interannual variability. Precipitation elasticity of runoff often exceeds unity, and basin-outlet responses mask substantial tributary-scale contrasts, underscoring the need for tributary-focused water-balance assessment in Himalayan climate-adaptation planning.
