Inter‑basin water transfer (IBWT) is increasingly used to address water scarcity in arid and semi‑arid regions, yet these projects involve complex trade‑offs among economic, social, environmental and technical objectives. This study develops a comprehensive decision‑making framework for selecting the optimal discharge of a water transfer tunnel from the Doogh River to the Gorganrud River, upstream of the Bostan Dam in Golestan Province, Iran. A coupled simulation‑optimisation approach is employed, integrating the Water Evaluation and Planning (WEAP) model with three Multi‑Criteria Decision‑Making (MCDM) methods: AHP, TOPSIS and VIKOR. Six discharge scenarios (Q‑0.19, Q‑0.57, Q‑0.76, Q‑0.91, Q‑1.06 and Q‑2.00 m³/s) are evaluated against twelve criteria across four sustainability dimensions. The baseline scenario (without water transfer) reveals a substantial unmet water demand of 30.06 MCM/year, underscoring the need for the transfer. Results show that while the highest‑discharge scenario (Q‑2.00) performs best from a purely technical perspective, the intermediate scenario (Q‑0.91) provides an optimal balance, achieving 98
Satluj and Beas, Indus basin Himalayan glaciers fed rivers, have seen astronomic magnitude of human interference due to agricultural intensification, population pressure, urbanization and industries which subsequently deteriorates the water quality of the river systems. It is therefore, important to realize the dynamics and the need to regularly monitor the river water bodies to explore the influence of changes on the collective water quality using robust analytical approaches that allow accurate predictive modeling of water quality for better river water management. Such approaches provide framework for better comprehension of complex river water quality data, enable understanding of data structure/pattern and also identifies the key parameters influencing the water quality. This work describes the application of two such approaches: multiple linear regression (MLR) and Random Forest regression (RFR) for predicting the water quality index (WQI) for the river bodies of Indian Punjab over the conventional indexing procedure using fixed set of parameters. MLR and RFR provided non-linear and linear wrapper framework around computed CCME WQI to establish the relation between WQI and variables. This analysis besides confirming the importance of variables involved in the CCME WQI computation such as Biological Oxygen Demand (BOD), Dissolved Oxygen (DO) and Total coliform (T.Coli), also identified other variables important for predicting the status of water quality. Models were able to predict the WQI with RFR had a higher predictability (R2 = 0.97) than MLR model (R2 = 0.64). The approach presented here offers quick alternative to procedural WQI computation, which allows data exploration for prediction compared to following the set steps for sub index formulation of the constituent water quality variables and their aggregation.
Increasing urbanization and water scarcity have intensified the need for advanced wastewater treatment technologies capable of producing high-quality effluent suitable for reuse. This study investigates the performance of an integrated Media Filtration–Membrane Bioreactor (MF–MBR) system for domestic wastewater treatment under conditions representative of Indian municipal wastewater. A pilot-scale experimental setup consisting of standalone Media Filtration (MF), standalone Membrane Bioreactor (MBR), and hybrid MF–MBR configurations were operated continuously for 90 days. The treatment performance was evaluated in terms of organic pollutant removal, suspended solids reduction, nutrient elimination, pathogen removal, membrane fouling mitigation, energy consumption, and operational sustainability. Results demonstrated that the hybrid MF–MBR system achieved superior treatment efficiency with average removals of 98.3
In Sub-Saharan Africa, competition over scarce irrigation water is shaped not only by physical water availability but also by institutional arrangements, power relations, information access, and participation. This study investigates the systemic determinants of social exclusion in water allocation within Ethiopia’s Awash-Awash Sub-basin using a mixed-methods approach and Multi-Criteria Decision Analysis (MCDA). Results showed that state farms had the highest Social Inclusion Index (Si = 0.82), followed by private investors (Si = 0.75), Irrigation Water Users Associations (IWUAs) (Si = 0.48), and smallholder farmers (Si = 0.42). Among smallholders, conflict and conflict resolution (Relative Importance Index (RII) = 0.92), power dynamics and trust (RII = 0.89), and knowledge and information access (RII = 0.85) were particularly important exclusion factors. In contrast, actor size (RII = 0.98) and financial constraints (RII = 0.96) primarily constrained IWUAs. The findings indicated that social exclusion is reinforced through four interrelated dimensions: knowledge and communication, participation and representation, resource and financial constraints, and organizational, cultural, and political dynamics. The study concludes that inequitable water allocation is a systemic governance outcome rather than a consequence of physical scarcity alone. The findings also have implications for Sustainable Development Goal (SDG) 6.5, which calls for integrated water resources management (IWRM), by demonstrating the importance of inclusive participation, information access, institutional coordination, accountability, and conflict resolution. Strengthening the organizational and financial capacity of IWUAs, improving transparency in water-allocation information, and establishing inclusive local monitoring mechanisms can support more equitable water governance in the Awash-Awash Sub-basin.
Rapid urban expansion in tropical mining cities is increasing stormwater volumes, shortening runoff response times and exposing drainage systems to recurrent surcharge. This study evaluates two contrasting approaches for urban stormwater discharge prediction in the Musonoi sub-catchment of Kolwezi, Democratic Republic of Congo: a physics-informed one-dimensional Convolutional Neural Network (CNN), in which SCS-CN rainfall excess is incorporated as an input feature to constrain learning with hydrologically meaningful runoff-generation processes, and the classical Soil Conservation Service (SCS) triangular Unit Hydrograph (UH). Topographic and hydraulic survey data, SRTM-derived terrain attributes, catchment morphometry, land-use-based runoff coefficients and SCS Curve Number parameters were integrated with stochastic daily rainfall forcing for 2021–2023 and a January-March 2024 forecast window. The CNN used a 14-day sliding input window comprising rainfall, SCS-CN runoff excess and antecedent rainfall memory, thereby constraining data-driven learning within hydrologically meaningful runoff-generation processes. The CNN consistently outperformed the UH across calibration, validation and independent testing, with NSE values of 0.783, 0.791 and 0.802, respectively, and lower RMSE than the UH. The UH generated a conservative forecast peak of 88.0 m3/s, whereas the CNN produced a moderated operational peak of 18.0 m3/s. This difference reflects the contrasting design philosophies of the two models, with UH providing a conservative event-based design estimate and CNN representing a continuous operational forecasting estimate. Both exceed the primary collector capacity of 4.57 m3/s during intense wet-season events, indicating structural vulnerability in the current drainage system. The results support a tiered modelling strategy in which the UH is retained as an upper-bound design tool and the CNN is used for daily operational forecasting, maintenance scheduling and flood-risk communication in rapidly urbanising tropical catchments.
