International Journal of Water (10 papers in press)

Regular Issues

• Cownomics and water ecology: towards a sustainable rejuvenation process in India  
  by Rabinarayan Patnaik 
  Abstract: Water is the lifeblood of the world, and global civilization cannot sustain itself without relying on it. However, this invaluable and indispensable resource has been wasted and rendered unusable due to various man-made and natural reasons. This has led to a deadly scenario known as the water crisis, which has become increasingly severe globally, including in India. Many traditional methods for water rejuvenation exist, but none have been deemed sustainable and eco-friendly solutions. In contrast, Cownomics is the only in-situ water treatment process that has begun changing perceptions of these technologies. With encouraging results in various water rejuvenation projects in lakes and rivers across India, it has been applied to rejuvenate six heritage water bodies situated in coastal areas of Cuttack and Puri in Odisha (an Eastern state in India). An outcome based study like this can be useful for policymakers and stakeholders in the future.
  Keywords: cownomics; pollution; rejuvenation; sustainability; water crisis; water policy; ground water; water stress; heritage water bodies; vedic treatment.
  DOI: 10.1504/IJW.2025.10076758
   
  
• Riverine flood hazard zonation and its spatio-temporal variation in the Upper Brahmaputra River Valley, Assam  
  by Arunima Nandy, Avinash Kumar 
  Abstract: Riverine flooding is a major natural hazard, particularly in regions situated on alluvial plains. The upper Brahmaputra River valley in Assam, India, experiences severe and recurrent floods almost every year. This study assesses flood hazard zonation and its spatio-temporal variation from 2001 to 2021 using moderate resolution imaging spectroradiometer (MODIS) imagery and a multi-criteria weighted overlay approach. Key factors such as elevation, slope, topographic wetness index, curvature, vegetation, proportion of water bodies, and land use/land cover were integrated to generate flood hazard maps. The region was classified into five hazard zones: very low, low, moderate, high, and very high. Results indicate a significant expansion about 25.54% in high and very high flood-prone areas over the two decades. Model outputs were validated using historical flood records, ensuring reliability of the hazard assessment. The study highlights increasing flood susceptibility and provides valuable insights for flood management and mitigation planning.
  Keywords: curvature; flood; landuse/landcover; MODIS; moderate resolution imaging spectroradiometer; multi-criteria analysis; spatio-temporal change; topographic wetness index; weighted overlay.
  
  
• Delineation of groundwater prospect zones using AHP and weighted overlay method in Gaya district Bihar, India  
  by Tarun Kumar, Vaishnavi Swami, Sunita Singh 
  Abstract: Groundwater is vital for environmental sustainability and socioeconomic development, but overuse and climate change threaten its availability. This study identifies groundwater prospect zones in Gaya, a drought-prone district in Bihar, India, which holds religious significance for Buddhists and Hindus. Using Remote Sensing (RS) and geographic information systems (GIS), 10 thematic factors geomorphology, lithology, lineament density, slope, land use/land cover (LULC), soil, rainfall, drainage density, topographic wetness index (TWI), and roughness index were analysed. Weights were assigned via the analytical hierarchy process (AHP), and overlay analysis produced a groundwater prospect map with five categories: good, good to moderate, moderate, low, and very low potential. Results show 43.70% of the area has good to moderate potential, 25.45% moderate, while 14.03% and 7.15% fall under low and very low prospects, respectively. These findings offer a scientific basis for sustainable groundwater management, aiding efficient resource utilisation in the region.
  Keywords: groundwater prospect zones; AHP; analytical hierarchy process; remote sensing; GIS; geographic information system; weighted overlay analysis.
  DOI: 10.1504/IJW.2026.10078283
   
  
• Prediction model of groundwater conditions based on mine geological environment  
  by Xiao Kong 
  Abstract: This study addresses the need for accurate groundwater prediction in mining areas to ensure safety and resource management. Traditional models struggle with complex spatial-temporal variations and sudden changes. To overcome these limitations, a novel model combining Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) is proposed. The model achieves high performance with a fitting degree of 0.92, accuracy of 0.91, and efficiency of 0.86. In real-world scenarios, it reaches up to 0.99 accuracy, outperforming existing models. The results highlight its effectiveness in improving prediction accuracy and efficiency, advancing intelligent groundwater forecasting technologies.
  Keywords: ARIMA; autoregressive integrated moving average; LSTM; long short-term memory; groundwater prediction; features.
  DOI: 10.1504/IJW.2025.10078759
   
  
• Analysing climate change impacts on the Cauvery river basin using machine learning techniques  
  by Ravi Ande, Darshan Mehta, Shivendra Jha, Hardik Patel, Upendrasingh Singh 
  Abstract: General circulation models (GCMs) are widely used for regional climate change assessments, making the selection of reliable models crucial for impact studies. This study evaluated 26 CMIP5 and 16 CMIP6 GCMs for simulating temperature and precipitation over the Cauvery Basin, a region prone to floods and droughts in southern India. Using multi-criteria compromise programming, the models were ranked based on predictive performance, identifying CanCM4 and CanESM5 as the most credible GCMs. To improve regional climate projections, GCM outputs were downscaled using statistical and machine learning approaches. A novel extreme gradient boosting decision tree (EXGBDT) model was developed and compared with existing methods using historical daily observations. Results showed that EXGBDToutperformed other techniques, achieving R2 and NSE values of 0.750.85 and a mean variance of about 15%. The model also better reproduced CLIMDEX indices and demonstrated strong stream-flow prediction capability, with NSE and R2 values above 0.7, highlighting its suitability for climate and hydrological applications.
  Keywords: GCMs; general circulation models; climate change assessment; statistical downscaling; machine learning.
  DOI: 10.1504/IJW.2026.10079696
   
  
• Predictive analysis of sedimentation and capacity loss in the hydraulic structure at Maithon reservoir using hybrid STGCN and MOO with NSGA-II model  
  by Ankita Sharma, Maya Rajnarayan Ray, Umank Mishra 
  Abstract: The Maithon Reservoir provides essential water storage for hydropower generation, flood control and water supply. A comprehensive dataset from the Maithon Reservoir Survey (19552019) was used to investigate sediment accumulation along with capacity reduction in Maithon Reservoir. This study proposes a hybrid spatio-temporal graph convolutional network (HST-GCN) and multi-objective optimisation (MOO) with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) model. The performance of the model was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), R2 and Nash-Sutcliffe efficiency (NSE). The trained model was used to forecast future values from 2020 to 2100. The proposed model is contrasted with artificial neural networks (ANN), support vector machine (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) to assess predictive performance. The proposed model obtained an R2 value of above 0.99 and minimal error values, which proved its reliability for reservoir management.
  Keywords: Maithon reservoir; sedimentation prediction; capacity loss; hydraulic structure; hybrid model; future prediction.
  DOI: 10.1504/IJW.2026.10080323
   
  
• Design of an IoT-based continuous water quality monitoring system for Northern Nigerian Rivers  
  by Samaila Kasimu Ahmad, Yogesh Singh Rathore 
  Abstract: For safeguarding public health, the stability of the ecosystems in Northern Nigeria, it is necessary to monitor the quality of river water on a continuous basis. Conventional sampling techniques are mostly based on laboratory; where, it has low temporal frequency, and are expensive with delay reporting. Meanwhile, Internet of Things (IoT)-based sensing is limited by point-based monitoring, where it estimates non-optical parameters weakly. To address these drawbacks, the current research proposes a hybrid IoT-remote sensing water quality monitoring framework, combining in-situ sensor networks with multispectral satellite images by using a new IoT-based dynamic satellite calibration and fusion (IoT-DSCF) layer. The workflow comprises IoT sensing, satellite data pre-processing, adaptive calibration through real-time ground truth, weighted fusion, and cloud-based visualisation with an alert mechanism. The experimental findings results with a turbidity RMSE of 2.2 NTU and alert accuracy of 96.2%, indicating a substantial improvement over the baseline methods.
  Keywords: water quality monitoring; IoT sensors; remote sensing; data fusion; IoT-DSCF; calibration model; turbidity estimation; pollution alerts; northern Nigeria rivers.
  DOI: 10.1504/IJW.2026.10080834
   
  
• Efficiency, equity, and ROI in privatised water utilities: global evidence with insights from the Balkans  
  by Petros Lois, Spyros Repousis 
  Abstract: Purpose: This paper examines the return on investment (ROI) of privatised water utilities globally, focusing on efficiency, equity, and governance, with particular attention to the Western Balkans. Design/ methodology/approach: A systematic review synthesises 50 peer-reviewed studies published between 1990 and 2025, integrating financial performance, efficiency assessments, and comparative case studies. Findings: Privatisation often generates high initial investor returns but mixed outcomes for efficiency and consumer welfare. Strong regulatory frameworks are critical for transparency, equity, and reinvestment. In the Western Balkans, small hydropower investments initially produced strong subsidy-driven returns but were weakened by policy volatility, governance shortcomings, and social opposition. Equity remains a persistent challenge, particularly in lower-income settings. Practical implications: Effective regulation, transparency, and stakeholder engagement are essential to balance investor returns and public welfare. Originality/value: The study integrates global evidence with underexplored Balkan experience, offering insights for sustainable and equitable water governance.
  Keywords: water privatisation; ROI; return on investment; efficiency and equity; regulatory frameworks; PPPs; public-private partnerships; western Balkans; SHPPs; small hydropower plants; water governance.
  DOI: 10.1504/IJW.2026.10080859
   
  
• Integrated model of water management in green schools: governance, teaching and learning, community partnerships, and operations  
  by Meenal Arora 
  Abstract: In an era characterised by industrialisation and resource depletion, green schools are essential for fostering environmental stewardship and empowering young people to harvest and conserve water in schools. This study explores the implementation of sustainable water management practices within school environments, focusing on governance, curriculum, community engagement, and operations. Using a mixed-methods approach, the research analyses data from school audits, surveys, and case studies to identify best practices in adopting green initiatives. It explores interdisciplinary activities, such as rainwater harvesting, water quality citizen science project, and collaborative efforts with parents and local communities to foster responsible water consumption. The research also investigates the role of schools in reducing water waste through efficient infrastructure, including water-efficient building systems. By integrating disciplines such as science, economics, and social studies, the study aims to promote water conservation awareness among students. The findings advocate for policy changes to institutionalise sustainable water management in schools.
  Keywords: green schools; sustainability; governance; teaching and learning; community partnerships; water management.
  DOI: 10.1504/IJW.2026.10080912
   
  
• An integrated modelling approach for sustainable development of the UNESCO classified ecosystem (Ichkeul Lake, North Africa)
  by Béchir Béjaoui 
  Abstract: This paper presents the results of a simulation study on the impact of integrating water intake and output for and from Lake Ichkeul, a Ramsar and UNESCO reserve wetland in North Tunisia. Three scenarios were simulated over nine years, with varying amounts of freshwater injected into the lake from different dams. The study showed that a non-active lake management policy would result in severe ecosystem degradation, with the lake eventually becoming a salt marsh. Under the status quo, the area and density of Potamogeton would decrease dramatically and the number of migratory birds would rapidly decline. The second scenario would allow the ecosystem to be barely resilient and maintain itself, while the third scenario would lead to long-term sustainability and stabilize all ecosystem components. The results of the present study have implications for water management policies and the preservation of the unique biodiversity of the Ichkeul Lake ecosystem.
  Keywords: Ichkeul Lake; Water management; Ecosystem resilience; Wetland conservation; Scenarios.