International Journal of Water (5 papers in press)

Regular Issues

• Flood risk assessment and hydrological simulation in the Upper Krishna River Basin, India  
  by Ravi Ande, Darshan Mehta, Vandana Maurya, Prashant Yadev Birbal, James Furze, Shashikant Verma 
  Abstract: Floods are among the most frequent and destructive natural disasters, and in India, they affect nearly 8% of the land area. Fluvial floods, mainly caused by heavy monsoon rainfall, occur annually, with Karnataka particularly North Karnataka being highly vulnerable due to the Krishna River. This study examines recent flooding issues in the Upper Krishna River system, where downstream areas face recurring floods from intense rainfall and inadequate real-time monitoring and forecasting systems. A key component in flood modelling is the use of digital elevation models (DEMs). For this research, DEMs from ASTER, SRTM, and Cartosat were evaluated across the basin. The Cartosat DEM proved most effective for flood risk assessment, offering higher accuracy for large-area studies. With advances in hydrological modelling, decision-makers can now access more spatially detailed data, improving real-time responses, recovery operations, and long-term flood mitigation strategies.
  Keywords: flooding; Krishna Basin; DEM; digital elevation model; hazard.
  DOI: 10.1504/IJW.2025.10073346
   
  
• Accuracy of rainfall prediction using deep learning based on a recurrent neural network with an LSTM layer method  
  by Rindra Yusianto, Rabei Raad Ali, Pulung Nurtantio Andono, Herwin Suprijono 
  Abstract: This study aims to improve the accuracy of rainfall prediction using deep learning on agro-industrial commodity planting land. The variables used are temperature, humidity, and rainfall data as training and test data. The deep learning model used in this study is a Recurrent Neural Network (RNN) with an LSTM layer. The study results show that the RNN model with the LSTM Layer in deep learning can predict rainfall based on temperature and humidity data. We found that the accuracy during the training stage using 375 LSTM layers was better with 100 epochs because the result was 91.25% compared to 1000 epochs, only obtaining an accuracy of 89.32%, and 1500 epochs of 87.27%.
  Keywords: deep learning; LSTM layer; RNN; prediction accuracy; rainfall prediction.
  DOI: 10.1504/IJW.2025.10073347
   
  
• Participatory decision-making process of water resources management: the arid and semi-arid region of the Iranian plateau  
  by Shahla Choobchian, Elham Akbari, Fatemeh Sheidaei, Mohammad Sadegh Allahyari, Enayat Abbasi, Tarek Ben Hassen 
  Abstract: Effective water governance requires building strong ties between and within institutions and communities horizontally, i.e., between organizations, cities, and rural communities, and vertically, i.e., from the urban to international levels. We aim to allow stakeholders to express their opinions by promoting participation in decision-making and water management. The participation of stakeholders is a prerequisite of all sustainable water resources management systems. In this regard, a new interpretation of the participation of the government and the public in water management is required, which needs fundamental revisions in the decision structure of institutions in charge of decisions for water resources and the redefinition of roles, authorities, and responsibilities. To implement a practical participatory approach, we must understand what tools and methods can help design and speed up the decision process. The current inquiry aims to explain the factors that influence the participatory decision process and use them to propose a model for identifying the relationships of all relevant stakeholders for their participation in decisions on water resources management in the agricultural sector.
  Keywords: Water governance; sustainable water resources management; decision structure; participatory approach; agriculture; Iran.
  DOI: 10.1504/IJW.2025.10073348
   
  
• Rainwater management in sponge cities based on SWMM  
  by Bingyang Ye, Mengran Shi 
  Abstract: The accelerating and expanding urbanisation has led to a marked increase in water scarcity and flood disasters. The study proposes an urban rainstorm and flood management model in conjunction with the pilot project of Sponge City. The model is designed to calculate and simulate precipitation, runoff, and infiltration. Through the precipitation distribution data and infrastructure planning of sponge city, it was concluded that the frequent occurrence of rainstorms in summer would lead to a rapid increase in urban rainfall. The situation of waterlogging areas and drainage sections would promote the development of waterlogging. Under the design with a recurrence interval of 20 years, the maximum rainfall intensity was 137.02 mm/h, and the average impermeability of the flooded area was 50.73%. After construction planning and drainage system renovation, the infiltration rate in some areas has increased by 30%, and the maximum flow velocity of underground pipelines was 2.83 m/s, with an overall rainwater utilisation rate of up to 93%. The proposed method is feasible and advantageous, providing technical reference for urban rainstorm and flood management, and improving the quality of sponge city planning and construction.
  Keywords: sponge city; rainstorm flood management model; waterlogging phenomenon; drainage system; facility construction.
  DOI: 10.1504/IJW.2025.10073497
   
  
• Groundwater quality issues and challenges for drinking and irrigation uses in Central Mahanadi Basin  
  by Noopur Tirkey 
  Abstract: It's important to learn more about water sources to figure out how likely they are to get dirty. The study looked at the Mahanadi Basin. Twenty-one samples of groundwater were taken at the beginning and end of the wet seasons. The amount of pH, EC, Cl?, SO42?, Ca2+, Mg2+, HCO3?, TH, Na2+, and K+ was checked. The Water Quality Index (WQI) showed that 5% of the samples taken before and after it rained were not safe to drink. To see if the area was good for growth, SAR, MHR, RSC, %Na, and Kelley's measure were used. 29% (MHR) and 71% (RSC) don't like it when it rains. Correlation Matrices, Box plots, Gibbs plots, Piper plots, and Wilcox plots were all used to show that the WQI was the same in every way, even in terms of how it worked chemically and physically.
  Keywords: water; groundwater pollution; water quality index; human health risk assessment.
  DOI: 10.1504/IJW.2025.10074013