International Journal of Water (3 papers in press)

Regular Issues

• Integrated Water Resource Management in Iraq: balancing supply, demand, and distribution losses  
  by Sarah Al-Jarjees 
  Abstract: Iraq faces significant water scarcity challenges due to a combination of external and internal factors, including upstream water diversions, ageing infrastructure, and climate variability. This study provides an integrated analysis of Iraq's water resource management, examining the balance between supply, demand, and water losses in both northern and southern regions. Using data from official sources, the study highlights the declining water imports from the Tigris and Euphrates, regional disparities in water production, and the rising inefficiencies in water distribution networks. The analysis emphasises the need for infrastructure modernisation and investment in alternative water technologies to ensure Iraqs water security. Recommendations include upgrading distribution systems, expanding desalination capacity, and adopting Integrated Water Resource Management (IWRM) strategies to mitigate the impacts of water scarcity and improve long-term sustainability.
  Keywords: water resources management; water security; water scarcity; Iraq; sustainable water management; climate change impact; water conservation; potable water production.
  DOI: 10.1504/IJW.2025.10070785
   
  
• Ensemble learning based flood forecasting models for the northern districts of Bihar  
  by Vikas Mittal, T.V. Vijay Kumar, Aayush Goel 
  Abstract: During past few decades, global warming and climate change have engendered certain change in relationships between various environmental parameters. There has been a significant increase in the number of occurrences of various natural hazards, such as floods, which is being observed globally. In recent years, floods have become the most regularly occurring natural hazard in India that has resulted in continuous and significant loss to lives and property. Continuously changing weather patterns make forecasting of such hazards increasingly difficult. In order to capture the changing dynamics of key weather parameters to improve forecasting, many flood forecasting models that use machine learning techniques, have been proposed in the literature. In this paper, flood forecasting models, using ensemble learning techniques, have been proposed that seek to enhance the flood forecasting capability of the existing machine learning based flood forecasting models. Experimental results have shown that these proposed models have performed better than the existing flood forecasting models using machine learning techniques on key performance metrics such as accuracy, precision, recall, F-measure and AUC-ROC.
  Keywords: global warming; natural hazards; disaster; floods; forecasting; ensemble learning.
  DOI: 10.1504/IJW.2025.10071809
   
  
• Soil moisture and geology factors implementation in enhancing water-harvesting modelling in semi-arid area: a case study in Andalusia  
  by Reham Hassan Dweib 
  Abstract: Rapid population growth coupled with climate change has exacerbated concerns about water scarcity globally, especially in regions such as Spain, where the population will exceed 47 million in 2024. Many countries are resorting to methods of collecting water, especially rainwater, to utilise it. During dry seasons and to avoid floods. Although there are studies on determining the locations of dams using the surface runoff or numerical curve method, which depends on the elements of rainfall (R), land use (LU), soil characteristics (SO) and slope (S), these studies neglected the effect of soil moisture (SM) and the geological factors (GF) of the region. By utilising geographical information system (GIS) and the soil conservation service curve number (SCS-CN) model, the research aims to comprehensively examine environmental influences on runoff in Andalusia, which is characterised by its unique combination of climatic conditions, topography, soil compositions, and land use patterns. The semi-arid Mediterranean climate of Andalusia, characterised by unpredictable rainfall, provides a suitable environment for studying rainwater harvesting systems. Analysing data from 2010 to 2020, the study used the SCS-CN model, which is known for its applicability in watersheds with variable rainfall, moderate slope, and limited data accessibility. Two scenarios/models were examined - one including all factors, including SM and GF, and the other excluding SM and GF. The first model includes R-LU-SO-S in addition to SO and GF. The area of the area in which these conditions are met was estimated at 8069 km², in the Sevilla, Huelva, and Cadiz region. The most suitable areas are Sevilla, Huelva. Since the southern part of Sevilla has a high slope, wet soil, and impermeable rocks such as dolomite, limestone, flint, schist, gneiss and clayey rocks. It greatly helps surface runoff, in addition to being suitable for building dams and reservoirs. While the second model included R-LU-SO-S. The area of the preferred area was estimated at 4812 km², in the areas of Sevilla, Huelva, Cadiz, Malaga, and Granada. The Malaga, Granada area was ignored because it is close to faults and contains dry soil and permeable rocks, which allow water to be absorbed into the groundwater. The results reveal that the combination of SM and GF significantly improved the replay model. Differences have been observed in optimal sites for rainwater harvesting with respect to location, size and coverage area. Northwest Andalusia emerges as a prime candidate for rainwater harvesting, as its runoff basin extends over an area of 77,345 km², constituting approximately 9.5% of the total area of Andalusia in 2020. It is worth noting that impermeable rock formations dominate the primary rainwater harvesting areas in the north and northwest regions, in order to facilitate the large surface runoff model. These findings extend beyond Andalusia, providing valuable insights for alleviating water scarcity and environmental hardship in semi-arid regions around the world.
  Keywords: GIS; geographical information system; RWH; rainwater harvesting; SCS-CN model; runoff; soil moisture.
  DOI: 10.1504/IJW.2025.10073048