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Climate Change, Hydrological Uncertainty and Sustainable Water Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: 10 March 2026 | Viewed by 1224

Special Issue Editor


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Guest Editor
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat 123, Oman
Interests: hydrology; climate change; remote sensing and GIS applications in water and environmental studies

Special Issue Information

Dear Colleagues,

Climate change profoundly impacts the hydrological cycle, leading to more frequent and intense extreme weather events, while also increasing uncertainty in water resources management, making it essential to develop innovative strategies that enhance resilience, minimize risks, and secure water resources for the future. Addressing hydrological uncertainty—stemming from climate fluctuations, data gaps, and model limitations—is crucial in terms of formulating adaptive water management policies and infrastructure solutions.

This Special Issue seeks to deepen scientific knowledge of climate-driven hydrological variability and its effects on sustainable water management. It aims to highlight interdisciplinary approaches that integrate hydrological modeling, climate adaptation strategies, water governance, and policy development. By tackling these critical issues, the Special Issue contributes to global sustainability goals, particularly SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land).

The Special Issue welcomes original research and review articles on a broad range of topics, including:

  • Hydrological Uncertainty and Climate Change: Examining climate change’s influence on water resources, enhancing predictive models, and incorporating AI-based forecasting techniques.
  • Sustainable Water Management and Adaptation Strategies: Investigating nature-based solutions, urban water conservation methods, and integrated water resources management to support long-term sustainability.
  • Hydrological Modeling: Utilizing new and emerging technologies and decision-support tools to strengthen climate-resilient water governance.
  • Water Policy, Governance, and Societal Impacts: Assessing policy frameworks, transboundary water management, and community-driven solutions for fair water distribution.

By bringing together contributions from researchers, policymakers, and practitioners, this Special Issue seeks to promote innovative approaches that enhance climate resilience in water management. We invite submissions that offer fresh perspectives on hydrological processes, uncertainty analysis, and adaptive governance strategies to effectively address the water-related challenges of climate change.

We look forward to receiving your contributions.

Dr. Ghazi Ali Al-Rawas
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change
  • hydrological uncertainty
  • water resources management
  • extreme events
  • resilience
  • hydrological modeling
  • water governance
  • sustainable water management
  • integrated water resources management

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Published Papers (2 papers)

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Research

17 pages, 5323 KB  
Article
Mapping Flood-Prone Areas Using GIS and Morphometric Analysis in the Mantaro Watershed, Peru: Approach to Susceptibility Assessment and Management
by Del Piero R. Arana-Ruedas, Edwin Pino-Vargas, Sandra del Águila-Ríos and German Huayna
Sustainability 2025, 17(17), 7809; https://doi.org/10.3390/su17177809 - 29 Aug 2025
Viewed by 522
Abstract
Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models [...] Read more.
Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article
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19 pages, 2237 KB  
Article
Flood Season Division Model Based on Goose Optimization Algorithm–Minimum Deviation Combination Weighting
by Yukai Wang, Jun Li and Jing Fu
Sustainability 2025, 17(15), 6968; https://doi.org/10.3390/su17156968 - 31 Jul 2025
Viewed by 400
Abstract
The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. [...] Read more.
The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article
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