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Advances in Hydrology and Water Resources Management for Sustainable Futures

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1826

Special Issue Editors

Department of Earth and Environmental Sciences (ERTH), College of Natural Sciences, California State University, Chico, CA, USA
Interests: hydrologic modeling; machine learning; deep learning; water resources management; water–energy–food nexus; ecohydrology

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Guest Editor
Department of Civil Engineering, Ajou Univeristy, Suwon-si, Republic of Korea
Interests: water resources management; reservoir operation; flood; droughts; rainfall-runoff modeling; hydropower; integrated watershed management; hydrologic processes

Special Issue Information

Dear Colleagues,

Sustainable water resources management is one of the most pressing challenges of our time. Rapid environmental change, population growth, and increasing water demand are reshaping hydrologic systems and testing the limits of existing management strategies. Advances in hydrologic modeling, data-driven tools, and interdisciplinary approaches now offer powerful means to support informed decision-making and adaptive water governance.

This Special Issue, titled “Advances in Hydrology and Water Resources Management for Sustainable Futures,” aims to bring together innovative research that connects hydrologic science with practical water management solutions to ensure resilient and equitable water systems for the future.

We invite you to contribute your recent research that advances hydrologic understanding, modeling innovations, and management strategies aimed at achieving sustainable water futures. The potential topics for this Special Issue include, but are not limited to, the following:

  • Hydrologic modeling and applications;
  • Climate change impacts;
  • Flood risk assessment;
  • Drought mitigation;
  • Integrated water resources management;
  • Simulation and optimization for reservoir operation;
  • Groundwater modeling and management;
  • Hydrology and ecosystem interactions;
  • Machine learning applications;
  • Uncertainty and risk analysis.

Dr. Sooyeon Yi
Prof. Dr. Jaeeung Yi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • hydrologic modeling
  • water resources management
  • climate change
  • extreme weather events
  • flood risk assessment
  • drought mitigation
  • environment and ecosystem responses
  • reservoir operation
  • machine learning
  • uncertainty and risk analysis

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Published Papers (2 papers)

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Research

17 pages, 2181 KB  
Article
A Specific Energy-Based Operational Strategy for Improving Hydropower Generation Efficiency
by Eunkyung Lee, Jungwon Ji, Sooyeon Yi, Jeongin Yoon and Jaeeung Yi
Water 2026, 18(10), 1114; https://doi.org/10.3390/w18101114 - 7 May 2026
Viewed by 439
Abstract
Hydropower is a major renewable energy source, and improving the operational efficiency of existing hydropower systems has become essential. The objectives of this study are to (a) develop a specific energy-based hydropower efficiency method at a daily scale; (b) establish a comparable and [...] Read more.
Hydropower is a major renewable energy source, and improving the operational efficiency of existing hydropower systems has become essential. The objectives of this study are to (a) develop a specific energy-based hydropower efficiency method at a daily scale; (b) establish a comparable and general indicator for hydropower reservoir operation planning; (c) propose an operational strategy and practical decision support tool that maximizes generation performance under identical generation discharge constraints. We develop a method to estimate specific energy for different power output levels using the power output and discharge relationship and construct operating combinations based on the output range with the highest specific energy. We applied it to a single day and extended it to an entire month, using Hwacheon hydropower dam in South Korea. The results show that the daily increase ranged from 2.73 to 18.4 MWh, and the total monthly cumulative increase was 177.39 MWh. This corresponds to a potential increase of about 2.1 GWh in electricity generation. This approach achieves higher energy generation than observed operational performance. A specific energy-based operational strategy can consistently improve generation performance across varying hydrologic conditions. Specific energy provides a practical decision support tool for improving generation performance under water resource constraints. Full article
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22 pages, 2344 KB  
Article
Climograph-Supported Assessment of Temperature–Precipitation Trends Using Classical and Innovative Statistical Methods in the Yeşilırmak Basin, Türkiye
by Murat Pinarlik
Water 2025, 17(24), 3532; https://doi.org/10.3390/w17243532 - 13 Dec 2025
Viewed by 830
Abstract
Understanding long-term variations in temperature and precipitation is essential for interpreting regional hydroclimatic behavior and detecting potential shifts in water availability. This study analyzes annual and seasonal temperature–precipitation trends in the Yeşilırmak Basin, Türkiye, using data from seven meteorological stations over a 38-year [...] Read more.
Understanding long-term variations in temperature and precipitation is essential for interpreting regional hydroclimatic behavior and detecting potential shifts in water availability. This study analyzes annual and seasonal temperature–precipitation trends in the Yeşilırmak Basin, Türkiye, using data from seven meteorological stations over a 38-year period (1975–2012). The Randomness Test, Mann–Kendall (MK), and Innovative Trend Analysis (ITA) were applied to detect trends. In addition, a climograph was constructed to characterize seasonal climatic patterns. The climograph for Tokat and Dökmetepe stations shows May precipitation to be 40–50% higher than in winter, while August precipitation is nearly 89% lower than in May. Temperatures rise by approximately 20 °C from January to July, reflecting continental climatic characteristics influenced by the semi-arid transition between northern and central Türkiye. Results indicate statistically significant warming trends at confidence levels above 90%, particularly during summer and autumn, with autumn temperatures increasing by approximately 0.03–0.05 °C per year (Z = 2.3–2.5) at most stations. Precipitation exhibited moderate increases at certain stations, while overall patterns remained steady. While MK and ITA yielded largely consistent results, ITA proved advantageous in weak or borderline cases by detecting structural patterns across value zones. Across all seasonal and annual analyses, ITA identified additional trends in approximately 83% of the cases where MK detected no significant change, corresponding to 25 out of 30 seasonal comparisons. Moreover, in over 92% of statistically significant MK results, ITA outcomes were fully consistent, reinforcing its robustness. Full article
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