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Application of Hydrological Modelling to Water Resources Management
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Application of Hydrological Modelling to Water Resources Management

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1709

Special Issue Editors

Prof. Dr. Doosun Kang

E-Mail Website
Guest Editor
Department of Civil Engineering, Kyung Hee University, 1732 De-ogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: water resources management; hydrological modeling; water–energy nexus; decision support system; environmental impact assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Amir Saman Tayerani Charmchi

E-Mail Website
Guest Editor
Department of Civil Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: water resources management; water–energy nexus analysis; sustainable management strategies using big data; advanced machine learning techniques
Dr. Fatemeh Ghobadi

E-Mail Website
Guest Editor Assistant
Department of Civil Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: water resources management; advanced machine learning techniques; hydrological modeling; flood forecasting

Special Issue Information

Dear Colleagues,

This Special Issue emphasizes the application, rather than the further development, of both conventional and advanced hydrological models to water resources challenges. Submissions are invited that illustrate the operationalization of data-driven, physically based, conceptual, lumped, distributed, deterministic, and stochastic models. Of particular interest are studies that integrate these models with artificial intelligence, machine learning, deep learning, optimization problems, and probabilistic frameworks to enhance model-informed decision making. The scope includes, but is not limited to, applications related to flood and drought management, climate change impact assessment, reservoir and inter-basin operation, water allocation, and the water–energy nexus (e.g., optimizing hydropower or floating photovoltaic reservoir systems). In contrast to traditional research emphasizing hydrological modeling, this Special Issue shifts the focus toward evidence-based implementation. Key areas of interest include rigorous model calibration and validation using diverse observational sources (in situ, remote sensing, crowdsourced, and big data streams), integration into decision support systems and digital twin platforms, and translating model outputs into actionable water policy and governance strategies. Contributions should articulate how the presented applications support the advancement of the United Nations Sustainable Development Goals, specifically SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 13 (Climate Action), and SDG 15 (Life on Land). Through original research articles, review papers, regional case studies, and comparative analyses, this Issue aims to identify knowledge gaps, showcase best practices, and offer strategic insights for scalable, resilient, and just water resources management under changing climatic conditions.

Prof. Dr. Doosun Kang
Dr. Amir Saman Tayerani Charmchi
Guest Editors

Dr. Fatemeh Ghobadi
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • climate-change adaptation
  • climate-related disasters
  • decision support systems
  • digital twins
  • early warning systems
  • environmental uncertainties
  • hydrological modeling
  • integrated water resources management (IWRM)
  • machine learning and deep learning
  • model-informed decision making
  • multi-criteria decision making
  • multi-objective opti-mization
  • optimal water allocation
  • probabilistic modeling
  • proactive management strategies
  • reinforcement learning
  • remote sensing and GIS applications
  • scheduling and planning
  • Sustainable Development Goals (SDGs)
  • water–energy nexus
  • water supply and demand
  • water resources management

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Published Papers (3 papers)

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Research

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18 pages, 8435 KB  
Article
Modeling Sentiment–Hydrology Interaction Using LLM: Insights for Adaptive Governance in Ceará’s Water Management
by Tatiane Lima Batista, Ticiana Marinho de Carvalho Studart, Marlon Gonçalves Duarte and Francisco de Assis de Souza Filho
Water 2025, 17(17), 2615; https://doi.org/10.3390/w17172615 - 4 Sep 2025
Viewed by 147
Abstract
This study aims to analyze the relationships between concerns and sentiments of stakeholders and the drought stage in a semi-arid region of Ceará from Language Technologies based on Artificial Intelligence. The dataset comprises 36 meeting minutes of water management bodies (2007–2024), of which [...] Read more.
This study aims to analyze the relationships between concerns and sentiments of stakeholders and the drought stage in a semi-arid region of Ceará from Language Technologies based on Artificial Intelligence. The dataset comprises 36 meeting minutes of water management bodies (2007–2024), of which 17 correspond to dry periods and 19 to normal periods (reservoir volume > 50%). Natural Language Processing (NLP) techniques were applied to generate word clouds, and sentiment analysis was performed using a Large Language Model (Llama 3.2, 3B). Sentiment scores were compared with reservoir volume data. Results show that both perceptions and themes differed between drought and normal phases, with higher water availability coinciding with more positive sentiments. A moderate positive correlation was found between sentiment and reservoir volume (r = 0.53, p = 0.00095, 95% CI [0.24, 0.73]). Statistical tests confirmed differences between periods (Welch’s t-test, p = 0.0018; Mann-Whitney, p = 0.0039). Box-plot analyses indicated that over 75% of sentiments were positive in normal phases, while about 65% were negative in drought phases. These findings highlight the sensitivity of human perceptions to hydrological conditions and point to the potential of LLMs as innovative instruments for integrating qualitative data into complex socio-environmental analyses. Full article
(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)
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17 pages, 5008 KB  
Article
Selection of Hydrologically Vulnerable Areas in Urban Regions Using Techniques for Order Preference by Similarity to Ideal Solution
by Jungmin Lee, Myeongin Kim, Youngtae Cho and Jaebeom Park
Water 2025, 17(16), 2455; https://doi.org/10.3390/w17162455 - 19 Aug 2025
Viewed by 478
Abstract
Hydrologically vulnerable areas should be identified for sustainable urban watershed management, flood mitigation, and climate-resilient infrastructure planning. However, assessing hydrological vulnerability in complex urban environments requires a comprehensive framework that integrates hydrological components and considers spatial heterogeneity. Thus, this study proposes an objective, [...] Read more.
Hydrologically vulnerable areas should be identified for sustainable urban watershed management, flood mitigation, and climate-resilient infrastructure planning. However, assessing hydrological vulnerability in complex urban environments requires a comprehensive framework that integrates hydrological components and considers spatial heterogeneity. Thus, this study proposes an objective, data-driven method for identifying hydrologically vulnerable areas in urban regions using multicriteria decision-making (MCDM). The MCDM technique is used to rank the hydrological health of subwatersheds in an urbanizing watershed. Entropy-based weights are assigned to key hydrological indicators, which are computed using the soil and water assessment tool. Entropy-based weighting reveals that groundwater-related components contribute more to overall vulnerability than surface runoff. According to initial MCDM analysis, the most vulnerable areas are those in the upper reaches of the watershed, where steep slopes accelerate runoff and limit infiltration. This confounding influence of elevation is addressed by implementing topographic normalization and reevaluating subwatershed vulnerability while controlling for elevation bias. The findings underscore the importance of incorporating both hydrological and topographical factors into urban watershed vulnerability assessment and demonstrate the applicability of entropy-weighted MCDM to complex, data-scarce urban environments. The proposed framework is a replicable decision support tool for prioritizing hydrologically sensitive areas in intervention planning. Full article
(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)
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Review

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24 pages, 1395 KB  
Review
A Systematic Literature Review of MODFLOW Combined with Artificial Neural Networks (ANNs) for Groundwater Flow Modelling
by Kunal Kishor, Ashish Aggarwal, Pankaj Kumar Srivastava, Yaggesh Kumar Sharma, Jungmin Lee and Fatemeh Ghobadi
Water 2025, 17(16), 2375; https://doi.org/10.3390/w17162375 - 11 Aug 2025
Viewed by 748
Abstract
The sustainable management of global groundwater resources is increasingly challenged by climatic uncertainty and escalating anthropogenic stress. Thus, there is a need for simulation tools that are more robust and flexible. This systematic review addresses the integration of two dominant modeling paradigms: the [...] Read more.
The sustainable management of global groundwater resources is increasingly challenged by climatic uncertainty and escalating anthropogenic stress. Thus, there is a need for simulation tools that are more robust and flexible. This systematic review addresses the integration of two dominant modeling paradigms: the physically grounded Modular Finite-Difference Flow (MODFLOW) model and the data-agile Artificial Neural Network (ANN). While the MODFLOW model provides deep process-based understanding, it is often limited by extensive data requirements and computational intensity. In contrast, an ANN offers remarkable predictive accuracy and computational efficiency, particularly in complex, non-linear systems, but traditionally lacks physical interpretability. This review synthesizes existing research to present a functional classification framework for MODFLOW–ANN integration, providing a systematic analysis of the literature within this structure. Our analysis of the literature, sourced from Scopus, Web of Science, and Google Scholar reveals a clear trend of the strategic integration of these models, representing a new trend in hydrogeological simulation. The literature reveals a classification framework that categorizes the primary integration strategies into three distinct approaches: (1) training an ANN on MODFLOW model outputs to create computationally efficient surrogate models; (2) using an ANN to estimate physical parameters for improved MODFLOW model calibration; and (3) applying ANNs as post-processors to correct systematic errors in MODFLOW model simulations. Our analysis reveals that these hybrid methods consistently outperform standalone approaches by leveraging ANNs for computational acceleration through surrogate modeling, for enhanced model calibration via intelligent parameter estimation, and for improved accuracy through systematic error correction. Full article
(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)
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