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Application of Hydrological Modelling to Water Resources Management

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

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 12502

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Prof. Dr. Doosun Kang

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Department of Civil Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: water resources management; hydrological modeling; water–energy nexus; decision support system; environmental impact assessment
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Dr. Amir Saman Tayerani Charmchi

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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
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Dr. Fatemeh Ghobadi

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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 Issues, Collections and Topics in MDPI journals

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 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

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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Related Special Issue

Application of Hydrological Modelling to Water Resources Management, 2nd Edition in Water

Published Papers (9 papers)

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Research

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18 pages, 11982 KB

Open AccessArticle

A Baseflow Equation: Example of the Middle Yellow River Basins

by Haoxu Tong and Li Wan

Water 2026, 18(2), 280; https://doi.org/10.3390/w18020280 - 21 Jan 2026

Viewed by 388

Abstract

Existing baseflow estimation methods—such as exponential recession models, linear reservoir approaches, and digital filtering techniques—seldom account for anthropogenic disturbances or evapotranspiration-induced streamflow alterations. To address this limitation, a physically based baseflow equation that explicitly integrates human water withdrawals and evapotranspiration losses has been introduced. The governing equation was reformulated from a nonlinear storage–discharge relationship and validated against multi-decadal streamflow records in the Middle Yellow River Basin (MYRB). Results demonstrate that the proposed model accurately reproduces observed recession behavior across diverse sub-basins (NSE ≥ 0.94; RMSE ≤ 152 m³ s⁻¹). By providing reliable baseflow estimates, the equation enables quantitative assessment of eco-hydrological benefits and informs cost-effective water-resource investments. Furthermore, long-term baseflow simulations driven by climate projections offer a scientific basis for evaluating climate-change impacts on regional water security. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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27 pages, 11028 KB

Open AccessArticle

Integration of Satellite-Derived Meteorological Inputs into SWAT, XGBoost, WGAN, and Hybrid Modelling Frameworks for Climate Change-Driven Streamflow Simulation in a Data-Scarce Region

by Sefa Nur Yeşilyurt and Gülay Onuşluel Gül

Water 2026, 18(2), 239; https://doi.org/10.3390/w18020239 - 16 Jan 2026

Viewed by 783

Abstract

The pressure of climate change on water resources has made the development of reliable hydrological models increasingly important, especially for data-scarce regions. However, due to the limited availability of ground-based observations, it considerably affects the accuracy of models developed using these inputs. This also limits the ability to investigate future hydrological behavior. Satellite-based data sources have emerged as an alternative to address this challenge and have received significant attention. However, the transferability of these datasets across different model classes has not been widely explored. This paper evaluates the transferability of satellite-derived inputs to eleven types of models, including process-based (SWAT), data-driven methods (XGBoost and WGAN), and hybrid model structures that utilize SWAT outputs with AI models. SHAP has been applied to overcome the black-box limitations of AI models and gain insights into fundamental hydrometeorological processes. In addition, uncertainty analysis was performed for all models, enabling a more comprehensive evaluation of performance. The results indicate that hybrid models using SWAT combined with WGAN can achieve better predictive accuracy than the SWAT model based on ground observation. While the baseline SWAT model achieved satisfactory performance during the validation period (NSE ≈ 0.86, KGE ≈ 0.80), the hybrid SWAT + WGAN framework improved simulation skill, reaching NSE ≈ 0.90 and KGE ≈ 0.89 during validation. Models forced with satellite-derived meteorological inputs additionally performed as well as those forced using station-based observations, validating the feasibility of using satellite products as alternative data sources. The future hydrological status of the basin was assessed based on the best-performing hybrid model and CMIP6 climate projections, showing a clear drought signal in the flows and long-term reductions in average flows reaching up to 58%. Overall, the findings indicate that the proposed framework provides a consistent approach for data-scarce basins. Future applications may benefit from integrating spatio-temporal learning frameworks and ensemble-based uncertainty quantification to enhance robustness under changing climate conditions. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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23 pages, 2473 KB

Open AccessArticle

Multi-Model Comparison of Hydrologic Simulation Performance Using DWAT, PRMS, and TANK Models

by Deokhwan Kim, Wonjin Jang, Heechan Han, Hyoung-Sub Shin, Hyeonjun Kim and Cheolhee Jang

Water 2026, 18(2), 145; https://doi.org/10.3390/w18020145 - 6 Jan 2026

Viewed by 711

Abstract

This study compares the streamflow simulation performance of a semi-distributed hydrological model, DWAT (Dynamic Water Resources Assessment Tool), and two conceptual models, PRMS and TANK, across three watersheds in the Republic of Korea representing mountainous (Okdong-gyo), mixed-use (Wonbu-gyo), and urbanized (Daegok-gyo) conditions. All models were calibrated and validated using identical hydroclimatic datasets and evaluation periods to ensure a fair comparison. Model performance was evaluated using nine statistical metrics (R², NSE, LogNSE, KGE, RMSE, MAE, RE, VE, and RSR), supplemented by low-flow analysis based on a Q90 threshold and non-parametric statistical tests. DWAT exhibited the most stable and highest overall performance across all watersheds, with particularly strong results in the urbanized Daegok-gyo basin (NSE = 0.85, R² = 0.88). The TANK model performed best in the mixed-use Wonbu-gyo basin (NSE = 0.82, R² = 0.83), whereas PRMS showed a systematic tendency to underestimate streamflow, especially under high-flow and low-flow conditions. Statistical comparisons using Friedman and post hoc Dunn tests confirmed that performance differences among models were statistically significant (p < 0.001). Overall, the results demonstrate that hydrological model performance strongly depends on watershed characteristics and provide a quantitative and statistically supported basis for selecting appropriate runoff simulation models according to basin type. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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25 pages, 7632 KB

Open AccessArticle

Study on Inundation Analysis Characteristics of a Grid-Based Urban Drainage System (GUDS)

by Dahae Yu, Jungmin Lee, Dongjun Kim and Jungho Lee

Water 2025, 17(24), 3539; https://doi.org/10.3390/w17243539 - 13 Dec 2025

Viewed by 636

Abstract

The risk of urban flooding has escalated with increasing rainfall intensity and the expansion of impervious surfaces. While commercial models such as XP-SWMM provide reliable hydraulic analyses, their closed-source structure limits transparency and integration with external tools. In contrast, the Grid-Based Urban Drainage System Analysis Model (GUDS), developed on the Weighted Cellular Automata 2D (WCA2D) framework, offers greater flexibility for process verification and coupling with platforms such as GIS and spreadsheets. This study presents a comparative assessment of numerical stability and velocity estimation schemes between XP-SWMM and GUDS. Moving beyond previous validation-focused studies, it quantitatively examines how algorithmic formulations—particularly in flow velocity computation and numerical treatment—affect inundation propagation and model stability under varying topographic conditions. Results demonstrate that XP-SWMM yields higher analytical precision but is prone to numerical instability on steep slopes, whereas GUDS maintains stable simulations due to its simplified water-level-difference approach, albeit with reduced responsiveness to rapidly changing flows. The differences in maximum inundation depth, inundation area, and propagation speed were relatively minor—approximately 11.6%, 10.7%, and 9.2% on average, respectively. This work provides a novel quantitative perspective on the trade-offs between precision and stability in urban flood modeling, highlighting GUDS’s robustness and practical applicability as an open and extensible alternative to conventional equation-based models. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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17 pages, 3574 KB

Open AccessArticle

Rooftop-Scale Runoff Reduction Performance of Smart Blue-Green Roofs and Their Potential Role in Urban Flood Mitigation

by Sung Min Cha, Jaerock Park, Kyung Soo Han, Jong Dae Kim, Jung Min Lee, Soonchul Kwon and Jaemoon Kim

Water 2025, 17(22), 3328; https://doi.org/10.3390/w17223328 - 20 Nov 2025

Cited by 1 | Viewed by 1230

Abstract

Urban areas face increasing flood risks due to climate change, intensified rainfall events, and high impervious surface coverage. Blue-Green Roofs (BGR) have emerged as a nature-based solution to retain stormwater, while Smart BGR systems integrate active control functions to enhance performance under varying rainfall conditions. This study evaluated the rooftop-scale runoff reduction efficiency of conventional roofs, BGR, and Smart BGR using 31 monitored rainfall events in 2024, while eight years of historical rainfall data (2017–2024) were used only to characterize long-term rainfall patterns in the study area. A multiple-linear regression analysis was performed for exploratory trend identification between rainfall characteristics and runoff reduction; variables unrelated to short-term storm responses such as evapotranspiration or initial storage were beyond the study scope. Results showed that the annual runoff per unit area was 1.115 m³/m² for conventional roofs, 0.547 m³/m² for BGR, and 0.128 m³/m² for Smart BGR, corresponding to reduction rates of 50.98% and 88.53% for BGR and Smart BGR, respectively. In higher rainfall classes, Smart BGR maintained significantly higher performance: for Class 3 (average 53.00 mm), BGR reduced runoff by 54.89% while Smart BGR achieved 86.71%; for Class 4 (average 121.21 mm), the rates were 54.68% and 90.00%, respectively. These findings indicate that Smart BGR’s storage optimization and controlled discharge enable superior effectiveness during intense and prolonged events. The study highlights Smart BGR’s potential as an advanced stormwater management technology, offering clear advantages over both conventional roofs and passive BGR designs. Limitations include the need for testing under more extreme rainfall scenarios, optimization of operational strategies, and economic feasibility assessments. Nevertheless, Smart BGR represents a promising approach for enhancing urban flood resilience in the context of climate change. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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22 pages, 3652 KB

Open AccessArticle

Research on Optimal Water Resource Allocation in Inland River Basins Based on Spatiotemporal Evolution Characteristics of Blue and Green Water—Taking the Taolai River Basin of the Heihezi Water System as an Example

by Jiahui Zhang, Xinjian Fan, Xinghai Wang, Lirong Wang, Jiafang Wei and Yuhan Xiao

Water 2025, 17(20), 2935; https://doi.org/10.3390/w17202935 - 11 Oct 2025

Cited by 1 | Viewed by 1097

Abstract

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the inherent relationship between blue and green water supply and demand, particularly in terms of geographical differentiation characteristics and rational allocation of blue and green water supply–demand balance in inland river basins. Using the Taolai River Basin as a case study, this research uses the distributed hydrological model SWAT from a blue–green water resources viewpoint to simulate the spatiotemporal distribution features of blue and green water resources at the sub-basin scale from 2002 to 2021. The supply and demand balance relationship of blue and green water resources within the basin was investigated, an assessment index system for water resource security was developed, and the realizable potential of blue water resources was quantified using various indicators. The findings show that during the study period, the average annual green water resources in the Taolai River Basin were 1.95 times greater than blue water resources, making green water the most abundant component of regional water resources. Spatially, both blue and green water resources showed considerable latitudinal zonality, with a declining tendency from south to north and very consistent distribution patterns. Blue water resources showed high geographic variability, with a safety index more than one, suggesting that supply–demand imbalances were most concentrated in the upper and intermediate ranges of the irrigated region, as well as the desert zone, where safety levels were relatively low. In contrast, green water resources had a safety score ranging from 0.7 to 1.0, indicating great overall safety and negligible regional variability. During the research period, the average annual theoretical transferable blue water resources were 4.06 × 108 m³, based on cross-regional water resource allocation potential analysis. This reveals tremendous potential for enhancing regional water resource allocation, hence providing substantial support for effective water consumption within the Taolai River Basin and regional economic growth. In conclusion, the assessment method developed in this work provides a solid foundation for improving water resource allocation and sustainable management in river basins. It provides technical assistance in the construction of water network systems in inland river basins, which is critical in establishing reasonable water resource distribution across various areas within these basins. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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18 pages, 8435 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1864

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 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

Open AccessArticle

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 956

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, 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

Open AccessReview

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

Cited by 6 | Viewed by 3884

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 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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