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Hydrology
Special Issues
Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition
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Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition

A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: 25 April 2026 | Viewed by 1596

Special Issue Editors

Dr. Pengxiao Zhou

E-Mail Website
Guest Editor
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
Interests: hydrological modeling; wastewater modeling; uncertainty analysis; machine learning; life cycle assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Qianqian Zhang

E-Mail Website
Guest Editor
School of Management, Chengdu University of Information Technology, Chengdu, China
Interests: environmental risk analysis; water quality management; uncertainty analysis; data-driven modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fei Zhang

E-Mail Website
Guest Editor
1. SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Beijing, China
2. CAS Center for Excellence in Quaternary Science and Global Change, Xi'an, China
Interests: hydrology; ground water; surface water; geology; water quality assessment; geochemistry; chemical weathering
Special Issues, Collections and Topics in MDPI journals
Dr. Zoe Li

E-Mail Website
Guest Editor
Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Interests: uncertainty analysis; risk management; stochastic modelling; water resources management; climate change impacts; environmental systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrological modeling and the sustainable management of water resources play a vital role in addressing the complicated challenges related to water availability, quality, and sustainability. For instance, hydrological models are essential for flood control, while the management of water resources facilitates sustainable socio-economic development.

In the era of increasing water stress, this Special Issue, entitled ‘Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition’, serves as a platform for researchers to demonstrate problem-solving wisdom in this critical field. Our aim is to present innovative solutions and share cutting-edge research that can inspire, enhance and transform the way we model and manage water resources.

This Special Issue welcomes contributions that push the boundaries of hydrological modeling and offer insights into the effective management of water resources. We encourage submissions that explore emerging trends such as machine learning, remote sensing, digital twins, and data assimilation techniques to enhance our understanding of hydrological processes. Additionally, studies of computer simulation, risk analysis, and decision support for water resources are welcomed. Complementing these topics, this Special Issue seeks to encompass the latest developments in environmental modeling and technology, delve into environmental management, and highlight the critical role of environmental impact and risk assessment.

The publications in the first edition, which we believe may be of interest to you, can be found at https://www.mdpi.com/journal/hydrology/special_issues/4YBY90Z58V.

You may choose our Joint Special Issue in Environments.

Dr. Pengxiao Zhou
Dr. Qianqian Zhang
Prof. Dr. Fei Zhang
Dr. Zoe Li
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. Hydrology is an international peer-reviewed open access monthly 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 1800 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

  • hydrological modeling
  • data-driven models
  • human activity impacts on water quantity and quality
  • nonstationary rainfall runoff
  • runflow prediction
  • extreme event causality, impact and prediction
  • climate change impacts and adaptation
  • water resource management
  • flood and drought risks
  • risk analysis and management

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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24 pages, 11354 KB  
Article
AI-Integrated Framework for Designing Optimized Groundwater Level Observation Networks Based on Hybrid Machine Learning and Stochastic Simulation Frameworks
by Mohamed Haythem Msaddek, Yahya Moumni, Lahcen Zouhri, Bilel Abdelkarim and Adel Zghibi
Hydrology 2025, 12(12), 326; https://doi.org/10.3390/hydrology12120326 - 10 Dec 2025
Viewed by 229
Abstract
This study develops an integrated framework combining groundwater numerical modeling, probabilistic simulation, and machine learning to optimize the spatial design of an Optimized Groundwater Level Observation Network (OGLON) in the Mareth basin. A total of 565 existing monitoring wells were used to calibrate [...] Read more.
This study develops an integrated framework combining groundwater numerical modeling, probabilistic simulation, and machine learning to optimize the spatial design of an Optimized Groundwater Level Observation Network (OGLON) in the Mareth basin. A total of 565 existing monitoring wells were used to calibrate the groundwater flow model, complemented by stochastic groundwater simulations to train two AI-based approaches: the AI-Assisted Centroid Clustering (AIACC) algorithm and the Data-Driven Sparse Bayesian Learning (DDSBL) model. Three OGLON configurations were generated, AIACC (30 wells), DDSBL (30 wells), and Refined-DDSBL (30 wells), and benchmarked against the current monitoring network. Model performance indicates that the AIACC configuration reduces model error from 17,232 to 31.30, achieving an RMSE of 0.2145 m, significantly outperforming both the existing network (RMSE 0.5028 m) and the original DDSBL system (RMSE 0.6678 m). The Refined-DDSBL configuration provides the best overall accuracy, reducing model error from 21,355 to 1.32 and achieving the lowest RMSE (0.0153 m) and MAE (0.0091 m). Groundwater levels simulated under the proposed networks range between 3.8 m and 94.7 m, with the AIACC and Refined-DDSBL approaches offering improved spatial representation of key hydrogeological patterns compared to existing wells. Overall, results demonstrate a clear trade-off between computational efficiency (AIACC) and maximum predictive accuracy (Refined-DDSBL). Both AIACC and Refined-DDSBL significantly enhance spatial coverage and groundwater representation, confirming the effectiveness of integrating machine learning with groundwater modeling for cost-efficient and high-performance OGLON design. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
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20 pages, 4437 KB  
Article
Watershed Runoff Simulation and Prediction Based on BMA Coupled SWAT-LSTM Model
by Wenju Zhao, Yongwei Hao, Yongming Zhang, Haiying Yu and Xing Li
Hydrology 2025, 12(12), 312; https://doi.org/10.3390/hydrology12120312 - 24 Nov 2025
Viewed by 881
Abstract
In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) [...] Read more.
In response to the issues of low runoff prediction accuracy and difficulty in parameter determination in regions frequently experiencing extreme hydrological events, this study is based on data such as digital elevation, land use, soil type, and meteorology. The SWAT-LSTM (Long Short-Term Memory) model is coupled based on the Bayesian Model Averaging (BMA) method. The simulation accuracies of the optimized model are, respectively, compared with those of the SWAT (Soil and Water Assessment Tool) model and the SWAT-LSTM model. Taking the Zuli River Basin as an example, the optimal runoff prediction model for this basin is determined. Combining with future meteorological data, runoff predictions for the period from 2025 to 2030 are carried out. The findings indicate that the SWAT-LSTM-BMA coupled model is the optimal runoff prediction model for the Zuli River Basin. Compared with the SWAT model and the SWAT-LSTM model used alone, its simulation accuracy has been systematically improved. During the calibration period, R2 increased by 8–12%, NSE increased by 9–13%, and MSE decreased by 14–30%. During the validation period, R2 increased by 10–12%, NSE increased by 10–14%, and MSE decreased by 16–31%. Based on the model and the prediction of future climate data under multiple scenarios, the annual runoff of the basin will show a decreasing trend compared with the historical period between 2025 and 2030, with a decrease of 12–15%. The coupling framework proposed in this study effectively improves the accuracy of runoff prediction and provides a reliable theoretical foundation and technological assistance for revealing the evolution law of extreme hydrological events and the management of water resources in the basin. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
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