Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
3.2
Journals
Hydrology
Special Issues
Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition
share announcement

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 5115

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 27614 KB  
Article
Beyond Vertical Accuracy: Benchmarking Global DEMs for Hydrologic Connectivity and Flood Sensitivity in Flat Coastal Plains
by Jose Miguel Fragozo Arevalo, Jairo R. Escobar Villanueva and Jhonny I. Pérez-Montiel
Hydrology 2026, 13(2), 74; https://doi.org/10.3390/hydrology13020074 - 22 Feb 2026
Viewed by 493
Abstract
We assessed the vertical accuracy of six global digital elevation models—FABDEM (SRTM-enhanced), SRTM, ASTER GDEM, ALOS AW3D30, DeltaDTM and GEDTM—against a local photogrammetry-derived DEM as a benchmark in a flat coastal plain of the Colombian Caribbean. Using GNSS-RTK ground points and a high-accuracy [...] Read more.
We assessed the vertical accuracy of six global digital elevation models—FABDEM (SRTM-enhanced), SRTM, ASTER GDEM, ALOS AW3D30, DeltaDTM and GEDTM—against a local photogrammetry-derived DEM as a benchmark in a flat coastal plain of the Colombian Caribbean. Using GNSS-RTK ground points and a high-accuracy reference DEM, we computed BIAS, RMSE, and MAE. Errors were analyzed by land cover class and along transverse profiles relative to the reference DEM. We also evaluated hydrologic suitability by comparing flow accumulation and drainage patterns derived from each model, treating the photogrammetry-derived model as the control and the global DEMs as treatments to gauge their ability to represent hydraulic/hydrologic behavior. DeltaDTM, GEDTM and FABDEM showed the best overall performance, with the lowest vertical error (particularly in non-urban areas with sparse vegetation) and the highest drainage agreement, along with their flood extent sensitivity to a 0.5 m water level rise, all of which were comparable to the benchmark. These results provide practical guidance for selecting and preprocessing topographic models for risk management and territorial planning in flat regions. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
►▼ Show Figures

Figure 1

16 pages, 2412 KB  
Article
Daily and Monthly Scale Comparisons of Three Gridded Precipitation Datasets over the British Columbia Province, Canada
by Riki Ogawa, Yoshihiko Iseri, M. Levent Kavvas and Angela M. Duren
Hydrology 2026, 13(2), 52; https://doi.org/10.3390/hydrology13020052 - 1 Feb 2026
Viewed by 637
Abstract
Understanding the characteristics of precipitation datasets in a given region is crucial for hydrological studies. This study focuses on the British Columbia (BC) Province in Canada and evaluates the statistical characteristics of precipitation data from three gridded precipitation datasets: the Pacific Climate Impacts [...] Read more.
Understanding the characteristics of precipitation datasets in a given region is crucial for hydrological studies. This study focuses on the British Columbia (BC) Province in Canada and evaluates the statistical characteristics of precipitation data from three gridded precipitation datasets: the Pacific Climate Impacts Consortium’s northwestern North America meteorological dataset (PNWNAmet), Global Precipitation Measurement (GPM), and Global Precipitation Climatology Centre (GPCC). These precipitation datasets at both daily and monthly scales were compared with point observation data from the Global Historical Climatology Network (GHCN). For the daily-scale comparison of three precipitation datasets, seven indices of extreme precipitation were computed at ten observation points. Out of eleven locations for the monthly analysis, GPCC showed the lowest RMSE at six locations (five of them were in the northern to central BC), and PNWNAmet showed the lowest RMSE at four locations (three of them were in the southern BC), suggesting GPCC’s superior agreements with GHCN at the northern and central part of BC and PNWNAmet’s better agreements with GHCN at the southern part of BC. The comparison of monthly precipitation averaged over BC showed that PNWNAmet offers higher monthly precipitation than GPCC and GPM, while the variability of annual precipitation among the three datasets is similar. Spatial analysis of precipitation–elevation relationships revealed the value of considering both elevation and distance from the coast in evaluating the precipitation–elevation relationships. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
►▼ Show Figures

Figure 1

20 pages, 5161 KB  
Article
Copula-Based Bayesian Inference Approaches for Uncertainty Quantification for Hydrological Simulation
by Feng Wang, Ruixin Duan, Jiannan Zhang, Mengyu Zhai, Yanfeng Li, Yurui Fan and Yulei Xie
Hydrology 2026, 13(2), 50; https://doi.org/10.3390/hydrology13020050 - 29 Jan 2026
Viewed by 680
Abstract
In this study, an advanced copula-based Bayesian inference framework is proposed to characterize probabilistic features in hydrological simulations. Specifically, a Copula–Metropolis–Hastings (CopMH) algorithm is developed through integrating copula functions into the conventional Metropolis–Hastings (MH) algorithm within an interdependence-sampling framework. In CopMH, the interdependence [...] Read more.
In this study, an advanced copula-based Bayesian inference framework is proposed to characterize probabilistic features in hydrological simulations. Specifically, a Copula–Metropolis–Hastings (CopMH) algorithm is developed through integrating copula functions into the conventional Metropolis–Hastings (MH) algorithm within an interdependence-sampling framework. In CopMH, the interdependence structure among model parameters is quantified using copula functions, which are subsequently employed to generate proposal candidates. The proposed approach is then applied to uncertainty analysis in hydrological simulations of the Ruihe River watershed in Northwest China. The results indicate that, compared with the traditional MH, incorporating copula-based proposal distributions significantly improves convergence efficiency and simulation accuracy, as inter-parameter dependence is more effectively captured. All algorithms are independently repeated 15 times, and CopMH exhibits more robust and stable performance than MH. Furthermore, the intercorrelation analysis of hydrological model parameters reveals that interactive effects among parameters are ubiquitous. These findings highlight that consideration of the interrelationship among the parameters in hydrologic models is meaningful and necessary for uncertainty quantification of hydrological simulation. This study demonstrates the strong potential of the proposed CopMH approach for effectively quantifying and reducing parameter uncertainty in hydrological simulations. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management, 2nd Edition)
►▼ Show Figures

Figure 1

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
Cited by 1 | Viewed by 753
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)
►▼ Show Figures

Figure 1

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 1704
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop