Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.1
Journals
Hydrology
Special Issues
Hydrological Modeling and Sustainable Water Resources Management
share announcement

Hydrological Modeling and Sustainable Water Resources Management

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3199

Special Issue Editors

Dr. Pengxiao Zhou

E-Mail
Guest Editor
Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
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
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’ 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.

You may choose our Joint Special Issue in Environments.

Dr. Pengxiao Zhou
Dr. Qianqian Zhang
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 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. 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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 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

20 pages, 7069 KiB  
Article
The Development of a Hydrological Method for Computing Extreme Hydrographs in Engineering Dam Projects
by Oscar E. Coronado-Hernández, Vicente S. Fuertes-Miquel and Alfonso Arrieta-Pastrana
Hydrology 2024, 11(11), 194; https://doi.org/10.3390/hydrology11110194 - 15 Nov 2024
Viewed by 384
Abstract
Engineering dam projects benefit society, including hydropower, water supply, agriculture, and flood control. During the planning stage, it is crucial to calculate extreme hydrographs associated with different return periods for spillways and diversion structures (such as tunnels, conduits, temporary diversions, multiple-stage diversions, and [...] Read more.
Engineering dam projects benefit society, including hydropower, water supply, agriculture, and flood control. During the planning stage, it is crucial to calculate extreme hydrographs associated with different return periods for spillways and diversion structures (such as tunnels, conduits, temporary diversions, multiple-stage diversions, and cofferdams). In many countries, spillways have return periods ranging from 1000 to 10,000 years, while diversion structures are designed with shorter return periods. This study introduces a hydrological method based on data from large rivers which can be used to compute extreme hydrographs for different return periods in engineering dam projects. The proposed model relies solely on frequency analysis data of peak flow, base flow, and water volume for various return periods, along with recorded maximum hydrographs, to compute design hydrographs associated with different return periods. The proposed method is applied to the El Quimbo Hydropower Plant in Colombia, which has a drainage area of 6832 km2. The results demonstrate that this method effectively captures peak flows and evaluates hydrograph volumes and base flows associated with different return periods, as a Root Mean Square Error of 11.9% of the maximum volume for various return periods was achieved during the validation stage of the proposed model. A comprehensive comparison with the rainfall–runoff method is also provided to evaluate the relative magnitudes of the various variables analysed, ensuring a thorough and reliable assessment of the proposed method. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
►▼ Show Figures

Figure 1

17 pages, 2227 KiB  
Article
Evapotranspiration Estimation with the Budyko Framework for Canadian Watersheds
by Zehao Yan, Zhong Li and Brian Baetz
Hydrology 2024, 11(11), 191; https://doi.org/10.3390/hydrology11110191 - 12 Nov 2024
Viewed by 498
Abstract
Actual evapotranspiration (AET) estimation plays a crucial role in watershed management. Hydrological models are commonly used to simulate watershed responses and estimate AET. However, their calibration heavily depends on station-based data, which is often limited in availability and frequently inaccessible, [...] Read more.
Actual evapotranspiration (AET) estimation plays a crucial role in watershed management. Hydrological models are commonly used to simulate watershed responses and estimate AET. However, their calibration heavily depends on station-based data, which is often limited in availability and frequently inaccessible, making the process challenging and time-consuming. In this study, the Budyko model framework, which effectively utilizes remote sensing data for hydrological modeling and requires the calibration of only one parameter, is adopted for AET estimation across Ontario, Canada. Four different parameter estimation methods were developed and compared, and an attribution analysis was also conducted to investigate the impacts of climate and vegetation factors on AET changes. Results show that the developed Budyko models performed well, with the best model achieving a Nash-Sutcliffe Efficiency (NSE) value of 0.74 and a Root Mean Square Error (RMSE) value of 55.5 mm/year. The attribution analysis reveals that climate factors have a greater influence on AET changes compared to vegetation factors. This study presents the first Budyko modeling attempt for Canadian watersheds. It demonstrates the applicability and potential of the Budyko framework for future case studies in Canada and other cold regions, providing a new, straightforward, and efficient alternative for AET estimation and hydrological modeling. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
►▼ Show Figures

Figure 1

21 pages, 6264 KiB  
Article
Suitability Assessment and Optimization of Small Dams and Reservoirs in Northern Ghana
by Etienne Umukiza, Felix K. Abagale, Thomas Apusiga Adongo and Andrea Petroselli
Hydrology 2024, 11(10), 166; https://doi.org/10.3390/hydrology11100166 - 7 Oct 2024
Viewed by 909
Abstract
Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation [...] Read more.
Water shortages, exacerbated by erratic rainfall, climate change, and population growth, pose significant challenges globally, particularly in semi-arid regions like northern Ghana. Despite the construction of numerous small dams in the region that were intended to provide reliable water for domestic and irrigation purposes, critical water issues persist during dry periods. Key drivers in this failure are attributed to the lack of studies and/or the number of inadequate studies on suitable dam siting. This study focused on assessing the sites of selected small dams in northern Ghana, employing various methods such as stream order analysis and the Analytic Hierarchy Process within a Geographic Information System framework. Results showed that many existing dams are poorly sited, with over half located far from major stream networks, resulting in drying out during the dry season and failing to meet sustainable water storage standards. This study proposed new dam locations that would allow achieving a significant increase in storage capacities from 30% to 60%. These results highlight the necessity for decision-makers to adopt research-based approaches to address water shortages effectively, balancing agricultural, domestic, economic, and environmental needs. Future research should integrate climate change considerations, long-term monitoring, environmental impact assessments, and advanced decision-making techniques such as machine learning. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
►▼ Show Figures

Figure 1

18 pages, 4965 KiB  
Article
Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models
by Reza Piraei, Majid Niazkar, Fabiola Gangi, Gökçen Eryılmaz Türkkan and Seied Hosein Afzali
Hydrology 2024, 11(10), 163; https://doi.org/10.3390/hydrology11100163 - 3 Oct 2024
Viewed by 914
Abstract
This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple [...] Read more.
This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts. Full article
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop