Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.0
Journals
Water
Special Issues
Advances in Research on Hydrology and Water Resources
water-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Water Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Research on Hydrology and Water Resources

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 2850

Special Issue Editors

Dr. Md Jahangir Alam

E-Mail Website
Guest Editor
Murray-Darling Basin Authority (MDBA), Canberra, ACT 2601, Australia
Interests: hydrology; hydraulics; enviromental hydraulics; pollutant transport process and water quality; river system analysis; floodplain modelling; low flow prediction; climate change adaptation; water resources managment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Monzur Imteaz

E-Mail Website
Guest Editor
Department of Civil and Construction Engineering, School of Engineering, Swinburne University of Technology, Melbourne, Australia
Interests: water engineering; water quality; sustainable water use; environmental engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Faisal Anwar

E-Mail Website
Guest Editor
Department of Civil Engineering, School of Civil and Mechanical Engineering, Curtin University, Perth, Australia
Interests: hydrological processes; stormwater/greaywater management; contaminant remomval; ecological engineering; groundwater hydrology

Special Issue Information

Dear Colleagues,

Climate change and human activities are reshaping the global hydrological cycle, driving extreme weather events, altering seasonality, and intensifying water scarcity. Hydrologic non-stationarity has led to reduced stream flows in some regions while amplifying flooding in others. Rapid urban expansion and increasing agricultural demands further threaten water accessibility and quality, underscoring the urgent need for sustainable water management strategies.

Beyond ensuring environmental protection, strengthening community resilience to water scarcity is paramount. Additionally, safeguarding cultural values and their connection to land and water management is essential for holistic and equitable solutions.

This Special Issue will bring together pioneering research that addresses global water resource challenges through innovative approaches. We invite contributions from researchers, scholars, and industry professionals deepening scientific understanding and driving transformative advancements in hydrological science and sustainable water management.

Topics of interest include, but are not limited to, the following:

  • Innovations in hydrological modelling and forecasting.
  • Applications of remote sensing and GIS in water resource management.
  • Advancements in AI and machine learning in hydrology.
  • Climate change’s impacts on hydrological cycles and water availability.
  • The prediction and mitigation of extreme weather events, droughts, and floods.
  • Urban hydrology and stormwater management strategies.
  • Advances in research on surface water–groundwater interactions and low-flow simulation.
  • Advances in research on environmental flows.
  • Water quality forecasting, pollution control, and management.
  • Cultural flows and hydrological modelling.

We invite you to join us in shaping a resilient future for our water resources.

Dr. Md Jahangir Alam
Prof. Dr. Monzur Imteaz
Prof. Dr. Faisal Anwar
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. 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

  • hydrology
  • water resources
  • extreme climate
  • climate change
  • drought and floods
  • AI and machine learning in water management
  • low-flow simulation
  • enviromental pollution
  • cultural flows
  • river basin

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 (3 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

25 pages, 4278 KB  
Article
Temporal and Spatial Variation in B and Sr Isotopic Composition in the Erren River, Southwestern Taiwan
by Chuan-Hsiung Chung, Chen-Feng You and Tai-Ju Shih
Water 2026, 18(3), 368; https://doi.org/10.3390/w18030368 - 31 Jan 2026
Viewed by 441
Abstract
River water is a vital component of the hydrological cycle, sustaining ecosystems and serving as the most accessible freshwater resource for human use. Beyond elemental concentrations, isotopic tracers such as boron (δ11B) and radiogenic strontium (87Sr/86Sr) provide [...] Read more.
River water is a vital component of the hydrological cycle, sustaining ecosystems and serving as the most accessible freshwater resource for human use. Beyond elemental concentrations, isotopic tracers such as boron (δ11B) and radiogenic strontium (87Sr/86Sr) provide insights into weathering processes and anthropogenic impacts. This study examines spatial and temporal variations in the chemical composition of the Erren River to distinguish natural contributions from human-derived inputs and assess recent pollution. Samples collected from upstream to downstream were processed by micro-sublimation or column chromatography, with isotopes measured using MC-ICP-MS. Results show δ11B values from +4.8‰ to +30.4‰ (variation ~26‰) and 87Sr/86Sr ratios from 0.709679 to 0.710446. Major ion and isotopic data indicate upstream waters are dominated by silicate weathering, while downstream areas reflect seawater and salt spray influence, consistent with regional geology and hydrology. Furthermore, δ11B patterns combined with Cl−/Na and NO3−/B ratios suggest that tributaries in the mid-to-lower basin remain affected by anthropogenic pollution, likely linked to agricultural and urban activities. These findings highlight both natural controls and ongoing human impacts on the Erren River system. Full article
(This article belongs to the Special Issue Advances in Research on Hydrology and Water Resources)
►▼ Show Figures

Figure 1

13 pages, 2173 KB  
Article
Daily Streamflow Prediction Using Multi-State Transition SB-ARIMA-MS-GARCH Model
by Jin Zhao, Jianhui Shang, Qun Ye, Huimin Wang, Gengxi Zhang, Feng Yao and Weiwei Shou
Water 2026, 18(2), 241; https://doi.org/10.3390/w18020241 - 16 Jan 2026
Viewed by 533
Abstract
Under the combined influences of climate change and anthropogenic activities, the variability of basin streamflow has intensified, posing substantial challenges for accurate prediction. Although Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models characterize volatility in time series, many previous studies have neglected changes in series [...] Read more.
Under the combined influences of climate change and anthropogenic activities, the variability of basin streamflow has intensified, posing substantial challenges for accurate prediction. Although Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models characterize volatility in time series, many previous studies have neglected changes in series structure, leading to inaccurate identification of the form of volatility. Building on tests for structural breaks (SBs) in time series, this study first removes the series mean using an Autoregressive Integrated Moving Average (ARIMA) model and then incorporates Markov-switching (MS) to develop a multi-state MS-GARCH model. An asymmetric MS-GARCH (MS-gjrGARCH) variant is also incorporated to describe the volatility of streamflow series with SBs. Daily streamflow data from five hydrological stations in the middle reaches of the Yellow River are used to compare the predictive performance of SB-ARIMA-MS-GARCH, SB-ARIMA-MS-gjrGARCH, ARIMA-GARCH, and ARIMA-gjrGARCH models. The results show that daily streamflow exhibits SBs, with the number and timing of breakpoints varying among stations. Standard GARCH and gjrGARCH models have limited ability to capture runoff volatility clustering, whereas MS-GARCH and MS-gjrGARCH effectively characterize volatility features within individual states. The multi-state switching structure substantially improves daily streamflow prediction accuracy compared with single-state volatility models, increasing R2 by approximately 5.8% and NSE by approximately 36.3%.The proposed modeling framework offers a robust new tool for streamflow prediction in such changing environments, providing more reliable evidence for water resource management and flood risk mitigation in the Yellow River basin. Full article
(This article belongs to the Special Issue Advances in Research on Hydrology and Water Resources)
►▼ Show Figures

Figure 1

31 pages, 3511 KB  
Article
Development and Comparison of Methods for Identification of Baseflow-Dominant Periods in Streamflow Records
by Amin Aghababaei, Norman L. Jones, Gustavious P. Williams, Eniola Webster-Esho, Ryan van der Heijden, Xueyi Li, T. Prabhakar Clement and Donna M. Rizzo
Water 2025, 17(21), 3083; https://doi.org/10.3390/w17213083 - 28 Oct 2025
Viewed by 1364
Abstract
Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD [...] Read more.
Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD periods as flow conditions that occur with minimal contribution from quickflow, including periods dominated by bank flow, groundwater interaction, or residual flow routing through the system. We develop a comprehensive, expert-labeled dataset of BFD periods from 182 USGS stream gages across diverse hydrological settings in the continental United States as ground truth. Using this dataset, we evaluate various automated BFD identification methods, including three new approaches, a machine learning classifier, a gradient-based method, and a statistical method, as well as two established techniques: the BN77 and Strict Baseflow methods. Our results demonstrate that the machine learning model (RF-BFD) outperforms all other approaches, achieving an F1 score of 0.92 and 92% accuracy. This study characterizes challenges in identifying BDF periods and establishes benchmarks for improving BFD identification in large-scale hydrological studies. The findings offer a pathway toward more robust and scalable BFD identification techniques, enhancing low-flow forecasting and groundwater-surface water interaction assessments. Full article
(This article belongs to the Special Issue Advances in Research on Hydrology and Water Resources)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop