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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 January 2026 | Viewed by 814

Special Issue Editors


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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

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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

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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

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Published Papers (1 paper)

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Research

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 592
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)
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