Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1159

Special Issue Editors


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Guest Editor
Michigan Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI 48105, USA
Interests: water resources management; water quality modeling; data-driven; process-based modeling

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Guest Editor
Cooperative Institute for Great Lakes Research (CIGLR), Ann Arbor, MI 48109, USA
Interests: water management; hydrology; urban water; coastal flooding

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Guest Editor
Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA
Interests: water resources management; climate change; large-scale hydrological modeling; snow modeling; artificial intelligence

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Guest Editor
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Interests: groundwater inverse modeling; physics-informed machine learning; geostatistics; high performance computation

Special Issue Information

Dear Colleagues,

Hydrological modeling is an essential tool for understanding and managing the Earth’s water systems. By simulating the movement, distribution, and quality of water across various components of the hydrological cycle, it provides critical insights into water availability, distribution, and risks. In a world increasingly impacted by water scarcity, climate change, and population growth, hydrological modeling plays a critical role in enabling data-driven decisions, which help to ensure the sustainable management of water resources and the protection of ecosystems and communities. In recent years, Artificial Intelligence (AI) has emerged as a transformative force across various disciplines, including hydrology. Rapid advancements in AI have opened up new possibilities for addressing long-standing challenges, such as improving the prediction of hydrological extremes and advancing our understanding of the complex interactions between natural and human systems. This Special Issue emphasizes the importance of interdisciplinary approaches that integrate AI with hydrological science to support sustainable water resource management in the face of growing environmental and societal challenges.

Specifically, we invite submissions of manuscripts that include, but are not limited to, the development and application of AI tools in the following areas:

  • Modeling and forecasting streamflow and extreme hydrological events.
  • Inverse modeling of hydrogeological science and geoscience.
  • Assessing climate change impacts on water systems.
  • Developing climate change mitigation and adaptation strategies to enhance the resilience of water systems to climate-related challenges.
  • Informing operational decisions related to droughts, floods, and reservoir management.
  • Integrated modeling of hydrological and social systems under changing environmental conditions.
  • Large-scale high-quality datasets of hydrology, geoscience, and water resources.

We look forward to receiving your original research articles and reviews.

Dr. Xiaofeng Liu
Dr. Yi Hong
Dr. Ryan C. Johnson
Dr. Quan Guo
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
  • artificial intelligence (AI)
  • large-scale dataset
  • climate change
  • extreme hydrological events
  • operational hydrology
  • social systems

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Published Papers (1 paper)

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Review

45 pages, 3649 KiB  
Review
Protocols for Water and Environmental Modeling Using Machine Learning in California
by Minxue He, Prabhjot Sandhu, Peyman Namadi, Erik Reyes, Kamyar Guivetchi and Francis Chung
Hydrology 2025, 12(3), 59; https://doi.org/10.3390/hydrology12030059 - 14 Mar 2025
Viewed by 892
Abstract
The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, [...] Read more.
The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, leveraging Artificial Neural Networks (ANNs), a core technique in machine learning (ML), which is a subfield of AI, for water and environmental modeling (WEM) since the early 1990s. While protocols for WEM exist in California, they were designed primarily for traditional statistical or process-based models that rely on predefined equations and physical principles. In contrast, ML models learn patterns from data and require different development methodologies, which existing protocols do not address. This study, drawing on DWR’s extensive experience in ML, addresses this gap by developing standardized protocols for the development and implementation of ML models in WEM in California. The proposed protocols cover four key phases of ML development and implementation: (1) problem definition, ensuring clear objectives and contextual understanding; (2) data preparation, emphasizing standardized collection, quality control, and accessibility; (3) model development, advocating for a progression from simple models to hybrid and ensemble approaches while integrating domain knowledge for improved accuracy; and (4) model deployment, highlighting documentation, training, and open-source practices to enhance transparency and collaboration. A case study is provided to demonstrate the practical application of these protocols step by step. Once implemented, these protocols can help achieve standardization, quality assurance, interoperability, and transparency in water and environmental modeling using machine learning in California. Full article
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