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Hydrology
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Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications
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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 8594

Special Issue Editors

Dr. Xiaofeng Liu

E-Mail Website
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
Dr. Yi Hong

E-Mail Website
Guest Editor
Cooperative Institute for Great Lakes Research (CIGLR), Ann Arbor, MI 48109, USA
Interests: water management; hydrology; urban water; coastal flooding
Special Issues, Collections and Topics in MDPI journals
Dr. Ryan C. Johnson

E-Mail Website
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
Dr. Quan Guo

E-Mail Website
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 (5 papers)

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Research

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18 pages, 5442 KB  
Article
Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks
by Haoyu Niu, Samantha Murray, Fouad Jaber, Bardia Heidari and Nick Duffield
Hydrology 2025, 12(11), 284; https://doi.org/10.3390/hydrology12110284 (registering DOI) - 30 Oct 2025
Abstract
Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water [...] Read more.
Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water resource management and disaster preparedness but also leads to disproportionate impacts on vulnerable communities and critical infrastructure. Therefore, in this article, we introduce a hybrid modeling framework that combines Generalized Extreme Value (GEV) distribution fitting with deep learning models to forecast monthly maximum precipitation extremes. Long Short-term Memory models (LSTMs) are proposed to predict the cumulative distribution (CDF) values of the GEV-fitted remainder series. This approach transforms the forecasting problem into a bounded probabilistic learning task, improving model stability and interpretability. Crucially, a tail-weighted loss function is designed to emphasize rare but high-impact events in the training process, addressing the inherent class imbalance in extreme precipitation predictions. Results demonstrate strong predictive performance in both the CDF and residual domains, with the proposed model accurately identifying anomalously high precipitation months. This hybrid GEV–deep learning approach offers a promising solution for early warning systems and long-term climate resilience planning in hydrologically sensitive regions. Full article
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
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26 pages, 6665 KB  
Article
Using Entity-Aware LSTM to Enhance Streamflow Predictions in Transboundary and Large Lake Basins
by Yunsu Park, Xiaofeng Liu, Yuyue Zhu and Yi Hong
Hydrology 2025, 12(10), 261; https://doi.org/10.3390/hydrology12100261 - 2 Oct 2025
Viewed by 712
Abstract
Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long [...] Read more.
Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
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39 pages, 10741 KB  
Article
Modeling the Dynamics of the Jebel Zaghouan Karst Aquifer Using Artificial Neural Networks: Toward Improved Management of Vulnerable Water Resources
by Emna Gargouri-Ellouze, Tegawende Arnaud Ouedraogo, Fairouz Slama, Jean-Denis Taupin, Nicolas Patris and Rachida Bouhlila
Hydrology 2025, 12(10), 250; https://doi.org/10.3390/hydrology12100250 - 26 Sep 2025
Viewed by 666
Abstract
Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate [...] Read more.
Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate its natural discharge dynamics prior to intensive exploitation (1915–1944). Given the fragmented nature of historical datasets, meteorological inputs (rainfall, temperature, and pressure) were reconstructed using a data recovery process combining linear interpolation and statistical distribution fitting. The hyperparameters of the artificial neural network (ANN) model were optimized through a Bayesian search. Three deep learning architectures—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained to model spring discharge. Model performance was evaluated using Kling–Gupta Efficiency (KGE′), Nash–Sutcliffe Efficiency (NSE), and R2 metrics. Hydrodynamic characterization revealed moderate variability and delayed discharge response, while isotopic analyses (δ18O, δ2H, 3H, 14C) confirmed a dual recharge regime from both modern and older waters. LSTM outperformed other models at the weekly scale (KGE′ = 0.62; NSE = 0.48; R2 = 0.68), effectively capturing memory effects. This study demonstrates the value of combining historical data rescue, ANN modeling, and hydrogeological insight to support sustainable groundwater management in data-scarce karst systems. Full article
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
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21 pages, 4181 KB  
Article
Addressing Volatility and Nonlinearity in Discharge Modeling: ARIMA-iGARCH for Short-Term Hydrological Time Series Simulation
by Mahshid Khazaeiathar and Britta Schmalz
Hydrology 2025, 12(8), 197; https://doi.org/10.3390/hydrology12080197 - 27 Jul 2025
Viewed by 1297
Abstract
Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes [...] Read more.
Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R2 values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
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Review

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45 pages, 3649 KB  
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
Cited by 2 | Viewed by 4817
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
(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)
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