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Hydrology
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Global Rainfall-Runoff Modelling
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Global Rainfall-Runoff Modelling

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2306

Special Issue Editors

Dr. Kang Xie

E-Mail Website
Guest Editor
The National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210098, China
Interests: hydrological modeling; rainfall runoff modeling; water resources engineering; hydrologic and water resource modeling
Dr. Ziwei Li

E-Mail Website
Guest Editor
Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China
Interests: cold-region hydrology; soil–water–energy interactions; climate change impacts; process-based hydrological modelling
Dr. Xiongpeng Tang

E-Mail Website
Guest Editor
Center for Water Research, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai 519087, China
Interests: water–food–energy–ecosystem nexus; physics-informed machine learning for hydrological modeling; groundwater–surface water interactions; simulation-optimization-based integration with water resources management; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Maoyuan Feng

E-Mail Website
Guest Editor
Geosciences, Environment and Society Department, Université libre de Bruxelles, 1050 Brussels, Belgium
Interests: hydrological modelling; reservoir operations; lake ecosystems; greenhouse emissions

Special Issue Information

Dear Colleagues,

Rainfall-runoff processes are the core of the global water cycle, linking atmospheric precipitation to surface/groundwater runoff and underpinning critical applications such as flood forecasting, drought mitigation and water resource planning. Under global change, increasing climate variability and human activities have shifted hydrological systems from steady to non-steady states, challenging traditional models that rely on stationary assumptions. Historically, rainfall-runoff modelling has evolved from empirical methods to process-based models and recent hybrid approaches integrating machine learning and uncertainty analysis, yet gaps remain in global-scale integration and adaptation to ungauged basins.

The goal of this Special Issue is to collect papers (original research articles and review papers) to provide insights into the latest advances in global rainfall-runoff modelling, including uncertainty quantification under non-stationary conditions, model adaptation to human-induced and climatic changes and innovative applications in large river basins or ungauged regions. This aligns with the journal’s scope of advancing hydrological science and its practical implications for global water management.

This Special Issue will welcome manuscripts that link the following themes:

  • Non-stationary rainfall-runoff simulation under climate change and human activities
  • Parameterization and validation of global-scale models in ungauged basins
  • Integration of AI and remote-sensing data in rainfall-runoff modelling
  • Uncertainty quantification for extreme event prediction
  • Cross-basin comparison of hydrological responses to global environmental changes

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

Dr. Kang Xie
Dr. Ziwei Li
Dr. Xiongpeng Tang
Dr. Maoyuan Feng
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. 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

  • global rainfall-runoff modelling
  • non-stationary hydrology
  • uncertainty quantification
  • AI in hydrology
  • ungauged basins

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Published Papers (3 papers)

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Research

19 pages, 28283 KB  
Article
Evaluation of Coupled Hydrological–Hydrodynamic Scheme Applicability Under Reservoir Regulation in the Huai River Basin
by Zhengyang Tang, Yichen Zhao, Zhangkang Shu, Ziwei Li, Yuchen Li and Junliang Jin
Hydrology 2026, 13(5), 122; https://doi.org/10.3390/hydrology13050122 - 30 Apr 2026
Viewed by 293
Abstract
Accurate flood simulation in regulated, low-lying river basins is crucial for forecasting and risk mitigation, but performance depends strongly on whether models represent floodplain hydrodynamics and human regulation. This study evaluates three coupled hydrological–hydrodynamic schemes in the Huai River Basin upstream of Bengbu [...] Read more.
Accurate flood simulation in regulated, low-lying river basins is crucial for forecasting and risk mitigation, but performance depends strongly on whether models represent floodplain hydrodynamics and human regulation. This study evaluates three coupled hydrological–hydrodynamic schemes in the Huai River Basin upstream of Bengbu Station using identical meteorological forcing and VIC-generated runoff: (I) a linear routing scheme (VIC–Routing), (II) a natural hydrodynamic scheme (VIC–CaMa-Flood), and (III) an extended hydrodynamic scheme that incorporates reservoir regulation and levee effects (VIC–CaMa-Flood with Dam). Results reveal clear spatial differences in scheme suitability. The linear routing scheme performs best in upstream reaches, with NSE and KGE generally exceeding 0.81, but tends to overestimate peak discharge in downstream lowland sections. Incorporating hydrodynamic processes and regulation representation further reduces peak flow bias. Scheme III achieves the most consistent downstream improvement, particularly for high flows (>2000 m3/s), with NSE exceeding 0.80 in long-term simulations and improved agreement with satellite-driven inundation patterns. However, simplified reservoir operating rules can increase uncertainty in water level dynamics. During the 2020 plum rain flood, Scheme II yielded more accurate water levels in some reaches, suggesting that generalized operation rules may introduce compensating errors even when discharge accuracy improves. Overall, reliable flood simulation in well-managed basins requires an explicit representation of both floodplain hydrodynamics and regulation, and scheme selection should be guided by the dominant controls along the river network. Full article
(This article belongs to the Special Issue Global Rainfall-Runoff Modelling)
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21 pages, 4975 KB  
Article
Spatiotemporal Variability and Extreme Precipitation Characteristics in Arid Region of Ordos, China
by Shengjie Cui, Shuixia Zhao, Chao Li, Yingjie Wu, Xiaomin Liu, Ping Miao, Shiming Bai, Yajun Zhou and Jinrong Li
Hydrology 2026, 13(2), 68; https://doi.org/10.3390/hydrology13020068 - 11 Feb 2026
Viewed by 667
Abstract
Studying the precipitation characteristics and extreme precipitation events in arid and semi-arid regions is of significant baseline value for optimizing water resource allocation and utilizing precipitation resources. Utilizing multi-scale ERA5 precipitation data from 1960 to 2023, this study focuses on the typical arid [...] Read more.
Studying the precipitation characteristics and extreme precipitation events in arid and semi-arid regions is of significant baseline value for optimizing water resource allocation and utilizing precipitation resources. Utilizing multi-scale ERA5 precipitation data from 1960 to 2023, this study focuses on the typical arid and semi-arid region of Ordos as the research area. Precipitation exceeding the 90th percentile was defined as extreme precipitation, and three indices—extreme precipitation amount (EPA), extreme precipitation frequency (EPF), and extreme precipitation proportion (EPP)—were used to investigate its characteristics in the study area. Additionally, three typical extreme precipitation events in recent years were analyzed to study the precipitation process of these typical events. The main results are as follows: The annual average precipitation in the study area ranges from 170.3 to 606.1 mm, with an average of 378.5 mm, which has been on a declining trend over the years, with an average annual decrease of 1.2 mm. Overall, 70% of the precipitation is concentrated in the months of June to September. The daily average of extreme precipitation in Ordos is 18.7 mm and the annual average number of extreme precipitation days ranges from 8 to 13 days, with an average annual number of extreme precipitation days being 11. Extreme precipitation accounts for more than 50% of the total precipitation. Among all areas analyzed, Jungar Banner demonstrates the greatest vulnerability to intense rainfall events. Typical extreme precipitation events in Ordos are characterized by short-duration heavy rainfall, with the rain peak ratio coefficients of the three events ranging from 0.62 to 0.72, exhibiting a distinct “post-peak” pattern. These findings provide scientific support for water resource management and disaster prevention strategies in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Global Rainfall-Runoff Modelling)
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22 pages, 3994 KB  
Article
Study on Temporal Convolutional Network Rainfall Prediction Model and Its Interpretability Guided by Physical Mechanisms
by Dongfang Ma, Yunliang Wen, Chongxu Zhao and Chunjin Zhang
Hydrology 2026, 13(1), 38; https://doi.org/10.3390/hydrology13010038 - 19 Jan 2026
Viewed by 620
Abstract
Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal [...] Read more.
Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article
(This article belongs to the Special Issue Global Rainfall-Runoff Modelling)
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