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Climate Change and Hydrological Processes, 2nd Edition
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Climate Change and Hydrological Processes, 2nd Edition

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 918

Special Issue Editors

Prof. Dr. Alina Barbulescu

E-Mail Website
Guest Editor
Department of Civil Engineering, Transilvania University of Brașov, 500036 Brașov, Romania
Interests: hydrology; time series analysis; applied statistics; mathematical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Romulus Costache

E-Mail Website
Guest Editor
National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686 Bucharest, Romania
Interests: hydrology; natural hazards; geographic information science; bivariate statistics; machine learning and artificial intelligence applied in the natural hazard’s susceptibility assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Ștefan Dumitriu

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Robotics in Constructions, Technical University of Civil Engineering, Calea Plevnei 59, 021242 Bucharest, Romania
Interests: water quality, environmental modeling, soil pollution, climate change, project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is an essential element for human life and security. In recent years, the apparition and intensification of extreme events has aggravated the water availability and quality, significantly affecting the population's well-being. Drought episodes intensify water scarcity. At the same time, rainfall intensity or frequency have increased in different regions worldwide. In this context, evaluating and forecasting the apparition of extreme events and mitigating their effects have become necessary not only as research topics, but also as factors for policymakers and decision makers to avoid or mitigate. In this context, the main topics of this Special Issue are as follows:

  • Influence of climate change in the water runoff process;
  • Future projection of flash flood susceptibility according to climate change scenarios;
  • The variability of the maximum river discharges according to climate change projections;
  • The impact of climate change on the frequency and severity of droughts;
  • Risk and uncertainty in detecting drought events;
  • Quantitative and qualitative analysis of extreme events;
  • Hazards and risks in drought assessment;
  • Integrating environmental economics into flood/drough risk management;
  • Modeling the correlation between the climate variables and hydrological processes.

Prof. Dr. Alina Barbulescu
Dr. Romulus Costache
Dr. Cristian Ștefan Dumitriu
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. 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

  • climate change scenarios
  • extreme events
  • risk assessment
  • multivariate analysis
  • artificial intelligence models

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Related Special Issue

Published Papers (3 papers)

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Research

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24 pages, 10659 KiB  
Article
Spatiotemporal Dynamics of Drought–Flood Abrupt Alternations and Their Delayed Effects on Vegetation Growth in Heilongjiang River Basin
by Haoyuan Ma, Jianyu Jing, Changlei Dai, Yijun Xu, Peng Qi and Hao Song
Water 2025, 17(10), 1419; https://doi.org/10.3390/w17101419 - 8 May 2025
Abstract
Drought–flood abrupt alternations (DFAAs) have a greater impact on ecosystems and socioeconomic environments than lone droughts or floods. Despite the significant impact of DFAAs, research has paid little attention to their evolutionary characteristics, particularly in relation to vegetation growth in the Heilongjiang River [...] Read more.
Drought–flood abrupt alternations (DFAAs) have a greater impact on ecosystems and socioeconomic environments than lone droughts or floods. Despite the significant impact of DFAAs, research has paid little attention to their evolutionary characteristics, particularly in relation to vegetation growth in the Heilongjiang River Basin. Therefore, this study focuses on the Heilongjiang River Basin and employs the DFAA Index to identify and analyze abrupt alternation events from 1970 to 2019. It also examines the annual and interannual distributions of vegetation growth changes from 2000 to 2019, based on the Normalized Difference Vegetation Index. Lastly, it utilizes correlation analysis to investigate the responsive relationship between vegetation growth and DFAA events. The results indicate the following: (1) Within the Heilongjiang River Basin, the number of drought-to-flood events increased over time, whereas the number of flood-to-drought events decreased over time. The frequency of mutation was relatively high in the northern region, low in the eastern region, elevated in spring and summer, and reduced in winter. (2) The Normalized Difference Vegetation Index was lowest in January, highest in July, and approximately 0 during the winter. The vegetation coverage reached its peak during the summer. (3) Vegetation changes in response to DFAAs exhibited a significant time lag. Vegetation changes in spring–summer lagged behind DFAA events by 3–4 months, while in summer–autumn, the lag was approximately 3 months. These results are of great significance for the early warning and prevention of DFAAs in the Heilongjiang River Basin. Full article
(This article belongs to the Special Issue Climate Change and Hydrological Processes, 2nd Edition)
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22 pages, 4618 KiB  
Article
Understanding Climate Change Impacts on Streamflow by Using Machine Learning: Case Study of Godavari Basin
by Ravi Ande, Chandrashekar Pandugula, Darshan Mehta, Ravikumar Vankayalapati, Prashant Birbal, Shashikant Verma, Hazi Mohammad Azamathulla and Nisarg Nanavati
Water 2025, 17(8), 1171; https://doi.org/10.3390/w17081171 - 14 Apr 2025
Viewed by 413
Abstract
The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, [...] Read more.
The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R2 assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R2, and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m3/s. The test results were 0.681, 0.91, 0.828, and 41.237 m3/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article
(This article belongs to the Special Issue Climate Change and Hydrological Processes, 2nd Edition)
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Review

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53 pages, 1194 KiB  
Review
An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence
by Mercedeh Taheri, Mostafa Bigdeli, Hanifeh Imanian and Abdolmajid Mohammadian
Water 2025, 17(9), 1384; https://doi.org/10.3390/w17091384 - 4 May 2025
Viewed by 308
Abstract
Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, water resource management, and climate studies. Among various approaches that are employed for estimating ET, the Penman–Monteith equation is [...] Read more.
Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, water resource management, and climate studies. Among various approaches that are employed for estimating ET, the Penman–Monteith equation is known as the widely accepted reference approach. However, the extensive data requirement of this method is a crucial challenge that limits its usage, particularly in data-scarce regions. Therefore, as an alternative approach, artificial intelligence (AI) models have gained prominence for estimating evapotranspiration because of their capacity to handle complicated relationships between meteorological variables and water loss processes. These models leverage large datasets and advanced algorithms to provide accurate and timely ET predictions. The current research aims to review previous studies addressing the application of the AI model in ET modeling under four main categories: neuron-based, tree-based, kernel-based, and hybrid models. The results of this study indicated that traditional models like the Penman–Monteith (PM) require extensive input data, while AI-based approaches offer promising alternatives due to their ability to model complex nonlinear relationships. Despite their potential, AI models face challenges such as overfitting, interpretability, inconsistent input variable selection, and lack of integration with physical ET processes, highlighting the need for standardized input configurations, better pre-processing techniques, and incorporation of hydrological and remote sensing data. Full article
(This article belongs to the Special Issue Climate Change and Hydrological Processes, 2nd Edition)
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