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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 346

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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Published Papers (1 paper)

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Research

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 325
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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