Extreme Rainfall and Hydrological Extremes: Monitoring and Prediction Using Machine Learning and Physical Modeling
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 42
Special Issue Editors
Interests: precipitation; extremes; climate change; drought; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; climatology; meteorology; drought propagation; rainfall variability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Extreme rainfall events are becoming more frequent and severe due to climate variability and human activities, presenting considerable threats to water security, infrastructure, ecosystems, and human lives. Precisely monitoring and predicting these events is essential for effective disaster preparedness, early warning systems, and sustainable water resource management. This Special Issue seeks to unite innovative research and advanced methodologies that improve our understanding of extreme rainfall dynamics across different spatial and temporal scales.
We invite submissions focused on remote sensing techniques, ground-based observational networks, statistical and machine learning models, hydrological modeling, and integrated climate systems for rainfall prediction. Special attention will be given to case studies from vulnerable and data-poor areas, uncertainty quantification, and blending traditional knowledge with contemporary tools. Interdisciplinary approaches that bridge atmospheric science, hydrology, climatology, and disaster risk reduction are strongly encouraged.
The goal is to create a comprehensive platform for sharing knowledge on advanced monitoring techniques, novel prediction frameworks, and practical solutions that enhance climate resilience and adaptive water governance in the context of extreme weather events, such as rainfall.
Prof. Dr. Muhammad Abrar Faiz
Dr. Zhaoqiang Zhou
Guest Editors
Manuscript Submission Information
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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
- extreme rainfall
- prediction
- hydrology
- remote sensing
- early warning
- climate change
- machine learning
- disaster risk
- data assimilation
- flood forecasting
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