Advances in the Measurement, Utility and Evaluation of Precipitation Observations
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".
Deadline for manuscript submissions: 12 August 2025 | Viewed by 47
Special Issue Editors
Interests: rainfall-runoff modeling; data assimilation; machine learning
Special Issue Information
Dear Colleagues,
Precipitation is a critical component of the hydrological cycle, directly impacting various hydrologic processes such as runoff, groundwater recharge, and flood management. The accurate measurement and evaluation of precipitation is vital for enhancing water resource management, refining climate models, and improving disaster preparedness strategies. This Special Issue seeks contributions that address the challenges and innovations in precipitation measurement, including the latest technological advancements (e.g., deep learning, Internet of Things), the effectiveness of different observational techniques, and the evaluation of precipitation data across diverse climatic and geographic contexts. Topics of interest include, but are not limited to, remote sensing technologies, ground-based observations, the integration of multiple data sources, and the development of novel methodologies to improve precipitation measurement accuracy.
The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about a comprehensive understanding of how precipitation observations can be enhanced, applied, and critically assessed to support hydrologic research and applications.
This Special Issue will welcome manuscripts that link the following themes:
- Statistical Modeling of Rainfall Patterns;
- Deep Learning Approaches for Precipitation Measurement;
- Leveraging Internet of Things (IoT) for Enhanced Precipitation Estimation;
- Multi-Source Rainfall Observation Fusion;
- Uncertainty and Bias Analysis in Rainfall Data;
- Impacts of Climate Change on Precipitation Trends;
- Remote Sensing Techniques for Precipitation Observation;
- Satellite-Based Precipitation Monitoring and Evaluation.
We look forward to receiving your original research articles and reviews.
Prof. Dr. Jiangjiang Zhang
Prof. Dr. Junliang Jin
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
- precipitation observations
- deep learning
- data fusion
- remote sensing
- uncertainty quantification
- rainfall-runoff modeling
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