Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 8670
Special Issue Editors
Interests: hydrometeorology; climate change; numerical models
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; flood frequency; mixed populations; nonstationarity; hydrometeorology
Interests: machine learning; data mining; data science; neural networks; social network analysis
Special Issue Information
Dear Colleagues,
We are calling for submissions for the Special Issue “Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models”.
Hydrometeorological extremes such as droughts and floods due to climate change present an urgent issue. Long-lasting droughts in the western U.S. are such an example, along with more frequent floodings across the globe. These extremes are associated with increasing hydroclimatic intensity and more moisture held in air due to Clausius–Clapeyron scaling. While major efforts have been made to characterize and predict hydrometeorological extremes, this phenomenon remains a challenge due to the lack of proper approaches and metrics. This Special Issue aims to develop novel approaches and metrics to characterize and predict hydrometeorological extremes. We encourage submissions that are focused on leveraging machine learning techniques and numerical models. All related manuscripts are welcome. Topics of interest include, but are not limited to: the application of machine learning and numerical models for advancing the prediction skill of hydrometeorological extremes; the development of new approaches and metrics to quantify and predict hydrometeorological extremes; and the application of machine learning or other novel methods to improve climate models and hydrological models. Review articles are also encouraged.
Dr. Wei Zhang
Dr. Nancy A. Barth
Dr. Hamid Karimi
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
- hydrometeorological extremes
- machine learning
- approaches and metrics