Novel Approaches and Metrics to Characterize and Predict Hydrometeorological Extremes: Machine Learning and Numerical Models

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

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