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Machine learning of key variables impacting extreme precipitation in various regions of the contiguous United States
Citation
Lin, X., Fan, J., Hou, Z. J., & Wang, J. (2023). Machine learning of key variables impacting extreme precipitation in various regions of the contiguous United States. Journal of Advances in Modeling Earth Systems, 15(3), Article e2022MS003334. https://doi.org/10.1029/2022ms003334
Abstract
Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large-scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non-negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).
Date Issued
2023
Publisher
Wiley
Journal
Journal of Advances in Modeling Earth Systems
DOI
10.1029/2022ms003334