Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Lin, Xinming; Fan, Jiwen; Hou, Z. Jason; Wang, Jingyu

doi:10.1029/2022MS003334

Title: Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Journal Article · 26 February 2023 · Journal of Advances in Modeling Earth Systems

DOI: https://doi.org/10.1029/2022MS003334 · OSTI ID:1958905

Lin, Xinming ^[1];

^[1];

^[2]

Pacific Northwest National Laboratory Richland WA USA
Pacific Northwest National Laboratory Richland WA USA, National Institute of Education Nanyang Technological University Singapore Singapore

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).

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231; AC05-76RL01830

OSTI ID:: 1958905

Report Number(s):: PNNL-SA-183247; e2022MS003334

Journal Information:: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 3 Vol. 15; ISSN 1942-2466

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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