TxRAIN-Observational: A Hierarchical Bayesian Spatial Framework To Assess Nonstationary Rainfall Intensity, Frequency, And Duration In Texas

Authors

Yuchen Lu

Benjamin Seiyon Lee

James Doss-Gollin

John W. Nielsen-Gammon

Rewati Niraula

Published

December 10, 2024

Link

Estimates of extreme precipitation probabilities are widely used in engineering…

BibTeX

@inproceedings{lu_agu:2024,
  eventtitle = {{{AGU24}}},
  urldate = {2025-01-12},
  url = {https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1624973},
  publisher = {AGU},
  date = {2024-12-10},
  author = {Lu, Yuchen and Lee, Benjamin Seiyon and Doss-Gollin, James and Nielsen-Gammon, John W. and Niraula, Rewati},
  title = {{{TxRAIN-Observational}}: {{A Hierarchical Bayesian Spatial Framework}} to {{Assess Nonstationary Rainfall Intensity}}, {{Frequency}}, and {{Duration}} in {{Texas}}},
}

References

Lu, Y., Lee, B. S., Doss-Gollin, J., Nielsen-Gammon, J. W., & Niraula, R. (2024). TxRAIN-Observational: A Hierarchical Bayesian Spatial Framework to Assess Nonstationary Rainfall Intensity, Frequency, and Duration in Texas. In. Presented at the AGU24, AGU. Retrieved from https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1624973