Preprints
https://doi.org/10.5194/hess-2022-70
https://doi.org/10.5194/hess-2022-70
 
22 Mar 2022
22 Mar 2022
Status: this preprint is currently under review for the journal HESS.

Technical Note: Modeling Spatial Fields of Extreme Precipitation – A Hierarchical Bayesian Approach

Bianca Rahill-Marier1, Naresh Devineni2, and Upmanu Lall3 Bianca Rahill-Marier et al.
  • 1NCX, New York, NY
  • 2Department of Civil Engineering, City University of New York (City College), New York, NY 10031
  • 3Department of Earth and Environmental Engineering, Columbia Water Center, Columbia University, New York, NY 10027

Abstract. We introduce a hierarchical Bayesian model for modeling spatial rainfall for extreme events of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis. An extreme event is defined if any gaging site in the watershed experiences an annual maximum rainfall event, and the spatial field of rainfall at all sites corresponding to that occurrence is modeled. Applications to data from New York City demonstrate the effectiveness of the model for providing spatial scenarios that could be used for simulating loadings into the urban drainage system. Insights as to the homogeneity in spatial rainfall and its implications for modeling are provided by considering partial pooling in the Hierarchical Bayesian framework.

Bianca Rahill-Marier et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-70', Anonymous Referee #1, 18 Apr 2022 reply

Bianca Rahill-Marier et al.

Bianca Rahill-Marier et al.

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Short summary
We present a new approach to model extreme regional rainfall considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.