Preprints
https://doi.org/10.5194/egusphere-2022-391
https://doi.org/10.5194/egusphere-2022-391
 
13 Jun 2022
13 Jun 2022
Status: this preprint is open for discussion.

A Storm-centered Multivariate Modeling of Extreme Precipitation Frequency Based on Atmospheric Water Balance

Yuan Liu and Daniel Benjamin Wright Yuan Liu and Daniel Benjamin Wright
  • Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, 53706, USA

Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5,000 to 100,000 km2 and durations from 2 to 72 hours in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.

Yuan Liu and Daniel Benjamin Wright

Status: open (until 08 Aug 2022)

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Yuan Liu and Daniel Benjamin Wright

Yuan Liu and Daniel Benjamin Wright

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Short summary
We present a new approach to estimate extreme rainfall probability and severity using the atmospheric water balance, where precipitation is the sum of water vapor components moving in and out of a storm. We apply our method to the Mississippi Basin and its five major subbasins. Our approach achieves a good fit to reference precipitation, indicating that the rainfall probability estimation can benefit from additional information from physical processes that control rainfall.