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Abstract Tropical cyclones (TCs) generate extreme precipitation with severe impacts across large coastal and inland areas, calling for accurate frequency estimation methods. Statistical approaches that take into account the physical mechanisms responsible for these extremes can help reduce the estimation uncertainty. Here we formulate a mixed‐population Metastatistical Extreme Value Distribution explicitly incorporating non‐TC and TC‐induced rainfall and evaluate its implications on long series of daily rainfall for six major U.S. urban areas impacted by these storms. We find statistically significant differences between the distributions of TC‐ and non‐TC‐related precipitation; moreover, including mixtures of distributions improves the estimation of the probability of extreme precipitation where TCs occur more frequently. These improvements are greater when rainfall aggregated over durations longer than one day are considered.
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A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance
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 5000 to 100 000 km2 and durations from 2 to 72 h 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.
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- Award ID(s):
- 1749638
- PAR ID:
- 10403741
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 26
- Issue:
- 20
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 5241 to 5267
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-26-5241-2022
