A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance

Liu, Yuan; Wright, Daniel B.

doi:10.5194/hess-26-5241-2022

Cited by 2 publications

(4 citation statements)

References 118 publications

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“…Rainstorms associated with strong water vapor transport were identified over the MRB in ERA5 and CESM2. First, we applied a storm tracking method STARCH (Y. Liu & Wright, 2022b) to identify periods of intense water vapor transport over the tracking domain (29–50°N, 79–113°W, Figure 1a). STARCH applies a high threshold (500 kg m −1 s −1 ) to the IVT fields to identify regions of intense water vapor transport at each time step.…”

Section: Methodsmentioning

confidence: 99%

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

Liu,

Wright,

Lorenz

2024

Water Resources Research

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Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

show abstract

Section: Methodsmentioning

confidence: 99%

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

Liu,

Wright,

Lorenz

2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, we applied a storm tracking method STARCH (Y. Liu & Wright, 2022b) to identify periods of intense water vapor transport over the tracking domain (29-50° N, 79-113° W, Figure 1a). STARCH applies a high threshold (500 kg m -1 s -1 ) to the IVT fields to identify regions of intense water vapor transport at each time step.…”

Section: Rainstorm Identificationmentioning

confidence: 99%

“…The overlapping ratio between two consecutive IVT objects was defined as follows (Liu & Wright, 2022):…”

Section: Data Availability Statementmentioning

confidence: 99%

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A climate model-informed nonstationary stochastic rainfall generator for design flood analyses in continental-scale river basins

Liu,

Wright,

Lorenz

2023

Preprint

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Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6-hour and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by strong water vapor transport from the Gulf of Mexico—the key flood-generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large-scale atmospheric fields from a climate model, reflecting both spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine-scale precipitation arising from a specific large-scale atmospheric environment. Model parameters were fitted for each month from December-May, based on storms identified from 1979-2021 ERA5 reanalysis data and AORC precipitation. Validation showed good consistency in key storm characteristics between StormLab simulations and AORC data. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agreed well with AORC data and displayed bounded tail behavior. To our knowledge, this is the first SRG simulating nonstationary, anisotropic high-resolution precipitation over continental-scale river basins, demonstrating the value of conditioning such stochastic models on large-scale atmospheric variables. The simulated events provide a wide range of extreme precipitation scenarios that can be further used for design floods in the MRB.

show abstract

A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance

Cited by 2 publications

References 118 publications

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

A climate model-informed nonstationary stochastic rainfall generator for design flood analyses in continental-scale river basins

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