1. A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins.
- Author
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Liu, Yuan, Wright, Daniel B., and Lorenz, David J.
- Subjects
CLIMATE change models ,RAINFALL ,WATER vapor transport ,WINTER storms ,STORMS ,ATMOSPHERIC models ,WATERSHEDS ,FLOODS - Abstract
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. Key Points: A nonstationary stochastic rainfall generator driven by water vapor transport is proposed to simulate storms in the Mississippi Basin1,000 synthetic years of precipitation events were simulated based on large‐scale atmospheric variables from a global climate modelSimulations showed an increase in 1‐ to 500‐year precipitation in the future (2022–2050) compared to the historical period (1979–2021) [ABSTRACT FROM AUTHOR]
- Published
- 2024
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