A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Extreme precipitation modeling and Probable Maximum Precipitation (PMP) estimation in Chile
Study region: Maipo River Basin (MRB) in Chile Study focus: Aging infrastructures and climate-change emphasize an urgent need to improve extreme precipitation modeling and ultimately update Probable Maximum Precipitation (PMP) estimates. Motivated by this background, this study first examined the optimal physics parameterizations for modeling extreme precipitation. We then estimated PMP using the model-based method, which is compared with traditional methods-based PMP estimates. Finally, the exceedance probability of the estimated PMP was evaluated based on precipitation amount and its atmospheric drivers. New hydrological insights: Among the 48 patterns of physical parameterization combinations in the Weather Research and Forecasting (WRF) model, the combination of Stony–Brook University microphysics, BouLac PBL, and Grell–Freitas cumulus schemes resulted in the lowest RMSE of 72-hr MRB-average precipitation. We used the WRF with optimized parameterizations to estimate PMP, employing the ensemble runs of moisture amplification and storm transposition. The 72-hr MRB-average PMP was estimated to be 323.7 mm in the model-based method, whereas it was 348.3 mm in the moisture maximization method. The statistical method computed the station-average 72-hr PMP as 515.8 mm (standard deviation: 147.4 mm) within the MRB. Our analysis focusing on physical mechanisms showed that the integrated water vapor trasnport (IVT) change strongly drove the precipitation change on a basin scale and on hourly and event timescales for the target events. The IVT magnitude driving the PMP scenario was found to fall in return periods of ∼44.5 years, indicating that the estimated PMP was driven by moisture flux within the range of historical observations, suggesting its physical credibility for practical applications.
Extreme precipitation modeling and Probable Maximum Precipitation (PMP) estimation in Chile
Study region: Maipo River Basin (MRB) in Chile Study focus: Aging infrastructures and climate-change emphasize an urgent need to improve extreme precipitation modeling and ultimately update Probable Maximum Precipitation (PMP) estimates. Motivated by this background, this study first examined the optimal physics parameterizations for modeling extreme precipitation. We then estimated PMP using the model-based method, which is compared with traditional methods-based PMP estimates. Finally, the exceedance probability of the estimated PMP was evaluated based on precipitation amount and its atmospheric drivers. New hydrological insights: Among the 48 patterns of physical parameterization combinations in the Weather Research and Forecasting (WRF) model, the combination of Stony–Brook University microphysics, BouLac PBL, and Grell–Freitas cumulus schemes resulted in the lowest RMSE of 72-hr MRB-average precipitation. We used the WRF with optimized parameterizations to estimate PMP, employing the ensemble runs of moisture amplification and storm transposition. The 72-hr MRB-average PMP was estimated to be 323.7 mm in the model-based method, whereas it was 348.3 mm in the moisture maximization method. The statistical method computed the station-average 72-hr PMP as 515.8 mm (standard deviation: 147.4 mm) within the MRB. Our analysis focusing on physical mechanisms showed that the integrated water vapor trasnport (IVT) change strongly drove the precipitation change on a basin scale and on hourly and event timescales for the target events. The IVT magnitude driving the PMP scenario was found to fall in return periods of ∼44.5 years, indicating that the estimated PMP was driven by moisture flux within the range of historical observations, suggesting its physical credibility for practical applications.
Extreme precipitation modeling and Probable Maximum Precipitation (PMP) estimation in Chile
Yusuke Hiraga (author) / Joaquin Meza (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Extreme precipitation modeling and Probable Maximum Precipitation (PMP) estimation in Chile
| Elsevier | 2025
Estimation of Probable Maximum Precipitation (PMP) or Extreme Point Rainfall over India
| British Library Conference Proceedings | 2000
Estimating the Probable Maximum Precipitation
| ASCE | 2021
Estimating the Probable maximum Precipitation
| ASCE | 2021
Generalised Probable Maximum Precipitation Estimation Techniques for Australia
| British Library Online Contents | 1994