Pérez et al. (2026) Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-07-14
• Authors: Gabriel Pérez, Scott T. DeNeale, Shih-Chieh Kao, Felipe Quintero
• DOI: 10.1016/j.jhydrol.2026.136022

Research Groups

• School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK, USA
• Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
• IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, Iowa, USA

Short Summary

This study evaluates the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach. Using modern high-resolution gridded precipitation observations and atmospheric reanalysis products, the authors quantify PMP variability across four methodological dimensions: storm sample size, transposition domain, maximization procedures, and storm rotation.

Objective

• Investigate how PMP estimates respond to analyst-defined decisions such as storm selection, transposition domain, and rotation assumptions.
• Advance toward more reproducible, transparent, and physically defensible methodologies for PMP estimation.

Study Configuration

• Spatial Scale: Watershed scale (Red Rock watershed in Iowa, USA)
• Temporal Scale: 2002–2023

Methodology and Data

• Models used: None mentioned explicitly
• Data sources:
  • AORC precipitation dataset (hourly gridded precipitation estimates derived from radar, satellite, gauge, and numerical weather prediction products)
  • ERA5 reanalysis dataset (atmospheric variables including precipitable water)

Main Results

• PMP estimates vary significantly with storm sample size, transposition domain, maximization procedures, and storm rotation.
• Storm rotation alone can amplify PMP by more than 25%.
• The legacy PMP estimate of 308 mm is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions.

Contributions

• This study provides new insights into the sensitivity of PMP estimates to key methodological decisions.
• The findings highlight the need for systematic sensitivity and uncertainty analysis in PMP estimation.
• The results have implications for current and future PMP practices, particularly in the context of climate resilience and shifting storm patterns.

Funding

• Not specified

Citation

@article{Pérez2026Reevaluating,
  author = {Pérez, Gabriel and DeNeale, Scott T. and Kao, Shih-Chieh and Quintero, Felipe},
  title = {Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136022},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136022}
}