Liu et al. (2025) A Nonstationary Probabilistic Approach for Probable Maximum Precipitation Estimation Based on Global Climate Model Large Ensembles

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Hydrometeorology
• Year: 2025
• Date: 2025-11-26
• Authors: Yuan Liu, Daniel B. Wright, Felipe Quintero, John F. England, James A. Smith, Lei Yan
• DOI: 10.1175/jhm-d-25-0098.1

Research Groups

Not specified in the abstract.

Short Summary

This study proposes a novel approach integrating a stochastic rainfall generator (StormLab) with a nonstationary generalized extreme value (GEV) model to estimate probable maximum precipitation (PMP) and probable maximum flood (PMF) under varying climate conditions. The approach projects significant increases in PMP (15%–25%) and PMF (35%–36%) by 2100 in the upper Red River basin, highlighting the impact of climate change on extreme events.

Objective

• To develop and apply a new approach for estimating probable maximum precipitation (PMP) with specific annual exceedance probabilities (AEPs) across varying climate periods, addressing limitations of conventional PMP methods such as uncertainty, subjectivity, and the assumption of a stationary climate.

Study Configuration

• Spatial Scale: Upper Red River basin (south-central United States); PMP estimates for areas ranging from 25.9 km² to 51 800 km²; precipitation fields at 0.03° resolution.
• Temporal Scale: Precipitation field simulations from 1901 to 2100; PMP/PMF durations from 6 hours to 360 hours; precipitation fields at 6-hour resolution.

Methodology and Data

• Models used: Stochastic rainfall generator (StormLab), nonstationary generalized extreme value (GEV) model, global climate model (GCM), hydrologic model.
• Data sources: Simulated 10 000 years of high-resolution (6-hour, 0.03°) precipitation fields generated by StormLab, driven by 50 ensembles of a global climate model (GCM).

Main Results

• A projected increase of 15%–25% in PMP (with an annual exceedance probability of 10⁻⁴) from 2020 to 2100 across various spatial and temporal scales.
• Higher increases of 35% and 36% are projected for PMF in two major tributaries, also with an annual exceedance probability of 10⁻⁴.

Contributions

• Introduces a novel approach that integrates a stochastic rainfall generator (StormLab) with a nonstationary generalized extreme value (GEV) model to estimate PMP and PMF under a changing climate, overcoming limitations of conventional stationary methods.
• Demonstrates the value of using stochastic rainfall models and large ensembles from global climate models for robust PMP and PMF analysis in a nonstationary climate context.

Funding

Not specified in the abstract.

Citation

@article{Liu2025Nonstationary,
  author = {Liu, Yuan and Wright, Daniel B. and Quintero, Felipe and England, John F. and Smith, James A. and Yan, Lei},
  title = {A Nonstationary Probabilistic Approach for Probable Maximum Precipitation Estimation Based on Global Climate Model Large Ensembles},
  journal = {Journal of Hydrometeorology},
  year = {2025},
  doi = {10.1175/jhm-d-25-0098.1},
  url = {https://doi.org/10.1175/jhm-d-25-0098.1}
}