Bhatt et al. (2025) A Framework for Evaluating Uncertainty From Multiple Sources in Probable Maximum Precipitation Estimation by the Hershfield Method Using Imprecise Probability

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

Identification

• Journal: Water Resources Research
• Year: 2025
• Date: 2025-12-01
• Authors: Jaya Bhatt, V. V. Srinivas
• DOI: 10.1029/2024wr038052

Research Groups

Not explicitly mentioned in the abstract.

Short Summary

This paper proposes a novel framework based on imprecise probability theory to quantify and attribute uncertainty in Hershfield method-based Probable Maximum Precipitation (PMP) estimates, identifying key uncertainty sources and their contributions in case studies across Indian and US river basins.

Objective

• To propose a novel framework based on imprecise probability (IP) theory to quantify the overall uncertainty in PMP estimates arising from multiple sources and discern contributions from individual sources and their combinations, addressing the lack of methodologies to identify significant contributors to PMP uncertainty and determine overall uncertainty bounds.

Study Configuration

• Spatial Scale: Two major flood-prone river basins in India (Mahanadi and Godavari) and the Brazos River basin in the United States.
• Temporal Scale: Long-term historical precipitation records for PMP estimation.

Methodology and Data

• Models used: Hershfield method (HM) for Probable Maximum Precipitation (PMP) estimation; Imprecise Probability (IP) theory for uncertainty quantification.
• Data sources: Reasonably long precipitation observations (historical records).

Main Results

• The proposed imprecise probability framework effectively determines the IP bounds of PMP estimates.
• For the Indian river basins, the highest contributor to the overall uncertainty in PMP estimates is the combined/interaction component of all four analyzed uncertainty sources.
• For the United States basin, the highest contributor to uncertainty is the statistical sampling effect of precipitation observations.
• The least contribution to uncertainty is consistently from envelope curve construction across all case studies.
• Options and guidelines are provided to reduce the uncertainty (interval between IP bounds) arising from different sources in HM-based PMP estimation.

Contributions

• Introduces a novel framework based on imprecise probability theory to quantify and attribute overall uncertainty in Hershfield method-based PMP estimates.
• Provides a methodology to identify significant individual and combined contributors to PMP uncertainty, including sampling effects, regional definition, envelope curve space preparation, and curve construction.
• Establishes a method for determining overall uncertainty bounds for PMP estimates.
• Offers practical guidelines to reduce uncertainty in PMP estimation, enhancing the reliability of design flood assessments.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Bhatt2025Framework,
  author = {Bhatt, Jaya and Srinivas, V. V.},
  title = {A Framework for Evaluating Uncertainty From Multiple Sources in Probable Maximum Precipitation Estimation by the Hershfield Method Using Imprecise Probability},
  journal = {Water Resources Research},
  year = {2025},
  doi = {10.1029/2024wr038052},
  url = {https://doi.org/10.1029/2024wr038052}
}