Regional non-stationary frequency analysis based on extreme hydrological regionalization and hierarchical Bayesian framework

May 1, 2026Journal of Hydraulic Engineering

View at Publisher

Ask AI

Bookmark

Share

Key Points

This research aims to develop a robust method for non-stationary hydrological frequency analysis by incorporating spatial correlations among site sequences.
Utilized PAM clustering algorithm based on the F-madogram to classify hydrological regions.
Constructed hierarchical Bayesian models including non-pooled, partially pooled, and fully pooled approaches for frequency analysis.
Conducted a case study using annual maximum 24-hour extreme rainfall data from the Xiangjiang River Basin.
Divided 36 rain gauge sites in the Xiangjiang River Basin into 3 hydrological regions using spatial clustering.
Significant positive correlation noted between climate driving factors and rainfall, particularly in Hydrological Region I with FMA Nino12.
Partially pooled model showed optimal performance, reducing uncertainty of regional parameters by 10% to 35% compared to stationary models.

Demonstrates improved hydrological frequency analysis in river basins, suggesting better design strategies.

No abstract yet. The journal hasn't published one we can show.

No takes yet. Share an insight, caveat, or question.

ZENG et al. (2026) studied this question.

View at Publisher

Ask AI

Bookmark

Share