Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
https://doi.org/10.5194/hess-26-149-2022
Research article
 | 
12 Jan 2022
Research article |  | 12 Jan 2022

A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber

Viewed

Total article views: 3,625 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,801 751 73 3,625 117 58 56
  • HTML: 2,801
  • PDF: 751
  • XML: 73
  • Total: 3,625
  • Supplement: 117
  • BibTeX: 58
  • EndNote: 56
Views and downloads (calculated since 16 Jun 2021)
Cumulative views and downloads (calculated since 16 Jun 2021)

Viewed (geographical distribution)

Total article views: 3,625 (including HTML, PDF, and XML) Thereof 3,433 with geography defined and 192 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Dec 2024
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.