Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 1
Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 1
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: 4,582 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,489 1,007 86 4,582 167 90 105
  • HTML: 3,489
  • PDF: 1,007
  • XML: 86
  • Total: 4,582
  • Supplement: 167
  • BibTeX: 90
  • EndNote: 105
Views and downloads (calculated since 16 Jun 2021)
Cumulative views and downloads (calculated since 16 Jun 2021)

Viewed (geographical distribution)

Total article views: 4,582 (including HTML, PDF, and XML) Thereof 4,375 with geography defined and 207 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
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.
Read more
Share