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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-270', Anonymous Referee #1, 01 Oct 2021
    • AC1: 'Reply on RC1', Álvaro Ossandón, 10 Nov 2021
  • RC2: 'Comment on hess-2021-270', Anonymous Referee #2, 28 Oct 2021
    • AC2: 'Reply on RC2', Álvaro Ossandón, 10 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (22 Nov 2021) by Stacey Archfield
AR by Álvaro Ossandón on behalf of the Authors (22 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (23 Nov 2021) by Stacey Archfield

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Álvaro Ossandón on behalf of the Authors (23 Dec 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (03 Jan 2022) by Stacey Archfield

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.