Articles | Volume 26, issue 1
© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.
A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow
- Final revised paper (published on 12 Jan 2022)
- Supplement to the final revised paper
- Preprint (discussion started on 16 Jun 2021)
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
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 manuscript “A space-time Bayesian hierarchical modeling framework for projection of seasonal streamflow extremes” by Ossandón et al. proposes a Bayesian Hierarchical Model (BHM) to project seasonal streamflow extremes for multiple catchments in a river basin up to 2 months lead time. The spatio-temporal dependence is modelled through a Gaussian elliptical copula and Generalised Extreme Value margins with non-stationary parameters and covariates. The proposed model is used to model streamflow extremes at 7 gauges location in the Upper Colorado River Basin (UCRB). The proposed framework and its application to the UCRB are interesting and well presented. I have some (minor) comments, especially concerning the application of the model to the UCRB.
Why are the snow and temperature covariates spatially averaged? For such small catchments, wouldn’t local covariates (e.g. the snow water equivalent accumulated within each of the 7 catchments) be more skillful to predict (local) streamflow extremes? Would the choice of local covariates improve the performance of the projections for the 7 sites shown in figure A4b and c?
The authors state that, for computing the regional average, they considered (and averaged over) all the snow and temperature stations in the UCRB. I would suggest adding a map, showing the location of such stations used for the covariates and/or a table with summary information. Considering that all the 7 sites are located very close to each other in one part of the UCRB (fig.2), are the selected stations of the covariates, representative for the sites where the streamflow is recorded? I also suggest plotting the timeseries of the local covariates (i.e., at each station) together with the regional average and the seasonal streamflow extremes.