Articles | Volume 22, issue 8
Research article
23 Aug 2022
Research article |  | 23 Aug 2022

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-290', Anonymous Referee #1, 22 Dec 2021
    • AC1: 'Reply on RC1', Edward Salakpi, 08 Apr 2022
  • RC2: 'Comment on nhess-2021-290', Anonymous Referee #2, 26 Feb 2022
    • AC2: 'Reply on RC2', Edward Salakpi, 08 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (27 Apr 2022) by Maria-Carmen Llasat
AR by Edward Salakpi on behalf of the Authors (06 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (19 Jun 2022) by Maria-Carmen Llasat
RR by Anonymous Referee #2 (02 Jul 2022)
RR by Anonymous Referee #3 (03 Aug 2022)
ED: Publish subject to technical corrections (03 Aug 2022) by Maria-Carmen Llasat
AR by Edward Salakpi on behalf of the Authors (06 Aug 2022)  Author's response    Manuscript
Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and agro-ecological zones.
Final-revised paper