Articles | Volume 21, issue 7
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 2021
https://doi.org/10.5194/nhess-21-2215-2021
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, 2021
https://doi.org/10.5194/nhess-21-2215-2021

Research article 23 Jul 2021

Research article | 23 Jul 2021

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

Colin Keating et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-25', Anonymous Referee #1, 17 Feb 2021
    • AC1: 'Reply on RC1', Colin Keating, 29 Mar 2021
  • RC2: 'Comment on nhess-2021-25', Anonymous Referee #2, 01 Apr 2021
    • AC2: 'Reply on RC2', Colin Keating, 14 May 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (17 May 2021) by Kai Schröter
AR by Colin Keating on behalf of the Authors (19 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (19 May 2021) by Kai Schröter
RR by Anonymous Referee #2 (20 May 2021)
RR by Anonymous Referee #1 (27 May 2021)
ED: Publish subject to minor revisions (review by editor) (01 Jun 2021) by Kai Schröter
AR by Colin Keating on behalf of the Authors (08 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (14 Jun 2021) by Kai Schröter
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Short summary
Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
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