Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

Rogelis, María Carolina; Werner, Micha

doi:https://doi.org/10.5194/hess-22-853-2018

Articles | Volume 22, issue 1

https://doi.org/10.5194/hess-22-853-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-22-853-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 22, issue 1

Research article

|

01 Feb 2018

Research article |

| 01 Feb 2018

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

María Carolina Rogelis and Micha Werner

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Final revised paper (published on 01 Feb 2018)
Preprint (discussion started on 19 Jun 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'hess-2017-221', Anonymous Referee #1, 08 Sep 2017
- RC2: 'Comments on Rogelis and Werner's manuscript', Anonymous Referee #2, 25 Sep 2017
  - AC2: 'Response to Referee No 2', María Carolina Rogelis, 23 Nov 2017
- AC1: 'Response to Referee No 1', María Carolina Rogelis, 23 Nov 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (27 Nov 2017) by Florian Pappenberger

AR by María Carolina Rogelis on behalf of the Authors (04 Dec 2017) Author's response Manuscript

ED: Publish as is (05 Dec 2017) by Florian Pappenberger

AR by María Carolina Rogelis on behalf of the Authors (12 Dec 2017)

Short summary

Numerical weather prediction (NWP) models are fundamental for flood early warning, particularly in tropical mountainous watersheds. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that the post-processing of forecasts can provide, in a tropical mountainous area. The results show the potential of NWP but also the need for more detailed evaluation of the meteorological model in the study area.