A joint probabilistic index for objective drought  identification: the case study of Haiti

Monteleone, Beatrice; Bonaccorso, Brunella; Martina, Mario

doi:https://doi.org/10.5194/nhess-20-471-2020

Articles | Volume 20, issue 2

https://doi.org/10.5194/nhess-20-471-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Recent advances in drought and water scarcity monitoring,...

https://doi.org/10.5194/nhess-20-471-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 2

Research article

|

19 Feb 2020

Research article |

| 19 Feb 2020

A joint probabilistic index for objective drought identification: the case study of Haiti

Beatrice Monteleone, Brunella Bonaccorso, and Mario Martina

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Final revised paper (published on 19 Feb 2020)
Preprint (discussion started on 10 Sep 2019)

Interactive discussion

Status: closed

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

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- Supplement

SC1: 'Interactive comment on “A joint probabilistic index for objective drought identification: the case study of Haiti” by Monteleone et al. (nhess-2019-296)', Stavros Yannopoulos, 03 Dec 2019
- AC2: 'Answer to Stavros Yannopoulos', Beatrice Monteleone, 16 Jan 2020
RC1: 'Evaluation and Remarks to the authors', Anonymous Referee #1, 03 Dec 2019
- AC3: 'Answer to Referee #1', Beatrice Monteleone, 16 Jan 2020
RC2: '1st Review of nhess-2019-216', Anonymous Referee #2, 26 Dec 2019
- AC1: 'Answer to Referee #2', Beatrice Monteleone, 16 Jan 2020

Peer-review completion

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

ED: Publish as is (17 Jan 2020) by Athanasios Loukas

AR by Beatrice Monteleone on behalf of the Authors (24 Jan 2020) Author's response Manuscript

Short summary

This study proposes a new drought index that combines meteorological and agricultural drought aspects. The index is scalable, transferable all over the globe, can be updated in near real time and is a remote-sensing product, since only satellite-based datasets were employed. A set of rules to objectively identify drought events is also implemented. We found that the set of rules, applied together with the new index, outperformed conventional drought indices in identifying droughts in Haiti.