Dominant process zones in a mixed fluvial–tidal  delta are morphologically distinct

Perignon, Mariela; Adams, Jordan; Overeem, Irina; Passalacqua, Paola

doi:https://doi.org/10.5194/esurf-8-809-2020

Articles | Volume 8, issue 3

https://doi.org/10.5194/esurf-8-809-2020

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

https://doi.org/10.5194/esurf-8-809-2020

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

Articles | Volume 8, issue 3

Research article

| Highlight paper

|

25 Sep 2020

Research article | Highlight paper |

| 25 Sep 2020

Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct

Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua

Download

Final revised paper (published on 25 Sep 2020)
Preprint (discussion started on 22 Apr 2020)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Dominant process zones in a mixed fluvial-tidal delta: Referee Commentary', Anonymous Referee #1, 23 Apr 2020
RC2: 'Review of esurf-2020-28', Anonymous Referee #2, 27 May 2020
AC1: 'Response to reviewers', Paola Passalacqua, 01 Jul 2020

Peer-review completion

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

AR by Paola Passalacqua on behalf of the Authors (31 Jul 2020) Author's response Manuscript

ED: Publish as is (01 Aug 2020) by Daniel Parsons

ED: Publish as is (04 Aug 2020) by Tom Coulthard (Editor)

AR by Paola Passalacqua on behalf of the Authors (05 Aug 2020) Author's response Manuscript

Short summary

We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.