Articles | Volume 14, issue 5
https://doi.org/10.5194/esurf-14-801-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-14-801-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2026
Research article |
| 07 Oct 2026
| 07 Oct 2026
Forecasting coastal dune mobility: a logistic regression model driven by meteorological data and climate indices
Instituto de Investigación en Paleobiología y Geología, UNRN – CONICET, General Roca, 8332, Argentina
Silvio Casadío
Universidad Andres Bello, Facultad de Ingeniería, Geología, Autopista Talcahuano, 7100 Concepción, Chile
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Short summary
This study shows dune mobility can be predicted using a simple statistical method based only on meteorological data from nearby weather stations and freely available climate indices. The model provides an accessible, low-cost way to anticipate future dune behavior. This information is highly valuable for local communities and decision-makers, as it supports better land-use planning and helps reduce potential damage caused by dune migration, contributing to the management of coastal environments.
This study shows dune mobility can be predicted using a simple statistical method based only on...
