the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jan 2026
Sandy beaches' chaos: shoreline-sandbar coupling inferred from observational time series
Abstract. Sandy shoreline–sandbar systems exhibit complex variability arising from the interplay between hydrodynamic forcing and intrinsic morphological feedbacks. Using long-term satellite-derived shoreline and sandbar observations, we applied global polynomial modeling to reconstruct low-dimensional deterministic dynamics for four contrasting coastal sites. The resulting autonomous models reproduce key morphodynamic features, including self-sustained shoreline oscillations, shoreline–sandbar coupling, and intermittent transitions between quasi-stable configurations. Nonlinear stability analyses reveal that these systems behave as chaotic oscillators, characterized by locally divergent yet globally bounded trajectories. Energetic episodes correspond to rapid shoreline–sandbar exchanges, whereas long low-energy states reflect stable attractor confinement. Together, these results demonstrate that sandy coasts are governed by deterministic but chaotic dynamics, in which internal coupling and self-organization control both variability and finite predictability. The proposed framework offers a physically consistent and data-driven approach to characterize and compare coastal morphodynamics within a unified nonlinear dynamical perspective.
- Preprint
(6904 KB) - Metadata XML
-
Supplement
(1070 KB) - BibTeX
- EndNote
Status: open (until 19 Mar 2026)
- RC1: 'Comment on egusphere-2026-154', Anonymous Referee #1, 03 Feb 2026 reply
Data sets
Satellite-derived shoreline and sandbar positions (six sites) Salomé Frugier and Marcan Graffin https://doi.org/10.5281/zenodo.18220531
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 116 | 97 | 14 | 227 | 34 | 12 | 10 |
- HTML: 116
- PDF: 97
- XML: 14
- Total: 227
- Supplement: 34
- BibTeX: 12
- EndNote: 10
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This manuscript presents a data-driven dynamical systems model of sandbar migration. The manuscript covers a lot of ground and substantial work was done behind the scenes. The figures are of very good quality, and the text (while a bit over-reliant on acronyms) is generally sound. I think a version of this manuscript could appear in NPG but the revisions I feel are necessary are conceptual, and so will take a bit of thought. They are eminently doable, and I look forward to reading a revised version. There are several issues to address
1. The conclusion that the model has been validated is based on spectra, when phase space plots rather clearly show a mismatch. At the very least language regarding strength of conclusions needs to be toned down considerably.
2. There is a lot of dynamical systems verbiage throughout (granted a lot of is standard). I think this should be balanced out by a discussion of the physical system. One example is “dissipation”. What are we talking about here turbulent dissipation in the overlying fluid? Grain on grain friction that leads to dissipation? I am guessing “neither”, and I think this needs to be clarified.
3. The fundamental structure of the model is as an unforced system. Much is made of the theorems of dynamical systems theory for autonomous systems. But the the physical sandbar system has forcing and explicitly non-autonomous terms (e.g. a strong storm passing through is likely to shift the sandbar a lot). This requires clear discussion.
4. The sandbars that are modelled are not measured but are inferred. I am a theorist/numericist but I want to note that the comment of a satellite data expert would be very helpful on the uncertainty in the inference.
Perhaps a suitable way to conclude is to ask: If I have a site for which climate change leads to an increase in storm incidence and hence sandbar movement, how can the model account for this? By refitting? Is there a way to assert standard causality or for intuition based on science? Indeed what is the role for physics in this modelling exercise?
Detailed comments:
1. The Introduction overplays the role of strongly chaotic models; most fluid physicists do not rely on things like the Lorenz model to understand geophysical fluid mechanics.
2. In the Introduction the only “field” examples quote are self-citation. Can the work of others be included, or is this so novel only the authors have done it? If it is the latter, say so explicitly.
3. The term validation is a bit of a gimmick, since no actual validation in an engineering sense is done. The model does as well as it can (and this would be considered “not very good” in most engineering applications). The real questions are I) What is the accuracy limit of these methods (e.g. would a higher order polynomial fit do better?), II) what would make a better model? In physics based models this is usually thought of as either a data problem, or a parametrization problem. I am not sure what it means here.