The challenge the researchers set out to solve is a familiar one in aeolian geomorphology. Traditional approaches, such as drift potential calculations and mobility indices, do a good job of describing wind-driven sediment transport at a given moment, but they offer limited ability to forecast whether dunes will migrate or become fixed over time. That gap matters increasingly in a changing climate, where shifts in wind regimes, precipitation patterns, and storm frequency may alter dune behavior with real consequences for ecosystems, roads, and homes. Complex numerical simulations could in principle fill the gap, but they demand data and expertise that many coastal regions simply do not have.

Toffani and Casadío turned instead to binomial logistic regression, a statistical technique that predicts the probability of a binary outcome, coded as zero or one, from a set of continuous predictor variables. First developed in the early 1980s, the method has been applied successfully to landslide susceptibility, soil mapping, and vegetation dynamics, yet its use for coastal dune mobility remained notably limited. The elegance of the approach lies in its ability to fold multiple climatic variables, including wind speed, precipitation, and large-scale atmospheric indices, into a single probabilistic framework that outputs a forecast probability of dune activation rather than a static description.

The natural laboratory for the study was the northern coast of the San Matías Gulf, stretching roughly 180 kilometers from the mouth of the Negro River to San Antonio Oeste. The region is a cold semi-arid environment where strong westerly winds blow hardest during the austral summer, exacerbating the aridity created by the Andean rain shadow. Its dunefields are remarkably varied: the Bahía Creek–Caleta de los Loros system extends over 36 kilometers with active dunes up to 16 meters high migrating east-northeast at 6 to 10 meters per year, while dunes near El Cóndor advance at around 7 meters per year. The area is also home to thousands of residents and a booming tourist trade, with Las Grutas alone averaging 118,000 visitors annually between 2006 and 2023, making dune encroachment a genuine practical concern.

To build their model, the researchers drew on hourly meteorological records from two stations, Viedma Aero and San Antonio Oeste Aero, covering the standard 1991 to 2020 reference period. Wind speeds and directions were normalized to a standard height of 10 meters, and winds exceeding 6.17 meters per second were flagged as capable of transporting sand, a threshold calculated from local grain sizes using Bagnold’s classic equations for the onset of saltation. From these data the team computed drift potential values and combined two established mobility measures, the Tsoar and Lancaster indices, into a new integrated index they dubbed TsoLa. Dunes were coded as active only when both indices simultaneously indicated mobility and the resultant drift direction aligned with the coast’s general eastward migration pattern.

The statistical screening was rigorous. Temperature, potential evapotranspiration, and soil moisture were excluded because of multicollinearity, while wave height, cattle populations, and the Southern Oscillation Index were dropped because they added little predictive power. What survived was a lean trio of predictors: monthly average wind speed, total monthly precipitation, and the Southern Annular Mode, or SAM, the dominant mode of extratropical climate variability in the Southern Hemisphere. When SAM is positive, the westerly wind belt contracts poleward and mid-latitude conditions turn warmer and drier; when it is negative, storm tracks shift north and the study region around 40 degrees south experiences stronger, stormier weather.

The results were strikingly consistent across both study sites. Wind speed emerged as a positive predictor of dune migration, with coefficients of 0.64 at San Antonio Oeste and 0.32 at Viedma, while both precipitation and SAM carried negative coefficients, meaning higher rainfall or a positive SAM phase reduces the probability of migration. In practical terms, the model suggests dune movement becomes unlikely when monthly precipitation exceeds 150 millimeters, when wind speeds fall below 20 kilometers per hour, or when SAM values are high. The models achieved area under the ROC curve values of 0.77 and 0.78, comfortably above the 0.5 threshold of random guessing, and correctly classified between 73 and 77 percent of observations in training and test datasets. A bootstrap analysis of 1,000 resamples confirmed that all three predictors had stable, statistically significant effects.

Validation against the real world came from nearly four decades of satellite and aerial imagery. The team digitized dune fronts in images spanning 1985 to 2023, placing reference points every 50 meters along each dune crest and measuring perpendicular distances between successive front positions. In the Viedma area, average migration rates ranged from 2.92 to 10.12 meters per year and correlated strongly with wind intensity values, with a correlation coefficient of 0.84. In San Antonio, migration was slower, between 2.51 and 6.01 meters per year, and showed little correlation with wind, likely because limited sand supply and encroaching vegetation, rather than wind energy alone, control movement there. Vegetation cover in the San Antonio area has in fact surged from 16 percent in 1961 to 60 percent in 2021, a greening trend the authors link to weakening winds and a trend toward more positive SAM values.

That contrast between the two dunefields carries an important lesson: geology matters. Viedma’s proximity to the Negro River mouth provides abundant sand that feeds large transverse dunes whose migration tracks wind capability closely, while San Antonio’s narrower beaches deliver a more limited sediment budget. The authors argue that integrating such geomorphological context into predictive models is essential, and they suggest that future refinements could incorporate higher-resolution vegetation indices, buoy-based wave data, better livestock records, and estimates of monthly sediment supply. They also note that lagged effects of El Niño events, which influence regional precipitation by up to 50 millimeters, deserve further study with properly calibrated time lags.

What makes the study resonate beyond Patagonia is its accessibility. Every input, from weather station records to the SAM index published by the British Antarctic Survey, is freely available, and the R scripts and datasets are posted in a public Zenodo repository. Because the framework relies on standard meteorological observations, it can be adapted to other coastal and inland aeolian systems with minimal modification, offering a scalable pathway for integrating climate forecasting with geomorphic hazard assessment. For coastal communities watching dunes advance toward their roads and homes, the message is empowering: the data needed to anticipate the next phase of dune activity may already be sitting in the nearest weather station’s archive.

Subject of Research: Statistical forecasting of coastal dune mobility using meteorological data and climate indices in northeastern Patagonia

Article Title: Forecasting coastal dune mobility: a logistic regression model driven by meteorological data and climate indices

Article References: Toffani, M., & Casadío, S. (2026). Forecasting coastal dune mobility: a logistic regression model driven by meteorological data and climate indices. Earth Surface Dynamics, 14(5), 801-820. https://doi.org/10.5194/esurf-14-801-2026

Image Credits: AI Generated

DOI: 10.5194/esurf-14-801-2026

Keywords: coastal dunes, dune mobility, logistic regression, aeolian geomorphology, Patagonia, Southern Annular Mode, wind erosion, climate indices, remote sensing, coastal management, sediment transport, Earth Surface Dynamics