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

Predicting dune mobility under changing climatic conditions remains a challenge in aeolian geomorphology, particularly in data-scarce regions. This study presents a novel application of binomial logistic regression to forecast dune activation and migration using readily available meteorological data. We combine established dune mobility indices (Tsoar and Lancaster) into a new integrated index (TsoLa) and evaluate its performance against observed dune migration rates derived from satellite imagery. The model incorporates wind speed, precipitation, and the Southern Annular Mode (SAM) as predictors, achieving robust predictive accuracy (AUC > 0.75) for two distinct coastal dunefields in NE Patagonia, Argentina. Our results demonstrate that even with standard climatic inputs, logistic regression can effectively identify periods of dune activity, offering a low-cost tool for coastal management. The approach may be transferable to other aeolian systems, providing a framework for assessing dune dynamics under current and future climate scenarios.