## 2 Materials and Methods
### Analyses  
The adopted procedure is schematised in Figure 6. The DEMs and DTM, on a regular 1 \(\times\) 1 m grid and 2 \(\times\) 2 m grid, respectively, were mapped and analysed in a GIS tool (using ArcGIS 10.6 and its Spatial Analyst and 3D Analyst modules). Differences between elevation models were computed to quantify sediment budgets and changes in shoreline position for different periods. The data series allowed for assessing changes that occurred during the following periods: an approximate decade, between April 2009 and February 2019; two approximate 5-year periods, between April 2009 and 2014 and between 2014 and February 2019 (we assumed that the 2014 surveys took place on the 30 th of June, but precise dates are unknown); three periods of a year, between November 2008 and November 2009, between April 2009 and May 2010 and between May 2018 and February 2019 (not a complete year); and three seasons, between November 2008 and April 2009 (winter), between April 2009 and November 2009 (summer) and between November 2009 and May 2010 (winter) (Table 1). Figure 5: Distribution of 3-hourly wave directions for three significant-wave-height classes (**left plots**) and hourly wind directions for three velocity classes (**right plots**) for the 3 seasons monitored (from top to bottom: winter, summer, winter). To assess the local patterns, as well as the effect of beach exposure to wind and wave impacts, the studied coastal stretch was divided into segments. Therefore, the contour line of 1 m above the mean sea level (MSL) of the first survey (November 2008) was drawn and generalised (simplified) using the Douglas-Peucker simplification algorithm [46] with a specified maximum offset tolerance of 30 m. Each segment of this generalized line represented a beach segment, and for each segment, its length and orientation (facing direction) were extracted. Considering the isoline 1 m above MSL as representative of the shoreline, shoreline dynamics were obtained by calculating the difference between surveys in 2D area per linear meter, using the simplified isoline to determine the coastal stretch or segment length. Given \begin{table}
\begin{tabular}{l l r r r} \hline \hline
**Dates** & **Period** & \multicolumn{2}{c}{**\(\boldsymbol{\Delta}\) Volume (m\({}^{3}\))**} & \multicolumn{1}{c}{\(\boldsymbol{\Delta}\) Shoreline (m)**} \\ \hline November 2008–April 2009 & Season: winter & 223,094 & +2.9\% & 2.7 \\ April 2009–November 2009 & Season: summer & 108,848 & +1.4\% & 0.7 \\ November 2009–May 2010 & Season: winter & \(-\)6889 & \(-\)0.1\% & 1.2 \\ May 2010–June 2014 & & \(-\)54,483 & \(-\)0.7\% & \(-\)0.4 \\ June 2014–May 2018 & & 90,981 & 1.2\% & \(-\)3.3 \\ May 2018–February 2019 & 1 year (approx.) & \(-\)89,372 & \(-\)1.1\% & 0.2 \\ November 2008–November 2009 & 1 year & 331,942 & +4.4\% & 3.3 \\ April 2009–May 2010 & 1 year & 101,959 & +1.3\% & 1.9 \\ April 2009–June 2014 & 5 years & 47,476 & +0.6\% & 1.5 \\ June 2014–February 2019 & 5 years & 1608 & 0.0\% & \(-\)3.1 \\ April 2009–February 2019 & Decade & 49,084 & +0.6\% & \(-\)1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Changes in volume of the beach–dune system and in the shoreline position (positive and negative values indicate seaward and landward movements, respectively) that were observed between subsequent surveys (upper rows), and for the yearly, 5-yearly and the decadal periods of the time series analysed. Figure 6: Scheme of the adopted procedure, where digital elevation models (DEMs) and the digital terrain model (DTM) were analysed in terms of the spatial and temporal morphodynamics, which were subsequently related to local features and wave and wind patterns. that the inside limit of the study area (and of the segments) is fixed, the change in area per linear meter corresponded to the mean change in shoreline position. Volumes, differences in volume and differences in shoreline position between surveys were computed for the beach-dune system of the entire study area and per segment.