Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery

International audienceUnderstanding and predicting shoreline change along sandy coasts requires continuous (in both time and space) long-term (decades) shoreline data at good spatial (e.g. 100 s of metres) and temporal (e.g. months) resolution. Publicly available satellite imagery can now provide such time series. However, satellite-derived shorelines (SDS) are associated with uncertainties, particularly at high-energy meso-macrotidal coasts, which challenge the assessment of long-term trends and interannual variability. In this paper we address the 1984–2020 time- and space-evolution of 269 km of high-energy meso-macrotidal sandy coast in southwest France using uncertain (no tide and runup correction) SDS data. The shoreline trends are validated with field data collected over the period 2008–2019. Over 1984–2020, the shoreline eroded by 0.55 m/yr with maximum erosion (accretion) reaching 15.61 m/yr (6.94 m/yr), with the largest changes observed along coasts adjacent to the inlet and estuary mouths. We show that, away from the presence of ebb-tide deltas and swash bars affecting offshore wave transformation and nearshore circulation, the long-term shoreline trend is well explained by the gradients in longshore drift computed from a regional wave hindcast and an empirical longshore transport formula. By averaging the yearly SDS along the entire coastline, we find that interannual shoreline variability is well correlated with the winter West Europe Pressure Anomaly (WEPA), which outscores the other conventional teleconnection pattern indices. WEPA even explains >80 % of the space-averaged shoreline variability over the recent period 2014–2020 when more and higher quality satellite images are available. A more local assessment of the links between climate indices and shoreline response shows that correlation with all climate indices dramatically drops downdrift of the large-scale estuary mouths and inlets. This suggests that along this 10–20 km stretch of downdrift coast, shoreline response is controlled factors internal to the estuary mouth/inlet system. The rest of the coast is mostly controlled by factors external to the system, which are primarily the variability in winter-mean wave height correlated to winter WEPA index. Overall, we demonstrate that an adapted space-averaging of uncorrected (noisy) SDS dataset can allow addressing the time- and space variability of shoreline change and their primary drivers including large-scale climate patterns of atmospheric variability. We also advocate that such SDS analysis can be performed along any coastline in the world in order to guide future model development and application