Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation

Abstract

In the Paci c Basin, El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability and drives substantial changes in oceanographic forcing, likely having a signi cant impact on Paci c coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 35 years of Landsat imagery to map shoreline variability around the Paci c Rim (72,000 beach transects) and identify coherent patterns of beach erosion and accretion controlled by ENSO. We nd that approximately one third of all beaches experience signi cant erosion during El Niño phases, with the Eastern Paci c particularly vulnerable to widespread erosion (most notably during the large 1997/1998 event). In contrast, La Niña events coincide with signi cant accretion for approximately one quarter of all beaches, although conversely drives substantial erosion in south-east Australia and other localized regions. The signi cant regional variability in coastal response to ENSO should be considered in light of future projected intensi cation and shifts in ENSO amplitudes and avors. Main TextSandy coasts comprise 31% of coastal environments worldwide 1 , of which the majority are classi ed wave-dominated 2 . These coasts are particularly vulnerable to uctuations in ocean wave energy and water levels, that drive cycles of erosion and accretion at episodic, interannual and decadal timescales, impacting adjacent infrastructure and beach habitats. The interannual timescale is of particular interest as it is closely linked to the Earth's climate and its internal modes of climate variability. In a changing climate, a likely intensi cation of these important climate patterns 3,4 , coupled with projected changes in storminess 5,6 and rising sea levels, will likely exacerbate coastal erosion 7 and threaten the future resilience of many coastal communities worldwide 8,9 .

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

“…Lastly, the third and final metric we evaluate here is called “within C.I.” in Figure 10c, which represents the percentage of the time, during the “Hindcast (Validation)” period (2015–2020), that the model predicted shoreline position falls within the 95% confidence levels of the satellite‐derived shoreline observations, which are assumed to be identical to $$\pm 2{\varepsilon }_{\text{sat}}$$, where $${\varepsilon }_{\text{sat}}$$ is the 14 m RMSE derived at Ocean Beach (where dense GPS observations are available) and applied uniformly across the California coast. Although the uniform prescription of satellite‐error statistics is not ideal, we note that the general 10–15 RMS accuracy of satellite‐derived shorelines has been well established through extensive testing at many well‐monitored sites (e.g., Castelle et al., 2021; Hagenaars et al., 2017; Luijendijk et al., 2018; Pardo‐Pascual et al., 2018; Vos, Harley, et al., 2019; Vos et al., 2023). Figure 10c indicates that the model predictions are within the confidence intervals of the satellite observations approximately 95% of the time (on average) across California.…”

“…Since the 1980's, Earth‐observing satellites (e.g., the Landsat missions) have collected a massive archive of coastal imagery data that have only recently been leveraged for science and engineering applications (Turner et al., 2021). Recent advances in satellite remote‐sensing analysis provide a window into the recent past and current state of the world's beaches (Luijendijk et al., 2018) and their large‐scale vulnerability to climatic forces like El Niño (Vos et al., 2023). By leveraging the large streams of data offered from satellites, reduced‐complexity coastal‐change models seem poised for success in a challenging field of study owing to the newly found “treasure trove” of data (Barnard & Vitousek, 2023; Hunt et al., 2023; Vitousek et al., 2023a).…”

“…However, reliable forecasts of coastal erosion on decadal to centennial timescales over large geographic regions (e.g., state and country scale) are increasingly sought, even in “data‐poor” environments. Recent progress has been made to improve the temporal frequency of coastal observations over large spatiotemporal scales (e.g., 1 m ‐ 100 km and days ‐ decades) using satellites (Hagenaars et al., 2017; Luijendijk et al., 2018; Nelson & Miselis, 2019; Pardo‐Pascual et al., 2012; Vos, Harley, et al., 2019; Vos, Splinter, et al., 2019; Vos et al., 2023). Since the 1980's, Earth‐observing satellites (e.g., the Landsat missions) have collected a massive archive of coastal imagery data that have only recently been leveraged for science and engineering applications (Turner et al., 2021).…”

“…Yet, integration of satellite‐data with large‐scale, dynamic coastal‐change models has remained underdeveloped, until now. Most applications of satellite‐derived shorelines investigate shoreline trends (e.g., Calkoen et al., 2021; Castelle et al., 2022; Luijendijk et al., 2018) or interannual variability (Vos et al., 2023), rather than synoptic shoreline variability with wave and storm events. Recently, Alvarez‐Cuesta et al.…”

A Large Sediment Accretion Wave Along a Northern California Littoral Cell

“…The rocky and inaccessible nature of this coast and the multi‐decade evolution of the accretion wave made remote sensing the most viable option for measuring the time‐dependent evolution of the region's shoreline. Thus, we used satellite‐based shoreline measurement techniques as a primary data source because these methods can characterize littoral‐scale phenomena over seasonal to decadal time scales (Bishop‐Taylor et al., 2021; Castelle et al., 2022; Jayson‐Quashigah et al., 2013; A. Luijendijk et al., 2018; Mentaschi et al., 2018; Sayre et al., 2019; Vos, Harley, et al., 2019; Vos, Splinter et al., 2019; Vos et al., 2023; Warrick, Vos, et al., 2022). We combine satellite‐based data with sediment transport theory and historical imagery, written and oral accounts, and maps to explore the unique evolution of this wave and provide new regional context and broader geophysical understanding that can be applied to other littoral systems.…”