Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Coastal morphological changes can be assessed using shoreline position observations from space. However, satellite-derived waterline (SDW) and shoreline (SDS; SDW corrected for hydrodynamic contributions and outliers) detection methods are subject to several sources of uncertainty and inaccuracy. We extracted high-spatiotemporal-resolution (~50 m-monthly) time series of mean high water shoreline position along the Columbia River Littoral Cell (CRLC), located on the US Pacific Northwest coast, from Landsat missions (1984–2021). We examined the accuracy of the SDS time series along the mesotidal, mildly sloping, high-energy wave climate and dissipative beaches of the CRLC by validating them against 20 years of quarterly in situ beach elevation profiles. We found that the accuracy of the SDS time series heavily depends on the capability to identify and remove outliers and correct the biases stemming from tides and wave runup. However, we show that only correcting the SDW data for outliers is sufficient to accurately measure shoreline change trends along the CRLC. Ultimately, the SDS change trends show strong agreement with in situ data, facilitating the spatiotemporal analysis of coastal change and highlighting an overall accretion signal along the CRLC during the past four decades.

Understanding waterline variability at seasonal to interannual timescales is crucial for predicting coastal responses to climate forcing. However, relationships between large-scale climate variability and coastal morphodynamics remain underexplored beyond intensively monitored sites. This study leverages a newly developed 25-year (1997–2022) satellite-derived waterline dataset along the North American West Coast. Our results reveal distinct latitudinal patterns in seasonal waterline change, with excursions exceeding 25 m in the Pacific Northwest, decreasing to less than 10 m in Southern California and farther south. Waterline fluctuations strongly follow wave power in the Pacific Northwest (R = −0.78), northern California (R = −0.75), and Baja California (R = −0.62), while Baja California Sur aligns more with sea-level variations (R = −0.42). Interannually, waterline change exhibits latitudinal dependence: south of southern California, variability is low, with major erosion confined to strong El Niño-Southern Oscillation (ENSO) events, while northern regions show mixed responses. ENSO-driven storm track shifts modulate winter wave climate, resulting in enhanced (attenuated) erosion from southern California to Baja California Sur during El Niño (La Niña). However, further north, ENSO impacts are less consistent, reflecting a complex interplay of storm track displacement and intensification. These findings highlight the spatial complexity of ENSO-driven morphodynamics and provide a framework for assessing climate-induced coastal vulnerability.

Sandy beaches act as buffers against various coastal hazards but are vulnerable to episodic (seasonal) and chronic (interannual) erosion. Understanding the variation of shoreline position, a key metric in coastal morphology, over a wide spectrum of time scales is therefore crucial in assessing hazard vulnerability. Long-standing research has investigated the role of El Niño-Southern Oscillation (ENSO), the dominant mode of climate variability in the Pacific Basin, in seasonal shoreline position variability. Yet, ENSO's chronic influence–and that of another significant Pacific climate mode, the Pacific Decadal Oscillation (PDO)–on shoreline anomalies remains poorly understood. Here, we examine the variability of sandy beaches in the U.S. Pacific Northwest, a ∼750 km long diverse coastal region on the U.S. West Coast. We leverage 40 years (1984-2024) of shoreline position data from publicly available Earth-observing (Landsat) satellite imagery at a high spatial resolution (>10,000 shore-normal transects at 50-m alongshore spacing) and employ Convergent Cross Mapping (CCM), a novel methodology for inferring causality in dynamical systems. We discover that strong El Niño years are signified by erosion (75.1% of transects), and strong La Niña years largely exhibit accretional behavior (73.4% of transects). Furthermore, we establish, for the first time, that both ENSO and PDO exert a statistically significant driving control on interannual shoreline position variability, particularly on the alongshore component (in 95% and 100% of littoral cells, respectively), with water level fluctuations also playing a critical role. This effort advances our understanding of the complex seasonal-to-interannual interactions between Pacific Basin climate variability and the PNW's coastal morphodynamics, with implications for sediment management and coastal adaptation.

Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Yet, the key environmental factors that drive regional coastline changes remain poorly quantified. Here, using global satellite derived shoreline positions from 2000 to 2017 and a variety of reanalysis products, we demonstrate that coastlines are under the influence of 3 main drivers: the sea-level, and also ocean waves and fluvial inputs. The relative contribution of each of the drivers vary across the global coastline, with about a third exhibiting a clear dominance of one of the drivers (60% for sea level, 30% for rivers and 10% for waves). Furthermore, by establishing that these environmental forcing are all substantially constrained by El Niño Southern Oscillation (ENSO) at interannual time scales, we derive a conceptual global model of yearly shoreline changes that integrates the complex and diverse planetary climate influence of ENSO on each of these 3 drivers. This model reproduces well the observed shoreline changes with a global correlation of 0.4 and up to 0.6 in the tropical belt. We believe it represents a new solid physical and mathematical framework for understanding ENSO-driven littoral hazards as well as an efficient yet simple approach for their prediction.

The position of the coastal waterline—the interface between land and sea—varies over time due to sedimentary and hydrodynamic processes influenced by climate variability. While previous studies have mainly focused on local morphodynamic or storm‐driven shoreline changes, the large‐scale climatic controls on global waterline variability remain poorly understood. This study addresses this gap by analyzing how climate‐driven environmental factors govern coastal waterline dynamics across seasonal to interannual timescales. Using a globally consistent, satellite‐derived data set of coastal waterlines from 1999 to 2019, combined with reanalysis‐based estimates of sea‐level anomalies, wave setup and freshwater runoff, we use an empirical mode decomposition framework to disentangle the contributions of these drivers. This approach isolates the dominant temporal components of waterline variability and identifies regional relationships with environmental forcing. Our results reveal two main regimes of climate‐driven waterline variability: (a) a seasonal pattern that intensifies with latitude and is primarily linked to storm‐driven wave energy flux and sea‐level oscillations; and (b) an interannual pattern that peaks in the tropics and is strongly modulated by large‐scale climate oscillations, such as ENSO, and by variability in fluvial discharge. We demonstrate that regional sea‐level and freshwater anomalies can alter the waterline by amounts comparable to those caused by waves. This challenges the conventional view that wave‐driven morphodynamics dominate waterline change. By framing the waterline as a climate‐sensitive indicator of coastal change, this study establishes the first global‐scale, data‐driven framework for diagnosing and predicting waterline variability. The results provide a basis for developing climate‐informed waterline forecasts and improving assessments of coastal vulnerability in the context of ongoing sea‐level rise.