Satellite-Based Assessment of Coastal Morphology Changes in Pichilemu Bay, Chile

Abstract

Coastal erosion is a global issue exacerbated by extreme events, ENSO variability, storms, and anthropogenic pressures. In Chile, over 80% of beaches are affected by erosion, impacting more than one million people. This study analyzes the evolution of Pichilemu Bay between 1985 and 2024 using satellite imagery, spatio-temporal models, and drone-based surveys. A total of 554 shorelines were extracted, revealing and average shoreline retreat of −1.17 m/year, with maximum erosion of −1.76 m/year and maximum accretion of +0.9 m/year. Wave climate analysis (mean Hs 2.5 m, mean Tp 12.5 s) identified 10 major storm events exceeding 3 m, while sediment sampling showed significant negative correlations between grain size and erosion rates (r = −0.64, p < 0.05). The morphology before and after the 2010 earth-quake was assessed, evidencing up to 100 m of shoreline retreat in affected sectors. Remote sensing techniques proved highly effective for monitoring coastal dynamics, providing high-resolution insights that inform spatial planning, enhance regional erosion monitoring programs, and support adaptive management strategies in the face of climatic and tectonic challenges.

1. Introduction

Globally, approximately 60% of beaches are experiencing erosion due to rising sea levels, increased storm activity, and human pressures. Since 1995, the frequency of hurricanes has risen, driven by global warming and the Atlantic Multidecadal Oscillation (AMO) [1], intensifying coastal damage. In the Pacific, erosion is closely linked to the El Niño–Southern Oscillation (ENSO), which alters wave energy and direction. Projections suggest that a sea-level rise between 0.9 and 1.8 m could lead to the disappearance of many beaches within the next 70 years [2].

Chile faces a critical coastal erosion issue, with over one million people exposed to hazards such as tsunamis and earthquakes. Approximately 80% of the Chilean coastline is undergoing erosion, and in some areas of Central Chile, erosion rates have doubled in recent years [3]. The local importance is relevant for analyzing where on the beach and at what time of year these changes are occurring. However, these erosion processes are not unique to this region [4], which indicates that approximately 80% to 90% of sandy beaches along the Atlantic and Gulf coasts of the United States are experiencing erosion processes, with average rates of 0.6 m/year. This reflects a widespread trend of coastal retreat, attributed to factors such as sea level rise, reduced sediment supply, human activity, and extreme events such as storms. Traditional methods have analyzed shoreline changes using aerial photography and topographic surveys, but these methods have limitations in modeling the impact of climate change [5].

The MONCOSTA project has been key in coastal monitoring in Chile, particularly in Valparaíso, O’Higgins, and El Maule. Using Landsat and Sentinel satellite images, along with extraction algorithms like SHOREX and CoastSat, it has reconstructed shoreline evolution. This approach combines sedimentological data, drones, and citizen participation through the CoastSnap platform, allowing for continuous and precise monitoring.

Pichilemu, in the O’Higgins Region, is highly vulnerable to erosion. With a population of 16,394, over 60% live less than 30 m above sea level, exposing them to storms and tsunamis. Artisanal fishing, aquaculture, and tourism, key economic activities, are at risk. Currently, the erosion rate in Pichilemu reaches −1.3 m per year, and the disappearance of embryonic dunes is affecting beach stability [3]. Pichilemu Bay is distinguished by a unique combination of natural conditions, rendering it a particularly pertinent location for the study of coastal erosion. The logarithmic spiral configuration of its coastline facilitates wave refraction and promotes morphodynamic equilibrium states, allowing for the observation of how these patterns respond to variations in wave energy and sediment transport. Furthermore, the bay is influenced by interannual climate variability, specifically ENSO, which modulates coastal dynamics, as well as by recurrent storms that intensify beach retreat in exposed areas. This area is located in the impact zone of the earthquake followed by a tsunami on 27 February 2010 and its subsequent aftershocks. These seismic events caused tectonic subsidence and contributed to coastal retreat in specific areas. However, the impact of these seismic events must be considered alongside other interactive factors operating on multiple time scales. The heterogeneity of geomorphological environments and dynamic processes supports the formulation of adaptive management strategies in coastal environments susceptible to erosion.

Factors such as the tectonic convergence of the Nazca and South American plates and a reduction of up to 90% in water flow have intensified erosion. The 2010 earthquake caused a 0.5 m subsidence, altering the beach’s morphology, while increasing storm intensity has accelerated coastal erosion [6]. If this trend continues, the loss of beaches will severely impact on the local economy, especially tourism and artisanal fishing.

The aim of this study is to investigate how the shoreline in Pichilemu Bay has changed in terms of location over time. To do this, we will combine satellite data, wave climate information, sediment sampling and geospatial modelling. The research will focus on three sectors with different morpho dynamics to assess the evolution of the coastline, quantify erosion and accretion rates, and determine the impact of disruptive events, such as the 2010 earthquake and tsunami, on the coastal configuration.

2. Study Area

The study area is in the commune of Pichilemu in the O’Higgins region between the coordinates (34°23′02″ S/72°00′49″ W and 34°19′21″ S/71°58′38″ W), with a beach arc of ~8.7 km (Figure 1). The study spans from the coastal edge, which starts from the coastline to the dune zone.

The geology of the Pichilemu region is characterized by the presence of the late Paleozoic Pichilemu metamorphic complex, intrusive bodies and marine deposits and Neogene continental deposits [7]. In addition, the study area has a normal fault that is associated with the earthquake that occurred in 2010, which caused a subsidence of 0.5 m.

3. Materials and Methods

The methodology for the temporal analysis of coastal dynamics at Pichilemu Beach was structured into four main phases:

3.1. Massive Extraction of Shorelines

The CoastSat algorithm [8] is an open-source tool for the extraction of shorelines from satellite imagery, allowing temporal analyses of more than 30 years with a horizontal accuracy of approximately 10 m on sandy shorelines. It uses Landsat 5, 7, 8 and 9 images, in addition to Sentinel-2, obtained from public databases such as EarthExplorer of the U.S. Geological Survey and Copernicus Services Data Hub of the European Space Agency. Mass extraction of shorelines is performed in Google Earth Engine (GEE), employing bands of the visible spectrum and the MNDWI index, along with edge detection algorithms and neural classifiers. Preprocessing includes cloud masking and resolution enhancement by data fusion and bilinear interpolation. Shoreline detection is based on image classification with neural networks, separating pixels into sand, water, white water and other land features, which avoids errors caused by wave foam. Subsequently, the Modified Normalized Difference Water Index (MNDWI) and an Otsu thresholding algorithm are applied to segment the sand–water interface with sub-pixel accuracy. Finally, the extracted shorelines are exported in geojson format for use in Geographic Information Systems (GIS), enabling long-term coastal erosion monitoring and management.

3.2. Wave Climate

Numerical simulations from ERA5, a climate reanalysis from the European Centre for Meteorological Forecasting (ECMWF) covering atmospheric, land and oceanic variables with hourly updates and detailed coverage over the Pacific basin [9], were used to determine the wave parameters. Monthly average significant wave height (Hsw), period and direction were calculated from 1984 to present due to the scarcity and limited quality of in situ wave data in Chile. Measurements from the Hydrographic and Oceanographic Service of the Navy (SHOA) present incomplete records and missing data, affecting the reliability of the analyses, while other data from the Chilean Navy are not publicly available [10]. These limitations motivated the use of ERA5, which offers advantages such as broad temporal and spatial coverage, integration of multiple observation sources, and proven validation. For this research, only extreme wave events whose significant height exceeded 1.5 times the annual mean (2.3 m), with a minimum duration of 12 h, were analyzed using ERA5 data with a frequency of 6 h. In total, 10 swell events were identified between 1 January 1984, and 29 February 2020, excluding phenomena such as meteotsunamis, meteorological tide, astronomical tide and long waves.

The 3 m threshold was adopted based on previous coastal storm studies [11] for Australian beaches, which have wave conditions, exposure, and environmental characteristics similar to central Chile. This threshold effectively captures the upper tail of the wave energy distribution in Pichilemu, corresponding to observed storm conditions along the central Chilean coast. The Peaks Over Threshold (POT) method was used to classify events exceeding a site-specific critical threshold old as coastal storms, also requiring a minimum storm duration and a meteorological in independence criterion to ensure independent events are considered. The most commonly used distribution for modeling these surpluses is the Generalized Pareto Distribution (GPD):

$$P\left(Y\le y\right)=1-{\left(1+{\displaystyle \frac{\epsilon \gamma }{\sigma }}\right)}^{-{\displaystyle \frac{1}{\epsilon }}}, for y>0, yes \epsilon \ne 0$$

Y = X − µ is the excess above the threshold µ; ε = shape parameter; σ = scale parameter;

µ is the threshold defined for considering an event extreme.

3.3. Erosion Rates and Analysis of the Beach Width

Erosion and accretion rates were calculated using DSAS v6.0.168 [12]. The baseline was positioned landward of the maximum observed shoreline position, with transects every 50 m along the 8.7 km arc. The Linear Regression Rate (LRR) method was used, providing both mean rates and 90% confidence intervals (CI).

To address positional uncertainty, we applied:

• Satellite georeferencing error: ±5 m (Sentinel-2), ±7.5 m (Landsat).
• Extraction error from CoastSat RMSE validation: ±8 m.

The combined shoreline position uncertainty was propagated into LRR calculations to ensure statistical robustness. The process consists of three main steps: the definition and classification of the beach width, the parameterization of the recreational function of the beaches, and the linking of these two elements to understand the influence of the width on the recreational function from cross profiles every 50 m [13].

To make the spatiotemporal model, Triangulated Irregular Network (TIN) interpolation is performed, using monthly averages of the net beach width. Then, it is converted into a raster to detect when and where modifications occur in the position of the coastline [14].

3.4. Sediment Analysis

During four field campaigns (November 2023, July, September and November 2024), shoreline positions were recorded, cross-sectional profiles were surveyed, and sediment samples were collected from three sub-environments: the high tide line, backshore, and dune mounds. Samples were dried at 90 °C for three days and then sieved using the Wilson sieve grid. Granulometric parameters, such as D10, D50, D90, sorting, skewness, and kurtosis, were analyzed using the Gradistat macro. These data characterized grain size distribution and assessed the influence of factors such as wind, waves, and currents on coastal dynamics, providing key insights into the beach’s morphodynamical processes. The four campaigns were deliberately scheduled to capture seasonal variability, as austral winter is characterized by higher precipitation and runoff, which enhances sediment input from the surrounding catchments and influences nearshore sediment supply.

4. Results

4.1. Massive Extraction of Shorelines

A total of 554 shorelines were extracted over a 39-year period, from 1985 to 2024. Figure 2 shows the total number of shorelines extracted by each satellite: Landsat 5 contributed 137 lines, Landsat 7 provided 21, Landsat 8 accounted for 168, Landsat 9 added 38, and Sentinel-2 supplied 190 shorelines.

4.2. Wave Climate

During the study period, the hydrodynamic conditions of the beaches showed significant changes. The mean annual significant wave height (Hs) is 2.5 m, with 90% of records between 2.3 and 3 m, while peak periods (Tp) average 12.5 s, associated with storms. The dominant swell direction is southwest (231°), with slight oscillations. In winter, Hs increases (~3 m), periods are shorter (13.51 s), and the direction shifts towards 243°. Analysis of the multivariate ENSO index (MEI) shows that the most intense El Niño events (1997–1998, 2015–2016 and 2022–2023) coincide with minimum MEI values and historical maxima in wave height and period, with no significant changes in mean direction (Figure 3). Notable differences were identified between high-energy years (1989, 1998, 2010, 2011, 2016) and less energetic years (1996, 1997, 2002, 2004).

4.3. Erosion Rates and Analysis of the Beach Width

The rate of change of the shoreline for the Pichilemu inlet was analyzed in three sectors: S1 (proximal zone), S2 (middle zone), and S3 (distal zone), using the Linear Regression Rate (LRR) method to identify areas of erosion and accretion. A total of 448 transects, spaced at 20 m, were generated and processed in the DSAS application. The shoreline change analysis indicates an average retreat of approximately −1.17 m/year, with a maximum with a maximum erosion rate of −1.76 m/year and a maximum accretion rate of +0.9 m/year. Spatial variability is evident: S1 and S2 exhibit the most pronounced erosion, ranging from −1.76 to −0.2 m/year, whereas S3 shows more stable or accretional behavior. Figure 4 illustrates these patterns, where erosion is represented in red and orange, accretion in green, and transitional areas in yellow. The variability of the data is summarized in

Table 1. Standard deviation means, maximum and minimum of the three beach sectors.

Sector	Standard Deviation m/Year	Middle m/Year	Max m/Year	Min m/Year
S1	−0.9	−1.3	−1.76	0.5
S2	−0.4	−0.7	−1.04	0.1
S3	0.05	0.9	1.1	−0.06

, which presents the mean, standard deviation, maximum, and minimum values for the three beach sectors.

The spatiotemporal analysis of the Pichilemu Cove shows differentiated patterns of erosion and accretion in different sectors of the beach, influenced by events such as ENSO (1997–2000 and 2015–2016), the 2010 earthquake, and persistent storms since 2015 (Figure 5). During the 1997–2000 ENSO event, net accretion was recorded in the distal zone (P1, up to +0.8 m/year) and in the proximal zone (P3, up to +0.6 m/year), although P3 exhibited variability linked to shifts in wave patterns. The middle zone (P2) showed mild erosion (around −0.4 m/year). In contrast, the 2015–2016 ENSO produced generalized erosion, most severe in P2 (−1.8 m/year), moderate in P1 (−0.9 m/year), and least in P3 (−0.6 m/year). The 2010 earthquake caused significant shoreline retreat at P1 (−3.2 m/year) and P2 (−2.7 m/year), associated with tectonic subsidence and sediment redistribution, with particularly pronounced losses in P2. P3 experienced smaller but still measurable erosion (−1.1 m/year). Since 2015, intense storms have caused sustained erosion in P2, with episodic retreat rates exceeding −2 m/year. In contrast, accretion has been recorded in P3 in recent years (up to +0.5 m/year), possibly due to sediment transport toward its more sheltered setting.

4.4. Sediment Analysis

The results reveal a multimodal (trimodal) grain-size distribution in all three profiles, meaning that the sediments exhibit multiple peaks in particle size concentration, rather than a single dominant trend (Figure 6). This indicates the presence of several transport and deposition processes acting simultaneously or at different times, resulting in a mix of fine, medium, and coarse sediments. In this case, several peaks correspond to both fine particles (10–100 μm) and coarse particles (100–1200 μm).

Fine sediments are associated with suspension transport in aeolian (wind-driven) environments, particularly in the backshore and dune base, indicating relative stability in these areas during calm conditions. In contrast, coarse sediments are linked to saltation transport under high-energy conditions, often coinciding with sectors more exposed to wave action, such as the foreshore of S1 and S2, which also register the highest erosion rates. The coarser fractions likely originate from the upper parts of the basin and are transported to the beach via fluvial input, particularly near the Petrel Lagoon in S2, reinforcing its role as a sediment source zone.

Profile-specific observations highlight this spatial variability:

• Profile 1 (distal zone—S3): Trimodal distribution with transport by suspension at the high tide line, saltation at the backshore, and suspension at the dune base. This sector shows relative stability and occasional accretion, consistent with its higher proportion of fine sediments.
• Profile 2 (middle zone—S2): Trimodal trend with medium-to-fine sands and transport dominated by saltation. Proximity to the Petrel Lagoon suggests an additional sedimentary contribution from the estuary. Despite this input, its exposure to wave energy makes it erosion-prone.
• Profile 3 (proximal zone—S1): Trimodal distribution in both sampling dates, with coarse fractions more dominant in winter, linked to higher wave energy and storm influence. This sector shows the most persistent erosion, in line with its higher proportion of coarse particles and exposure to direct wave action.

Seasonal differences between November 2023 (spring) and July 2024 (winter) are evident. In winter, higher precipitation, snowmelt, and stronger winds result in heavier samples and a greater percentage of coarse sand, reflecting more energetic transport conditions. In contrast, spring samples are lighter, with a predominance of medium-to-fine sands, indicating calmer conditions and a higher degree of stability in certain zones. These patterns confirm that sediment size and distribution are closely linked to the spatial variability of erosion and accretion observed along Pichilemu Bay.

Grain-size distributions showed predominance of fine sand in backshore zones and fine-to-medium sands in foreshore and dune bases. Seasonal variability was observed, with coarser fractions during winter storms.

Pearson correlation between mean D50 and LRR erosion rates by profile indicated:

• Foreshore samples: a significant negative correlation was found (r = −0.64, p < 0.05, 95% confidence level) indicating that finer sediments are consistently associated with higher erosion rates. This outcome highlights the role of sediment texture in modulating beach stability: finer-grained foreshore deposits are more easily mobilized by wave action, reducing resistance to storm events and accelerating shoreline retreat. Conversely, coarser sediments enhance beach armoring and energy dissipation, contributing to lower erosion rates.
• Dune-base samples: no significant correlation was observed (r = −0.21, p > 0.05, 95% confidence level). This weak relationship suggests that processes other than grain size, such as aeolian reworking, vegetation cover, and dune morphology, exert greater control over erosion at the dune toe. These additional drivers can obscure the direct influence of sediment texture, leading to high spatial variability in erosion responses along the dune system.

5. Discussions

The analysis of coastal dynamics in Pichilemu Bay reveals an average erosion rate of −1.17 m/year between 1986 and 2024, consistent with previous estimates for the area estimates for the area [3,15]. This pattern confirms that coastal erosion is not an isolated phenomenon but rather a persistent trend shaped by both climatic and tectonic drivers.

ENSO events (1997–2000; 2015–2016) coincide with substantial shoreline changes, but their effects were spatially heterogeneous. In more wave-exposed sectors, high-energy conditions associated with ENSO favored both sediment transport and episodic deposition, while in semi-sheltered areas erosion was amplified. This contrast highlights the role of local geomorphology and exposure in modulating the system’s sensitivity to large-scale climatic oscillations. Recurrent storms since 2015 further reinforced erosion in the mid-sector, demonstrating how storm clustering can accelerate sediment losses where natural buffers are weak, thereby confirming the structural vulnerability of this zone.

The 2010 earthquake and associated coseismic subsidence triggered an abrupt shoreline retreat, particularly in the southern and mid sectors, where recovery has remained limited due to enhanced accommodation space and persistent wave exposure. In contrast, the northern sector retained greater sediment-holding capacity, suggesting that local geomorphic configuration and reduced exposure confer relative resilience. These sector-specific differences reveal that tectonic disturbances interact with pre-existing morphodynamic settings to produce uneven recovery trajectories.

Similar patterns have been documented in Cartagena [16], where the combination of ENSO events and the 2010 earthquake generated spatially differentiated responses. These parallels reinforce the notion that coastal management must account for both interannual climatic variability and the occurrence of local extreme events.

Recent advances in modeling (e.g., convolutional neural networks and hybrid models) [17,18] surpass the limitations of empirical rules such as the Bruun rule by incorporating multiple variables and their nonlinear interactions. In this regard, the multidimensional frameworks proposed by [19] demonstrate how integrating morphodynamic, climatic, and anthropogenic factors enhance predictive accuracy for complex coastal systems. Likewise, the wave-energy-centric approach of [20] underscores the pivotal role of directional wave spectra and nearshore hydrodynamics in anticipating erosion hotspots. Applying such methodologies in Pichilemu would provide a more robust basis for projections under climate change and sea-level rise scenarios.

Spatial differences in erosion rates and recovery capacity call for rethinking management strategies. Persistently eroding sectors, such as the mid and southern zones, require urgent adaptive measures, while more stable areas should be monitored to anticipate potential changes if wave or sedimentation patterns shift. Incorporating the advanced modelling techniques outlined above into local monitoring programs could bridge the gap between predictive science and actionable policy.

Overall, the findings confirm that Pichilemu’s coastal dynamics result from the interplay of drivers operating at different temporal and spatial scales. Integrating high-resolution predictive models with continuous observation will be key to designing flexible, evidence-based management plans capable of responding to both sudden disturbances and long-term trends.

6. Conclusions

The results obtained confirm the capability of remote sensing systems to effectively characterize shoreline evolution in response to a range of natural and anthropogenic factors. The proposed spatiotemporal model successfully described morphological changes across the three sectors of the beach, revealing clear spatial variability in erosion and accretion patterns. Average shoreline retreat reached −1.17 m/year, with maximum erosion rates of −1.76 m/year in the proximal (S1) and middle (S2) sectors, while the distal sector (S3) exhibited more stable or accretional behavior, with maximum accretion of +0.9 m/year. Sediment analysis indicated a trimodal distribution, with grain sizes ranging from fine to coarse sands, influencing the susceptibility of each sector to erosion. Disruptive phenomena such as coastal storms, earthquakes, and climatic oscillations associated with ENSO were found to exert a significant influence on shoreline dynamics, with the 2010 earthquake alone causing up to 100 m of shoreline retreat in certain locations and a loss of 51% of beach width in the most impacted areas. These findings underscore the importance of considering both climatic variability and tectonic disturbances in coastal management strategies. In addition, dedicated software is being developed to integrate the proposed methodology, with the potential for replication across multiple beaches. A preliminary test using Pichilemu Beach as a case study has demonstrated its operational feasibility. Once completed, the software is expected to deliver standardized, high-resolution shoreline change data for diverse coastal areas, enhancing regional erosion monitoring and supporting evidence-based, adaptive coastal planning under increasing climatic and tectonic pressures. While the approach demonstrated robust performance, limitations include the reliance on satellite imagery availability and potential uncertainties in shoreline extraction under complex wave and tidal conditions. Future work should focus on integrating higher-frequency field measurements, expanding the methodology to other morphodynamic settings, and improving the automation of sediment–morphology correlation analyses.