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Article

Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery

by
Idania C. Briceño de Urbaneja
1,2,*,
Josep E. Pardo-Pascual
2,
Carlos Cabezas-Rabadán
2,3,
Catalina Aguirre
4,5,
Carolina Martínez
6,7,8,9,
Waldo Pérez-Martínez
1,2 and
Jesús Palomar-Vázquez
2
1
Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Camino La Pirámide 5750, Huechuraba 8580745, Chile
2
Geo-Environmental Cartography and Remote Sensing Group, Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain
3
Univ. Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, F-33600 Pessac, France
4
Escuela de Ingeniería Oceánica, Universidad de Valparaíso, Av. Brasil 1786, Valparaíso 2362844, Chile
5
Centro de Ciencia del Clima y la Resiliencia [FONDAP/ANID 1523A0002], Blanco Encalada 2002, Santiago 8320000, Chile
6
Instituto de Geografía, Facultad de Historia, Geografía y Ciencia Política, Pontificia Universidad Católica de Chile, Campus San Joaquín, Avda. Vicuña Mackenna 4860, Macul 7820436, Chile
7
Centro de Investigación para la Gestión Integrada del Riesgo de Desastres [CIGIDEN], Pontificia Universidad Católica de Chile, Campus San Joaquín, Avda. Vicuña Mackenna 4860, Macul 7820436, Chile
8
Instituto Milenio en Socio-Ecología Costera [SECOS], Av. Libertador Bernardo O’Higgins, 340, Santiago 8331150, Chile
9
Centro UC Observatorio de la Costa, Pontificia Universidad Católica de Chile, Campus San Joaquín, Avda. Vicuña Mackenna 4860, Macul 7820436, Chile
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(13), 2360; https://doi.org/10.3390/rs16132360
Submission received: 5 April 2024 / Revised: 10 May 2024 / Accepted: 15 May 2024 / Published: 27 June 2024
Browse Figures
Figure 1
<p>(<b>a</b>) Study site of Playa Grande Beach in Cartagena Bay (Valparaiso Region), located between Punta Lacho and Punta Vera Capes. Green point wave node and (<b>b</b>) location of Valparaíso Region in Central Chile.</p> "> Figure 2
<p>Methodological workflow.</p> "> Figure 3
<p>Number of SDS obtained per year over the period studied. In 1985, there was a single image available, while in 2019, there were 52.</p> "> Figure 4
<p>Variability of height and period of the waves throughout the year, from January (month 1) to December (12).</p> "> Figure 5
<p>Seasonal variability of wave direction according to wave height percentile. The highest (lowest) waves are defined as those presenting a Hs ≥ P90 (≤P10), from January (month 1) to December (12).</p> "> Figure 6
<p>Annual mean wave climate conditions (1985–2019) described by the Hs (m) and Tp (s).</p> "> Figure 7
<p>Spatiotemporal model of beach width changes. The colors show the displacements of the shoreline landward (erosion represented by reddish colors) or seaward (recovery, blueish colors). Black horizontal lines divide into four sectors according to shoreline behavior.</p> "> Figure 8
<p>Variability of the mean shoreline position on Playa Grande beach (1985–2019) and the highest waves registered. Changes in mean beach width between 1985 and 2019 and their relationship to the moments with more energetic swells (Hs &gt; 3.12 m, i.e., the 95th percentile of the series analyzed).</p> "> Figure 9
<p>Comparison of the average beach width in sectors 2 and 4. Seasonal beach width variability is much higher (sometimes up to 80 m) in S2 than in S4.</p> "> Figure 10
<p>Seasonal beach width variability in sectors 2 and 4 (in blue and red respectively) during the years 1995–1996 (dashed line) and 2017–2018 (solid line). Both series present the width changes relative to the position at the start of the year.</p> "> Figure 11
<p>Temporal series monthly average wave energy flux.</p> "> Figure 12
<p>Conceptual model of the dynamics and sediment redistribution of Playa Grande (Cartagena Bay). The direction and energy of the waves and the orientation of the four beach sectors are considered. The size and direction of the most common waves are shown as those with the highest energy (green, from 233°), medium (yellow, 241°), and lowest (red, 255°). These wave types are translated into longshore transport with different magnitudes (represented by the various lengths of the arrows) in each beach sector.</p> "> Figure 13
<p>Comparison of beach width changes and the Oceanic Niño Index (ONI) is the difference between a three-month average SST over an ocean region from 120W to 170W along the equator and the long-term average for the same three months. Data from the US National Weather Service (<a href="https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php" target="_blank">https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php</a>, accessed on 17 February 2024).</p> "> Figure 14
<p>Time series of monthly ONI averages in black, significant height in blue, mean period in red, and mean direction in green.</p> "> Figure 15
<p>The relationship between the mean annual Hs and beach width (both in m) is described by a linear correlation (expressed as R<sup>2</sup>). To establish the linear correlation, the 2010–2012 data associated with earthquakes (highlighted in green) have been removed.</p> ">
Versions Notes

Abstract

:
Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism.

1. Introduction

Coastal monitoring has become challenging in recent years due to changes caused by extreme events, climate change, and anthropogenic activities [1,2]. Continuous exploitation of coastal zones alters the dynamics and interactions between natural and socio-economic components [3]. They represent large and vulnerable socio-ecological systems home to 60% of the world’s population [4].
In Chile, 5.7% of the inhabitants live in coastal areas below 10 m a.s.l. This means that more than 1 million people live in areas highly threatened by extreme events linked to the increased frequency and intensity of waves, changes in sea level, tsunamis, and anthropogenic action [5,6].
The shoreline position is an indicator of sandy beaches’ morphological state and dynamism [7]. It allows for quantifying short and long-term changes caused by hydrodynamic events, geomorphological alterations (e.g., barrier island migration), and other disruptive factors (e.g., seismic events and tsunamis) [8]. The shoreline is an essential geo-indicator in coastal evolution and provides the most critical information on coastal relief dynamics [8]. Thus, it serves as a valuable descriptor of beach response to disruptive events [9] and forecasting future scenarios [10].
Satellite-derived remote sensing has become an invaluable tool in monitoring coastal areas. This technique allows the definition of robust and comparable datasets to characterize the beach morphology over vast coastal areas by defining indicators as the instantaneous water/land interface or waterline position [11]. The limitations imposed by the coarse spatial resolution of the open-access satellite imagery (30 m for the Landsat and 10–20 m for the Sentinel 2 constellations) have been addressed by algorithms such as SHOREX [12,13], CoastSat [14], CASSIE [15] or SAET [16]. As a result, the waterline position is defined with sub-pixel accuracies typically below 10 m RMSE in diverse coastal types [17], which is tidal and energetic environments must be translated into the same elevation datum to define satellite-derived (SDSs) comparable over time [18].
Sustained on the ability to obtain multidecadal series of satellite data, different studies have attempted to analyze the links between changes in the beach morphology and climate variabilities at regional and global scales (e.g., [19,20,21]). In the Pacific Basin, the dominant mode of interannual climate variability El Niño–Southern Oscillation (ENSO), stands out due to its linkages and influence over a broad range of oceanic and atmospheric dynamics. ENSO may translate into the alteration of nearshore processes and beach morphology through shoreline position changes (e.g., [20,21]). Nevertheless, the analysis of this dynamism is hampered as long-term data series in Central and South America are still scarce [21].
The possibility of obtaining large satellite-derived morphological datasets over thick periods and coastal territories poses a significant challenge for managing and exploiting the resulting data. New analytical methods have been developed to take advantage of the wealth of information to quantify, analyze, and model the patterns of changes in sandy shorelines. In this sense, spatial-temporal models (STMs) of beach changes appear as a solution to characterize the beach state when punctual events such as storms affect large coastal segments and thus facilitate the analysis of temporal and geographical changes at different scales [22].
The central zone of Chile (CZC) has been affected by great magnitude earthquakes, followed by tsunamis (e.g., Algarrobo 1985 8.0 Mw; El Maule 2010 [Mw 8.8]; Illapel 2015 [Mw 8.4] [23]). The Pacific Coastal was also affected by strong ENSO events El Niño-Niña (1997–98; 98–2000) [24]; 2015–2016 [5]. Hydro-meteorological events in 2006, 2009, 2013, 2015, 2017, and 2018 caused damage to infrastructure, loss of human lives, cessation of port operations, and shipwrecks [6,25,26]. Most of the research about multitemporal changes of the Chilean coasts has been carried out from aerial images, topographic surveys, and high-resolution satellite images in specific temporal domains, i.e., mainly in summer seasons, conditioned to the availability of cartographic, photogrammetric, and economic resources for in situ data collection [26,27,28,29]. At the same time, other works analyzing the coastal changes have focused on quantifying the financial and morphological impacts of extreme events (e.g., [25,30,31]). In Chile, there is no information on the coastal position changes considering its interannual variability, which is essential for defining at what time of the year and where the most significant morphological transformations occur. This represents an essential limitation for implementing numerical models that consider the future behavior of these beaches according to climate change scenarios [32]. On the Chilean coast, the changes on sandy shores require robust tools to reconstruct their recent past, which is still largely unknown. The open-source satellite imagery with a high revisit rate could provide adequate temporal sampling to study the highly dynamic phenomena that determine coastal morphodynamics [33].
Based on the information provided by the satellite-derived shorelines, this work will characterize the beach morphological changes of Cartagena Bay (Central Chile) from 1985t2019. The analysis of these changes in the context of the transforming elements of the beaches, such as waves, and the occurrence of different climatic phases (ENSO) and extreme events (earthquakes, tsunamis) will make it possible to understand the dynamism and evolution of the sector and to propose a conceptual model of its behavior.

2. Study Area

Chile is in the subduction zone between the Nazca and South American plates, extending 3500 km. Central Chile’s coastal zone is located on a historically active segment of the megathrust between 30° and 34°S [34]. Cartagena Bay is located in the Province of San Antonio in the Algarrobo Santo Domingo conurbation. It is considered one of the areas with the highest population growth [35], showing significant residential and infrastructural development on the coastal front. The Valparaíso Region is dominated by a Mediterranean climate with an annual precipitation of 413.1 mm and an average annual temperature ranging between 11.4 and 17 °C [36].
The Bay has a length of approximately 6 km, is backed by a dune field, and is bounded by two rocky promontories (Punta del Lacho in the North and Punta Vera to the South). The Bay is composed of Playa Grande Beach, ~5.4 km in length on the north, which is the focus of this study, and a small beach to the south (Playa Chica) of ~400 m; however, it has been excluded from this analysis due to its short length and physical separation from the rest. The mouth of the Cartagena River forms a coastal wetland, currently heavily intervened by different urban uses (Figure 1).
It is a microtidal coast (tidal range ~1.8 m) composed of fine to medium sands, which vary over the different beach sub-environments (e.g., shoreface (D50 = 311.86 μm) foreshore (249.60 μm) and backshore (248.74 μm)) (www.gorev.moncosta.org, accessed on 25 January 2024).
The wind-wave climate of the study area is primarily influenced by waves generated over the southern Pacific Ocean in the Westerly wind belt (40°S–60°S). Intense fetches, typically associated with extratropical cyclones (low-pressure cores), serve as the energy sources of incident waves along the coasts of central Chile. These waves travel across the South Pacific and reach the study area as the prevailing swell, featuring a significant wave height of approximately 2 m, mean periods of 8–9 s, and a propagation direction from the southwest (e.g., [37]). This is the case even though, during the austral winter, the Westerly wind belt shifts northward, enabling the formation of higher energy cyclones at lower latitudes. These cyclones generate powerful winter surges that impact the coast. Then the flat annual cycle is attributed to the presence of an atmospheric coastal low-level jet in central Chile, which is more frequent during the austral summer (e.g., [38]) and generates local waves (wind-sea), by lowering the annual variability of wave heights in this region [39].
A singular morphological feature in this region is the existence of a submarine canyon in front of the port of San Antonio. However, the canyon does not seem to absorb the material contributed by the Maipo River to the coastal drift that nourishes the Playa Grande de Cartagena [40]. The main sedimentary contributions come mainly from the Maipo River, which is redistributed by the coastal drift that transports sediments from south to north, providing the sand that originates in andesitic and volcanic materials [40].

3. Materials and Methods

To understand Playa Grande’s evolution, the analysis was sustained on the shoreline position dataset defined from satellite imagery, which allowed the generation of a spatial-temporal model characterizing the changes and evolution of Playa Grande’s coastline. The wave regime was also described, and its influence on the shoreline change was analyzed.

3.1. Wave Conditions

To characterize the wave conditions during the analyzed period (1985–2019), the global wave hindcast GOW2 was utilized [41] Instituto de Hidráulica Ambiental de Cantabria IHC (https://ihdata.ihcantabria.com/, accessed on 15 November 2022). The WAVEWATCH III wave model with a multigrid two-way nesting configuration from 1979 onwards was used in this hindcast. The grids were generated using ETOPO1 bathymetry [42] and coastlines from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG). The model spectral domain was discretized by 24 direction regular bins and 32 frequencies ranging non-linearly from 0.0373 Hz to 0.7159 Hz, with each frequency being 1.1 times the previous one. To force the model, hourly ice coverage and wind data from the Climate Forecast System Reanalysis (CFSR) were used from 1979 to 2010, while since January 2011, forcings from CFSv2 were used. The reader is referred to the work by Amante et al. [42] for more details about model parametrizations, coefficients used, and validation performed with spectral wave data from buoy stations and multi-mission satellite altimeter measurements. In this model, wave parameters, including the significant wave height (Hs), wave period (Tp), and wave direction of the node 71.625°W and 32.75°S in the hourly resolution, were employed.

3.2. Definition of Satellite-Derived Shorelines and Spatial-Temporal Model of Beach Width Changes

The optical images acquired during the period 1985–2019 by Sentinel-2 (MSI sensor) and Landsat 5 (TM), 7 (ETM+), and 8 (OLI) satellites were used in this study (Figure 2). They were obtained free of charge from the Copernicus Open Access Hub (https://scihub.copernicus.eu/, accessed on 3 May 2023) and the U.S. Geological Survey (USGS) Earth Explorer (https://earthexplorer.usgs.gov/, accessed on 3 May 2023), respectively. The selection and downloading of the images, as well as their preprocessing steps and subpixel definition, were carried out using the SHOREX tool (see details in [13,43]) following the phases of (i) image downloading, (ii) selection of images non-affected by clouds, (iii) image sub-pixel georeferencing, (iv) approximate shoreline definition at pixel level by applying the water index AWEInsh [44], with a threshold = 0, and (v) definition at sub-pixel level by points (vertices) obtained every 5 m and 7.5 m (S2 and Landsat imagery respectively) describing the position of the water/land intersection. No tidal or other elevation corrections were applied as the study site is located on a microtidal coast, and slope data was unavailable to address such corrections. Therefore, the 590 resulting instantaneous waterlines were assimilated as shorelines (Figure 3). SWIR1 bands of 1.55–1.75 μm, 1.57–1.65 μm, and 1.56–1.65 μm from Landsat 5 and 7, Landsat 8 OLI, and Sentinel-2, from the summer of 1985 to the summer of 2019 were used.
A spatial-temporal model of the beach changes was obtained from SDS following the methodology described by Cabezas et al. [45]. To do so, a baseline divided into 100 m segments was defined, and the distance to the points (vertices) defining each SDS was measured. The shoreline displacement of beach width changes was calculated by taking the average shoreline position during 1986 as a reference, considering that the single SDSs available in 1985 could lead to a biased model. Subsequently, all the measurements were organized over time and space. Their interpolation allowed the transformation of the discrete information into a continuous spatial-temporal model. This led to a Hovmöller-style diagram with the values of the shoreline changes organized in 100 m/day cells, making data homogeneously available for the whole site and study period [45].

4. Results

4.1. Wave Climate Analysis

The main statistics of incident wave conditions show differences between the mean annual and winter conditions (Table 1). The annual mean significant wave height (Hs) is 2 m, and 90% of them are recorded between 1.19 and 3.12 m. The wave peak periods (Tp) are quite high (average 13.9 s and 90% are between 10 and 18 s). The dominant direction is WSW (241°), although a small oscillation is observed around this dominant direction. During winter, higher values of Hs (~10 cm) appear (average of 2.11 m), with slightly shorter periods (13.51 s) and a more westerly origin (243°) compared to the rest of the year.
When analyzing the monthly variation of wave characteristics (Figure 4 and Figure 5), it can be seen how, during the spring and autumn, the waves come with a somewhat more southerly direction, while in early summer and winter, waves with a slightly more westerly origin dominate. It is evident, however, that the average difference is minimal (less than 7°), but there is a clear relationship according to the height of the waves. The lower waves come from more southerly positions and have less seasonal variability. Conversely, the higher waves come from more westerly directions and oscillate more throughout the year.
The annual evolution of the significant wave height and the peak period over the study period (Figure 6) shows oscillations over time, sometimes following a multiannual pattern. The results show marked differences between peak years (1989, 1998, 2010, 2011, 2016) and less energetic years (1996, 1997, 2002 or 2004).

4.2. Shoreline Changes

Once all the SDS have been analyzed and the STM has been organized, it is possible to perform analyses on the beach’s scale and partially focus only on different geographic sectors or periods. The STM of changes allows us to describe, on the one hand, the precise seasonal dynamics observed, with greater beach widths in summer than in winter (Figure 7).
The quantification of the average width and rate of change of the shoreline position over the whole period under study shows significant differences between the sectors (Table 2). Generally, the northern part (sector 4) seems to follow a more cumulative trend than the southern end. The low R2 values are related to multi-year (decades) erosion/recovery cycles.
Taking the beach as a whole, the beach does not show a clear trend over the period 1985–2019. On the contrary, a well-defined oscillatory pattern is visible at different time scales (both sub-annual and multi-annual). Multi-year phases in which erosion predominates (1985–1990; 1997–2000; 2009; 2017–2019) are evident, while during others, there is a clear dominance of accretion (Figure 8). Among them, the recovery/accumulation detected in all areas in 2011 and 2012 stands out. In this sense, it is remarkable how since 2012, when a peak in width was recorded, the beach has followed an erosive trend until the end of the series.
The analysis of the evolution of the beach as a whole allows us to establish relationships with wave conditions and episodes of higher energy. Figure 8 shows the average evolution of the beach, i.e., the average of all changes on all dates and locations and the hourly records of significant heights above the 95th percentile (Hs > 2.12 m). A thorough analysis of the distribution of the most aggressive waves shows that they occur more frequently during the winter. This is associated with a decrease in the width of the beach, which varies from year to year. In general, when the beach is more expansive, the seasonal variability is also wider, while in years when the beach is narrower, the variability is much less marked.
The STM analysis allows us to recognize geographical differences in the evolution of the SDS. Four distinct sectors (Figure 7) have been differentiated based on this evolution. Thus, Sectors 1 and 3 experience pronounced erosive episodes, in contrast to Sectors 4 and sector 2, where the accretion is predominant. It is also noteworthy that these episodes occur in distinct temporal phases. Thus, between 1985 and 1990, the entire beach remained narrower, followed by a period between 1991 and 1996 when it gained width. Between 1997 and 2000, the width decreased again, but more pronouncedly in sectors 1 and 3. The years 2001–2008 presented overall accretion, but more prominently in sector 2 than in the rest. In 2009, a general retreat was evident throughout the area, followed by a significant width gain between 2010 and 2012, especially in the northern zone (sector 4). From 2013, a more pronounced retreat is observed in sector 3 and less so in the rest. In 2016, an increase in width was again detected, but from 2017 onwards, there were noticeable retreats, especially pronounced in sectors 1 and 3.

5. Discussion

The evolutionary dynamics of the Playa Grande de Cartagena are the result of multiple processes that interact at different spatial and temporal scales. The analysis of the results focuses on four aspects: the sub-annual dynamics controlled by the seasonal wave climate, the shoreline dynamism as a response to the sediment redistribution along Cartagena Bay, the interannual climate variability influenced by the ENSO phases, and the mid-to-long-term trends of the sector in which punctual disruptive events as earthquakes and coastal storms and the human actions play an essential role.

5.1. Seasonality

The beach presents a well-defined seasonal regime, wider during the summer months (i.e., December to March) and narrower during the rest of the year, being its minimum width during early winter (June). Nevertheless, the STM suggests the seasonal variability is not homogeneous along the beach (see Figure 9). This can also be interpreted by comparing the average shoreline position changes of sectors 2 and 4 (Figure 9), showing that the seasonal variability is generally higher in sector 2. However, this dynamism significantly changes over the study period. Thus, between September 1995 (end of winter) and February 1996 (end of summer), sectors 2 and 4 experienced significant differences in the variability of the shoreline position (Figure 10). This contrasts with the similar variability experienced between the summer of 2017 and the following winter by sectors 2 and 4. This leads us to think that the beach state and the changes in the seasonal profile play a key role in this variability. During summer, the lower waves accumulate sediment, leading to the beach widening, and the gentler slope favors a greater dynamism of the shoreline position. On the contrary, when the beach is in a narrower phase (winter season), the steeper slope leads to a lower shoreline position (and width) variability.
The origin of this seasonal dynamism is meteorological, and it is associated with the fact that summer waves have lower steepness (lower Hs and longer wavelength or Tp). Therefore, these waves would be more constructive, dragging sediment towards the shore as suggested by classic works on nearshore processes (e.g., [46]). The relatively sizeable seasonal width variations are partially linked to the existence of gentler slopes and somewhat finer sediments (e.g., [43,47]), which, in the case of Playa Grande Beach, ranges from small to middle sand (www.gorev.moncosta.org, accessed on 14 February 2024). The fact that width variations are more significant on wider beaches (Table 2) suggests that the beach face slope is lower and probably formed by somewhat finer sediments (e.g., [43,47]).

5.2. Sediment Redistribution and Shoreline Dynamism

Wave energy flux variability spans a wide range of temporal and spatial scales. Figure 11 shows the frequency, which may be dominated by a few narrow peaks, evidencing a vertical and temporal structure of each wave component and their interaction to form the total energy flux. The dominant energy from the SW shows a decrease during the summer, consistent with the decrease in the intensity of the winds over the Southern Ocean and the displacement of the storm tracks towards higher latitudes. However, during the summer, we can see an energy maximum that has a propagation direction from the NW, which corresponds to waves of ~15 s’s period. This energy maximum in the spectrum is associated with the increase in the intensity of extratropical cyclones in the northern hemisphere. In addition, it is observed that during the summer, the spectrum contains more energy at higher frequencies, with a propagation direction from the south. This energy input to the swell is associated with the local wind [39], as a low-level atmospheric jet on the central coast of Chile is more frequent during spring and summer [38].
The evolutionary behavior of the different sections composing the beach seems to be related to their orientation. According to the wave orientations, the diverse coastal orientations would be responsible for forming longitudinal currents in opposite directions. The analysis of the potential longshore transport effect, considering the direction of the waves, the orientation of the different beach sections and the wave energy, has resulted in the proposal of a conceptual model for Playa Grande Beach (Figure 12)
Since most of the sands on this beach came from the Maipo River, which flows about 7 km to the south (see Figure 1), Del Canto and Paskoff [40] indicated that the main coastal drift at Cartagena Bay was from south to north. If these observations are correct, the submerged coast in Punta Vera (rocky promontory in the southern area of Cartagena beach) must be considered shallow enough for this longitudinal transport to the north. In the southern section of Playa Grande Beach (sectors 1 and 2, see Figure 7), the orientation becomes N-S, so the transport is expected to continue towards the north. On the contrary, in sectors 3 and 4, the coastal orientation is NNW-SSE, which implies that most of the swells will not generate longitudinal currents because they are perpendicular to the coast. The lower waves, which tend to originate more southerly (see Figure 5), can still carry sediments northward. In contrast, the more energetic waves emanating from a more westerly source will cause transport to the south of greater magnitude. In sectors 3 and 4, the longitudinal transport towards the south could sometimes become significant, given that the response of the incident waves is from 255°. However, given its relatively low frequency, it will not reach a sizeable total magnitude. On the contrary, sectors 1 and 2, despite being mostly medium waves (241°), are expected to cause a more usual northward sediment displacement. Finally, waves coming from the south (233°) can contribute to a northward drift in all the sectors, but in a low magnitude due to their low energy and frequency.
In response to these three major wave types, sediment drift from sector 1 to sector 2 is expected to occur systematically. In contrast, sector 1 will only be fed under high energy conditions, as it must overcome the rocky promontory of Punta Vera. This may explain why sector 1 shows an erosive behavior, as sediment transported towards the northern sectors of the beach is not systematically replaced by new sediment coming from the south. At the same time, occasional sediment inputs from the Cartagena River (which flows into Sector 2 (Figure 10) may enhance the cumulative dynamics of this section. However, this material may also move northward. Finally, sector 4 will receive sediment from the south that cannot migrate north because it is protected by the rocky headland of Punta Lacho (the northern end of Cartagena Bay). Therefore, although sediment migrations to the southern sectors may occur during stronger waves (255°), they may be compensated by the reception of sand associated with the less energetic but more frequent waves from the south (241° and 255°). This is not the case in sector 3, which can be a “passage” sector, so during the periods with more energetic waves and transport to the south, negative sediment balances will be observed, and beach erosion will occur.

5.3. Influence of Interannual Climate Variability on the Shoreline Position

The evolution of Playa Grande beach width during the 35 years shows marked cycles, alternating erosive and recovery phases. Comparing the shoreline position changes on an annual scale (Figure 7) with the significant wave height (Figure 8) shows how, in some years, there is an inverse relationship between both parameters. At the same time, relatively well-marked cycles (peaks in 1989, 1998, 2010, and 2017) appear separated by less energetic years. This suggests a potential relationship with different phases of ENSO interannual climate variability, which seem related to a broad range of oceanographic and atmospheric processes along coastal regions [21,48,49,50].
To analyze the influence of this climatic phenomenon on beach morphology, the Oceanic Niño Index (ONI) was used and compared with changes in mean beach width (Figure 13 and Figure 14). The results show that the most significant index fluctuations are related to Playa Grande shoreline changes. Thus, the most pronounced ENSO phases are associated with phases of shoreline retreat, while La Niña phases (ONI < 0, see Figure 11) seem to be associated with beach recovery. The ENSO 97/98 event appears to be related to a significant erosion: between April 1997 and June 1998, Cartagena Beach experienced an average erosion of over 25 m. On the contrary, La Niña seems to be associated with beach recovery during 1988/89, 98–2000, and 2010–2012. The last of these multiannual events is associated with an average accumulation of about 50 m between November 2010 and September 2012. In some cases, the accumulation of La Niña events of significant magnitude in consecutive years appears to lead to substantial beach recovery in the years following the maximum negative value of the ONI index. The present results align with the analysis of Vos et al. [21], who compared the Multivariate ENSO Index (MEI) with shoreline position series at different Pacific coasts. Their results showed that El Niño (La Niña) phases of greater magnitude are often associated with widespread erosion (accretion) of beach morphology throughout the Pacific basin. However, sometimes, ENSO events do not appear to be associated with erosive shoreline displacements. This is the case of the events of 2009/10 and 2015/16, for which, despite the evidence of damage in certain parts of the Chilean coast [26,34,51], the shoreline position in Cartagena Bay did not show the expected retreat. As pointed out by Vos et al. [52] indicate that the 1997/1998 ENSO was the most erosive, with almost 50% of the transects studied having widespread extreme erosion. Regarding the shoreline accretion associated with La Niña, sometimes the erosion may have presented such a large magnitude that the recovery is not immediate, and it takes place in the medium term due to the succession of different years in the Niña phase.
Figure 14 presents the ONI time series, where negative values are associated with the warm phase (El Niño). The El Niño events of the greatest magnitude that occurred between 1984 and 2020 are El Niño 1997–1998 and El Niño 2015–2016. When the ONI reaches its minimum value, historical maxima in period, direction, and significant height are observed. These results show that the wave heights in the El Niño phase increase.

5.4. Mid and Long-Term Trends

The results show how the multiyear oscillatory behavior of the coastline predominates over multidecadal trends (Figure 7). Thus, over the 35 years, no clear trend has been detected. This fact contrasts with the observation made by Del Canto and Pascoff [40], who detected an increase in beach width of up to 200 m when comparing the situation in the 1980s with the 1875 nautical chart of the port of San Antonio, built at the end of the 19th century. In that sense, it should be noted that the present analysis started when the beach was very narrow (mid-80s), which impedes the determination of whether the long-term trend of Playa Grande is stable or, probably, slightly erosive. In any case, the contrast of this study with our results would indicate a change of trend in the area, moving from a cumulative phase towards a stable or erosive one. Since 2012, when a beach width peak was recorded, the beach has shown an erosive trend. The retreat is more marked in sectors 1 and 3, while sector 4 is becoming wider in the northern part. The observations align with the findings by Martínez et al. [52] from the combination of remote sensing and topographic surveys, indicating that the beach experienced an erosive trend during the last few years.
Among the possible causes of shifting towards an erosive stage, the reduction in the arrival of sediments to Cartagena Bay should be noted. A lower sedimentary contribution from the south of the study site may be partially associated with the construction of the Port of San Antonio. At the same time, there was a decrease in the flow of the Maipo River, which went from 123 m3/s in 1986 to 35.6 m3/s in 2019 (www.dga.cl, accessed on 30 March 2024). The reduction in sediment input to the coastal system has been observed at other sites in Chile, e.g., the Aconcagua River (1984 32.4 m3/s to 2019 2.7 m3/s, north of the study area, Maule River (895 m3/s to 96.3 m3/s) (www.dga.cl, accessed on 30 March 2024). Furthermore, the megadrought between 2010 and 2019 produced precipitation deficits of approximately 30% in much of Central Chile [53,54]. This would align with reduced sediment input to the coastal system from rivers occurring in a generalized manner worldwide [55,56].

5.5. The Role of Punctual Disruptive Events: Earthquakes, Tsunamis

The negative relationship between wave height and shoreline changes (Figure 15) is statistically weak (R2 = 0.24), suggesting the existence of other factors controlling beach morphological variability. One of these is undoubtedly seismic dynamics, as three major earthquakes affected this area during the study period (1985, 2010, and 2015). The measurement of vertical displacement values is not easy to carry out accurately. There is some debate about the magnitudes depending on the specific system used and the location where the measurements were carried out. Quezada et al. [57] speak of a 30–40 cm subsidence at Playa Chica, based on observing algae in a higher position than expected. Grandin et al. [58] and Vigny et al. [59] deduced a displacement of 16 cm at Rocas de Santo Domingo, 7 km to the south, based on GPS measurements. In contrast, no vertical changes were observed at Playa Grande de Cartagena from measurements of Lithothamnirides white coral. Nevertheless, there is agreement that, in some cases, the tsunami penetrated significantly into the beach. After the Maule earthquake (2010), the tsunami penetrated up to 103 m into the beach, with a run-up of between 2 m [60] and 4 m [61,62]. Indeed, this coast has been affected by tsunamis in recent years. Martínez et al. [35] cited the Maule (2010), Pisagua (2014), and Ilalpel (2015) earthquakes as the most important, in addition to the tsunami generated by the great earthquake of March 2011 in Japan.
The effects of the 1985 earthquake are barely detected in this analysis of the SDS. This is because only one useful satellite image (acquired after the earthquake) is available for that year, which prevents its use as an element of comparison. Regarding the second earthquake (27 February 2010), it is very striking that, despite its visible impact on that beach, where there is evidence of 100 m penetration of the tsunami wave, the SDS of that year does not show any significant setback. The SDSs progressively indicate a widening of the beach that will continue in the following two years (2011 and 2012). It is also striking that this increase in beach width is unrelated, as in the rest of the cases, to a decrease in wave energy. On the contrary, these are high-energy years, so these changes may be associated with the earthquake and an extraordinary input of sediment related to the impact of the tsunami. It is possible that part of the sands removed by the tsunami wave returned to the sea, contributing an extraordinary volume of material that would move south and return to the beach in the following years. This could be at least one of the causes that could explain the widening of the beach recorded in those years.
There is an anomaly in the behavior of the evolution of beach width concerning wave energy conditions. If we analyze the correlation between wave height and beach changes without considering data from 2010, 2011, and 2012, we detect substantial improvement in that relationship (R2 = 0.46).
Interestingly, there was a strong El Niño in 2010 (ONI > 2.5), but in the following years (2011, 2012), the ONI quickly became negative. However, the swell data indicate higher waves, while we see a widening on the beach. The effect of El Niño is not limited to wave conditions; it can also affect sea level. Although it is possible that sea levels were slightly lower during these years, there is no available tide gauge data to confirm this.
The direct effects of the earthquake of 16 September 2015 on the beach were not noticeable. The following day (17 September 2015), an SDS showed a small average beach advance of 4.7 m compared to the previous one on 1 September 2015. It is, therefore, clear that the above-mentioned concerns in relation to the 2010 tsunami will depend on the actual impact of each tsunami on a particular beach.

6. Limitations and Future Research

The high presence of clouds in Central Chile limits the availability of satellite images and SDSs.
This leads to a shoreline dataset with a scarcity of data during the first years. This limits the usage of optical imagery for analyzing in detail the response to specific events, such as the 1985 earthquake; there are no shoreline position data available before and after the event. The more recent combination of the Landsat 7, 8, and 9 missions and Sentinel 2 provide greater robustness to characterizing beach changes because more images are available (see Figure 2).
The accuracy and representativeness of SDS obtained by the SHOREX tool have been demonstrated in different coastal environments (microtidal, mesotidal, and macrotidal, and with different levels of energy). However, this work defined the shorelines without applying tidal or run-up corrections. We have calculated the RTR to determine the wave dominance at this beach (RTR = 1.46). The large availability of satellite imagery (and hence shoreline positions) during the study period has allowed a robust representation of erosion/accretion patterns while minimizing the influence of the instantaneous tidal state. However, the lack of tidal correction prevents this data set from answering complex questions, such as the link between climate oscillations and large-scale shoreline changes. These methodological limitations are essential and should be addressed if more ambitious statistical analyses are undertaken to study the relationship between shoreline position and climate [50].
Future validations using in situ data (such as from drone surveys) are recommended to extract the average slope in each sector. This would enable the application of tidal and wave corrections to the SDSs and, in addition, provide an opportunity to assess the shoreline accuracy on this site. At the same time, and considering the importance of the seasonal patterns of the shoreline changes, future analysis should consider the seasonal decomposition of the beach width time series to estimate the average seasonal width of each sector. Finally, when the required auxiliary data are available, estimating the wave energy flux and the alongshore sediment transport according to the orientation of the different coastal sectors could contribute to a better understanding of the dynamics and sediment redistribution along Cartagena Bay.

7. Conclusions

Recent innovations in remote sensing techniques enable efficient coastal change quantification at unprecedented spatial and temporal scales. The results demonstrate the high potential of deriving multidecadal datasets characterizing the beach morphology from images acquired by the optical satellites of the Landsat and Sentinel-2 series. Large shoreline position datasets enable the analysis of coastline changes on sites still relatively unknown, such as the Playa Grande Beach (Cartagena Bay) in Central Chile. This makes it possible to improve the comprehension of morphological changes about multiple natural and anthropogenic factors that condition the redistribution of sediment and beach morphology. Some phenomena, such as earthquakes or coastal storms, are punctual and generate changes. In contrast, changes in wave patterns are variable at different time scales, both seasonal and recurrent over the years, and linked to multi-annual and larger-scale processes such as ENSO climate phases. The multi-year oscillatory pattern of the shoreline position predominates over multi-decadal trends, with Playa Grande being embedded in an erosional cycle over the period 2012–2020. At the same time, different trends are experienced by the various sectors of the bay, being more cumulative at its northern end and erosive at its southern end. The proposed conceptual model describes the sediment redistribution along the different beach sectors according to their orientations and sediment input and in response to the great diversity of interconnected factors.

Author Contributions

Conceptualization, I.C.B.d.U., J.E.P.-P., J.P.-V. and C.C.-R.; methodology, I.C.B.d.U., J.E.P.-P., C.C.-R., C.A. and J.P.-V.; software, J.P.-V. and J.E.P.-P.; validation, I.C.B.d.U., J.E.P.-P., C.C.-R., J.P.-V., C.M. and C.A.; formal analysis, I.C.B.d.U., C.M., J.E.P.-P., J.P.-V., C.A. and W.P.-M.; Writing—original draft preparation, I.C.B.d.U. and C.C.-R.; writing—review and editing; I.C.B.d.U., J.E.P.-P., W.P.-M., J.P.-V., C.C.-R., C.M. and C.A.; visualization, C.A. and J.P.-V. Supervision, J.E.P.-P. and J.P.-V.; Funding: W.P.-M. and I.C.B.d.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Agencia Nacional de Investigación y Desarrollo (FONDEF IDeA I+D 2019, Proyecto ID19I10361), and by the projects MONCOSTA Monitoreo satelital de la dinámica y evolución de la costa; SIMONPLA subproject integrated within the ThinkinAzul programme by the Ministry of Science and Innovation with funds from the European Union NextGeneration EU (PRTR-c17.I1) and Generalitat Valenciana, and the MONOBESAT (PID2019−111435RB-I00) by the Spanish Ministry of Science, Innovation and Universities, while C.C.-R is funded by the M. Salas contract (Re-qualification programme) by the Ministry of Universities financed by the EU—NextGenerationEU, and the grant PAID-06-22 (CCR) by the Vicerrectorado de Investigación de la Universitat Politècnica de València (UPV). ANID-FONDECYT N°1200306, ANID/FONDAP/15110017; Instituto Milenio en Socio-ecología Costera (SECOS) ICN2019_015.

Data Availability Statement

Data SDS, critical areas, erosion rates, sedimentology, climate wave summarize, DSM from UAV are available online from supporting studies Moncosta–Coastal Monitoring.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Study site of Playa Grande Beach in Cartagena Bay (Valparaiso Region), located between Punta Lacho and Punta Vera Capes. Green point wave node and (b) location of Valparaíso Region in Central Chile.
Figure 1. (a) Study site of Playa Grande Beach in Cartagena Bay (Valparaiso Region), located between Punta Lacho and Punta Vera Capes. Green point wave node and (b) location of Valparaíso Region in Central Chile.
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Figure 2. Methodological workflow.
Figure 2. Methodological workflow.
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Figure 3. Number of SDS obtained per year over the period studied. In 1985, there was a single image available, while in 2019, there were 52.
Figure 3. Number of SDS obtained per year over the period studied. In 1985, there was a single image available, while in 2019, there were 52.
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Figure 4. Variability of height and period of the waves throughout the year, from January (month 1) to December (12).
Figure 4. Variability of height and period of the waves throughout the year, from January (month 1) to December (12).
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Figure 5. Seasonal variability of wave direction according to wave height percentile. The highest (lowest) waves are defined as those presenting a Hs ≥ P90 (≤P10), from January (month 1) to December (12).
Figure 5. Seasonal variability of wave direction according to wave height percentile. The highest (lowest) waves are defined as those presenting a Hs ≥ P90 (≤P10), from January (month 1) to December (12).
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Figure 6. Annual mean wave climate conditions (1985–2019) described by the Hs (m) and Tp (s).
Figure 6. Annual mean wave climate conditions (1985–2019) described by the Hs (m) and Tp (s).
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Figure 7. Spatiotemporal model of beach width changes. The colors show the displacements of the shoreline landward (erosion represented by reddish colors) or seaward (recovery, blueish colors). Black horizontal lines divide into four sectors according to shoreline behavior.
Figure 7. Spatiotemporal model of beach width changes. The colors show the displacements of the shoreline landward (erosion represented by reddish colors) or seaward (recovery, blueish colors). Black horizontal lines divide into four sectors according to shoreline behavior.
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Figure 8. Variability of the mean shoreline position on Playa Grande beach (1985–2019) and the highest waves registered. Changes in mean beach width between 1985 and 2019 and their relationship to the moments with more energetic swells (Hs > 3.12 m, i.e., the 95th percentile of the series analyzed).
Figure 8. Variability of the mean shoreline position on Playa Grande beach (1985–2019) and the highest waves registered. Changes in mean beach width between 1985 and 2019 and their relationship to the moments with more energetic swells (Hs > 3.12 m, i.e., the 95th percentile of the series analyzed).
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Figure 9. Comparison of the average beach width in sectors 2 and 4. Seasonal beach width variability is much higher (sometimes up to 80 m) in S2 than in S4.
Figure 9. Comparison of the average beach width in sectors 2 and 4. Seasonal beach width variability is much higher (sometimes up to 80 m) in S2 than in S4.
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Figure 10. Seasonal beach width variability in sectors 2 and 4 (in blue and red respectively) during the years 1995–1996 (dashed line) and 2017–2018 (solid line). Both series present the width changes relative to the position at the start of the year.
Figure 10. Seasonal beach width variability in sectors 2 and 4 (in blue and red respectively) during the years 1995–1996 (dashed line) and 2017–2018 (solid line). Both series present the width changes relative to the position at the start of the year.
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Figure 11. Temporal series monthly average wave energy flux.
Figure 11. Temporal series monthly average wave energy flux.
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Figure 12. Conceptual model of the dynamics and sediment redistribution of Playa Grande (Cartagena Bay). The direction and energy of the waves and the orientation of the four beach sectors are considered. The size and direction of the most common waves are shown as those with the highest energy (green, from 233°), medium (yellow, 241°), and lowest (red, 255°). These wave types are translated into longshore transport with different magnitudes (represented by the various lengths of the arrows) in each beach sector.
Figure 12. Conceptual model of the dynamics and sediment redistribution of Playa Grande (Cartagena Bay). The direction and energy of the waves and the orientation of the four beach sectors are considered. The size and direction of the most common waves are shown as those with the highest energy (green, from 233°), medium (yellow, 241°), and lowest (red, 255°). These wave types are translated into longshore transport with different magnitudes (represented by the various lengths of the arrows) in each beach sector.
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Figure 13. Comparison of beach width changes and the Oceanic Niño Index (ONI) is the difference between a three-month average SST over an ocean region from 120W to 170W along the equator and the long-term average for the same three months. Data from the US National Weather Service (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php, accessed on 17 February 2024).
Figure 13. Comparison of beach width changes and the Oceanic Niño Index (ONI) is the difference between a three-month average SST over an ocean region from 120W to 170W along the equator and the long-term average for the same three months. Data from the US National Weather Service (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php, accessed on 17 February 2024).
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Figure 14. Time series of monthly ONI averages in black, significant height in blue, mean period in red, and mean direction in green.
Figure 14. Time series of monthly ONI averages in black, significant height in blue, mean period in red, and mean direction in green.
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Figure 15. The relationship between the mean annual Hs and beach width (both in m) is described by a linear correlation (expressed as R2). To establish the linear correlation, the 2010–2012 data associated with earthquakes (highlighted in green) have been removed.
Figure 15. The relationship between the mean annual Hs and beach width (both in m) is described by a linear correlation (expressed as R2). To establish the linear correlation, the 2010–2012 data associated with earthquakes (highlighted in green) have been removed.
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Table 1. Average annual and winter wave regimes are characterized by the significant wave height (Hs), peak period (Tp), and direction (°).
Table 1. Average annual and winter wave regimes are characterized by the significant wave height (Hs), peak period (Tp), and direction (°).
AnnualWinter
Hs (m)Tp (s)Direction (°)Hs (m)Tp (s)Direction (°)
Mean 2.0213.87241.152.1113.51243.40
Standard dev. 0.602.299.980.732.1012.52
Minimum 0.545.21209.000.545.48209.00
5th Percentile 1.1910.26230.451.1110.20229.60
25th Percentile1.6012.42234.651.5712.20235.25
Median 1.9513.70238.802.0013.51240.55
75th Percentile 2.3615.04244.852.5314.81248.00
95th Percentile 3.12 18.18 259.85 3.4816.81267.40
Maximum 6.26 24.39 327.45 6.2624.39327.45
Table 2. Shoreline change rates for Cartagena Beach for the period 1985–2019.
Table 2. Shoreline change rates for Cartagena Beach for the period 1985–2019.
Average WidthRate Change (m/yr)R2
Sector 126.05−0.850.09
Sector 243.06−0.360.02
Sector 343.44−0.470.04
Sector 488.95+0.730.15
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Briceño de Urbaneja, I.C.; Pardo-Pascual, J.E.; Cabezas-Rabadán, C.; Aguirre, C.; Martínez, C.; Pérez-Martínez, W.; Palomar-Vázquez, J. Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery. Remote Sens. 2024, 16, 2360. https://doi.org/10.3390/rs16132360

AMA Style

Briceño de Urbaneja IC, Pardo-Pascual JE, Cabezas-Rabadán C, Aguirre C, Martínez C, Pérez-Martínez W, Palomar-Vázquez J. Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery. Remote Sensing. 2024; 16(13):2360. https://doi.org/10.3390/rs16132360

Chicago/Turabian Style

Briceño de Urbaneja, Idania C., Josep E. Pardo-Pascual, Carlos Cabezas-Rabadán, Catalina Aguirre, Carolina Martínez, Waldo Pérez-Martínez, and Jesús Palomar-Vázquez. 2024. "Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery" Remote Sensing 16, no. 13: 2360. https://doi.org/10.3390/rs16132360

APA Style

Briceño de Urbaneja, I. C., Pardo-Pascual, J. E., Cabezas-Rabadán, C., Aguirre, C., Martínez, C., Pérez-Martínez, W., & Palomar-Vázquez, J. (2024). Characterization of Multi-Decadal Beach Changes in Cartagena Bay (Valparaíso, Chile) from Satellite Imagery. Remote Sensing, 16(13), 2360. https://doi.org/10.3390/rs16132360

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