Net widening of Southern California beaches

Domain: neuroscience
Published: 2026-01-29
Editorial outcome: major_revision

Abstract
Human impacts from dams reduce river sediment fluxes and are primary causes of coastal erosion worldwide. Here we provide new satellite-derived shoreline observation techniques to examine beach area trends across the diverse coastal settings of California. Contrary to global trends, these data reveal that the most heavily urbanized and dammed region of southern California experienced net beach growth of over 2 million m2 during 1984-2024. While several beaches experienced severe erosion, overall widening is explained by sufficient sediment supply and concentrated widening from longshore transport captured at coastal structures and in littoral convergence zones. These results indicate that adequate sediment sources exist in this human-modified landscape to mitigate coastal erosion, but that this sediment is not effectively distributed to vulnerable beaches. This highlights the critical role that longshore sediment transport plays in long-term beach trends and illuminates management opportunities for coastal sustainability at the regional scale.

Introduction
Beaches of the world’s coasts provide important economic benefits, recreational opportunities, and habitats for ecosystems. Beaches are also dynamic landscapes that change over time in response to waves, sea levels, sediment supplies, geologic setting, and human-built modifications to the coast. The future of coastal landforms, including beaches, will depend on a range of local-to-global scale factors, including sea-level rise and sediment supplies, and the physical processes that move sediment to, along and away from these coastal landforms.

Human impacts from the construction of dams have reduced river sediment discharge to the coast throughout the world, increasing the potential for coastal degradation and shoreline erosion. Land use changes, including the urbanization of coastal watersheds, alter the timing, volume and characteristics, such as grain-size distributions, of sediment discharged to the coast. During the modern era of population increase, land use change, water resource development, sea level rise and changing coastal storms, it is important to develop a better understanding of these human impacts on the sustainability of coastal resources, including beaches.

The highly modified landscape of California provides an ideal study area to evaluate beaches and their response to human impacts, owing to the understanding that the region’s coastal watersheds provide a dominant source of California’s littoral sediment, and to its diversity of coastal settings, including a range of population densities, land use changes, and coastal modifications. The southern portion of California includes the majority of the region’s population, wide-spread urbanization, and large dams on all of its rivers, the latter of which are suggested to have reduced sand flux to the coast to approximately 50% of historical levels. Scientific study of California’s beaches also benefits from almost a century of coastal research in the region that has resulted in fundamental concepts and understanding of coastal morphodynamics and sediment movement through littoral cells. California’s beaches are also valuable natural resources, and the environmental and economic costs associated with the erosion of these beaches would be profound. Recently, it has been reported that most California beaches have transitioned to a state of chronic erosion during the past three-to-five decades and that this erosion will only increase in the future. This transition appears to be consistent with the presumed effects of urbanization and dams on reducing coastal sediment supplies from the region’s watersheds, even though field studies suggest that California’s urban sediment supplies are much more complex than these presumed effects. This presumed erosional episode in southern California followed pervasive beach widening during the mid-20th century when excess sediment from harbor and other construction projects was disposed at the coast. However, these recent findings of a shift to chronic erosion were developed from data with only 2-to-3 historical records of shoreline positions, each of which having large uncertainties. Because these measurements directly influence estimations of the magnitude and costs of sediment nourishment, coastal engineering, and natural and human constructed resource losses related to coastal changes, these findings can influence hundreds of million of US dollars in sediment management costs, billions of dollars of property and infrastructure, and millions of California residents. It is critical, therefore, to ensure that coastal assessments are based on the best available information and integrated with supporting factors that influence shoreline change.

Here, we examine beach changes along the California coast to explore the effects of these human impacts on the status and trends of the region’s numerous sandy beaches. We analyze the rates, patterns and behaviors of shoreline change across California with improved satellite-based measurements and newly developed time-series analyses to provide a reassessment of the trends and patterns in beach area change (refer to Methods). In doing so, we revise the assessments of human impacts on coastal change in the California study area and provide a new data synthesis technique that can be applied broadly to better understand coastal systems throughout the world. Most importantly, we find that the beaches of the southern California region have exhibited net widening—not chronic erosion—over the past four decades, which is the product of persistent widening of several beach segments in areas of net sediment convergence. This sediment convergence occurs in response to an abundance of sediment supply from fluvial sources, longshore transport, direct human management, and the coastal structures, such as harbors, breakwaters and jetties, that interrupt longshore transport near these beaches. These results will likely introduce new opportunities to redefine sand management and redistribution strategies at local, littoral and regional scales to enhance long-term coastal resilience and sustainability.

Results
Over the regional scales of northern, central and southern California, we document strong year-to-year variations in shoreline positions, including erosion during 1995, 1998, 2010 and 2016 and recovery during subsequent years (Fig. 1), patterns that are consistent with field observations. Additionally, there is reasonably strong correlation between the time series of beach areas of northern and central California (r2=0.63), whereas correlations between southern California and the other regions are more moderate (r2=0.18-47; Fig. 1) but still statistically significant. This provides evidence that there are temporally coherent phases of erosion and recovery across California but that the southern California shorelines have been exhibiting somewhat different patterns from those in central and northern California.

One of the key differences between the three regions of California is the rate of change, or trend, in shoreline positions and beach area over time. Notably, there is a highly significant positive trend (linear regression, r2 adj = 0.51, p<0.001) in beach area for southern California, whereas the trend in northern California is moderate to weak (r2 adj = 0.11, p=0.02) and there is no significant trend in central California (r2 adj = -0.02; p=0.56; Fig. 1). The net widening of northern California beaches, although barely decipherable owing to high year-to-year variability, is not unexpected, owing to the introduction of a massive accretionary wave of sediment in the late 1980s and early 1990s resulting in 1.2 million m2 of new subaerial beach in the Klamath littoral cell. This increase in beach area from newly introduced sediment scales with the weak trend in net beach growth across the entire northern California region (+1.2 ± 1.0 million m2 of beach area over the 41-yr record; ±95% c.i.), which provides a link—albeit a statistically weak one—that the integrated changes in beach area may be associated with changes in littoral sediment volumes.

The net widening trend in southern California is much stronger than northern California, and this trend can be approximated using linear regression (±95% c.i.) to be +56,000 ± 17,000 m2/yr, which is equivalent to +2.3 ± 0.7 million m2 of beach area formation over the 41-yr measurement record. These rates of change are equivalent to an average beach widening of 7.2 ± 2.2 m over the 41-yr observations (or 0.18 ± 0.05 m/yr) integrated across the 320 km of southern California beach length evaluated herein (Supplementary Fig. S1). Although there is strong year-to-year variability in the beach area of southern California, the computed linear trends are not sensitive to the initial year used for the time series (Supplementary Table S1). Using back-beach locations measured at the toes of cliffs, dunes or urban development (refer to Methods), we calculate that roughly 23 million m2 of total beach area existed in southern California at the beginning of the satellite-derived shoreline records in 1984. This implies that there was approximately 10% expansion in total beach area within southern California during the 41-yr measurement interval. However, as detailed in the subsequent sections of this paper the rates of shoreline change over scales of littoral cells and beach segments vary considerably—including both positive and negative (i.e., widening and narrowing) trends—and these changes are related to the physical settings and historical context of these sites.

How is net beach widening across southern California reconciled with reports of beach loss and storm damage to infrastructure across the region? And, why are southern California’s beaches widening at a rate greater than northern California considering the latter experienced a massive introduction of new sediment during the late 1980s and 1990s? To address these questions, we examine the beach segment scale and document high variability in shoreline change rates from linear regression, which range between -1.7 and 2.0 m/yr over the 1984–2024 records (Fig. 2). Narrowing (red) and widening (blue) beach segments are distributed across the entire extent of the southern California coast, and narrowing and widening sections generally co-occur within the eight primary littoral cells (Fig. 2). Overall, we find that beach segments with significant widening trends (p<0.05) represent 155 km of the 320 km total shoreline length (49%) assessed in our analyses of southern California, while significant narrowing is found on beach segments that combine to represent 100 km (31%) of the shoreline. No trend (p>0.05) was measured on beach segments totaling 45 km (20%) of the shoreline.

The association between beach widths (measured from the toe of cliffs, dunes or urban development; refer to Methods) and shoreline trends is shown in Fig. 2b, illustrating the general pattern of how the widest beaches of the region have been exhibiting the greatest widening shoreline trends during 1984–2024. The most rapidly widening beaches correspond to the McGrath State Beach, Huntington Beach, and Venice Beach segments, which are labeled i-iii in Fig. 2a respectively, and these three beach segments were all roughly 200 m wide at the end of the 1984–2024 observations (Fig. 2b). Thus, the beach-segment-scale observations provide evidence that there is great diversity in shoreline trends across southern California. Yet, these results also provide evidence that beach widening is more common than narrowing, that the broadest beach segments are widening generally at the most rapid rates, and that the net trend across southern California is not erosional as suggested in other studies with more uncertain data that capture only several historical shoreline postions.

Given the high spatial variability in shoreline trends, we now examine shoreline change across the eight primary littoral cells of the southern California coast. For each littoral cell, we integrate all beach segment measurements to derive measurements of total beach area relative to the initial shoreline positions of our records (Fig. 3, left hand column). We also provide these littoral-cell results in units of mean shoreline position integrated across each littoral cell (Fig. 3, right hand column).

Six of the eight littoral cells exhibit significant (p<0.05) increasing trends in beach area, five of which are highly significant (p<0.001; Fig. 3). The largest increases in beach area are found in the Santa Barbara, Santa Monica and San Pedro littoral cells. Independently, these account for 0.48-to-0.66 million m2 of net beach growth, as determined from linear regressions slopes, over the 1984–2024 records (Fig. 3a, c, d). If considered together, these three cells contribute to an average beach growth rate of 44,000 ± 9000 m2/yr (±95% c.i.), which would account for 1.8 million m2 of new beach area over the 1984–2024 records. Of these littoral cells, the San Pedro has widened most persistently, resulting in a beach growth trend of 16,000 ± 2000 m2/yr, which is equivalent to an average beach widening rate of 0.61 ± 0.07 m/yr (Fig. 3d). This trend is equivalent to 25 ± 3 m of average widening across the San Pedro littoral cell during the 1984-2024 records. The Zuma littoral cell, in contrast, exhibits highly significant narrowing that results in the loss of approximately -0.3 million m2 of beach area; equivalent to a mean integrated shoreline change rate of -0.18 ± 0.07 m/yr (Fig. 3b). The net change of beaches is more modest in the remaining four littoral cells: Laguna, Oceanside, Mission Bay and Silver Strand (Fig. 3e-h). Although the Laguna and Mission Bay littoral cells exhibit highly significant widening trends, these cells encompass relatively short lengths of shoreline and therefore are not major contributors to the overall net beach area expansion across southern California (Fig. 3e, g). In addition to trends, all littoral cells exhibit year-to-year variations in beach area, including both multi-year intervals of beach widening (wide intervals generally occur during 1999–2009 and 2012–2015) and years with punctuated narrowing (such as 1991, 1998, 2010 and 2016; Fig. 3).

The combination of the beach-segment-scale and littoral-scale shoreline trends provides evidence that most of the net widening observed across southern California beaches can be attributed to high rates of widening from a limited number of beach segments within a few of the littoral cells. For example, the most rapidly widening beach segment of southern California is McGrath State Beach in the Santa Barbara littoral cell (labeled ‘i’ in Fig. 2a), where the mean widening trend of 1.97 m/yr (r2 adj = 0.92; Supplementary Fig. S2) for this 6.4-km beach segment explains 0.52 million m2 of beach growth over the measurement records. This amount of beach growth is equivalent to ~80% of the total expansion in beach area integrated across the Santa Barbara littoral cell (~0.66 million m2; Fig. 3a), even though the McGrath State Beach segment represents only 7.0% of the integrated beach length sampled of this cell. Expanding these analyses, we find that the four beach segments with the highest rates of beach widening represent less than 22 km of shoreline, or only 6.8% of the cumulative shoreline length of the study area; however, when considered in unison these segments can account for 65% of the total net widening trend across southern California (i.e., 1.5 of the 2.3 million m2 of new beach area). These four rapidly widening beach segments have steady rates of change over time (all linear r2 adj > 0.9; Supplementary Figs. S2-S4), which implies that the causes of widening for these segments have been regular and persistent over the past 4 decades.

Although the rapidly widening beach segments contribute substantially to the net beach area growth in southern California, narrowing beach segments also exist throughout southern California resulting in erosional impacts to property and natural resources. Examples include most of the Zuma littoral cell and the northernmost section of the Oceanside littoral cell (Fig. 2a), both of which exhibit narrowing beach segments that have caused erosion challenges to these coastal communities.

Shoreline change can be attributed to one or more factors including new sediment supply from river discharge and bluff erosion, sediment management activities (e.g., beach nourishment or sediment bypassing of harbors), net cross-shore exchange of sediment from time-varying wave energy, longshore sediment transport, and/or losses of beach sediment to offshore sediment pathways such as submarine canyons. Here we examine the role of these factors across southern California littoral cells to evaluate the causes of the observed shoreline changes.

We will first examine the effects of sediment budgets, owing to observations that sediment inputs from watersheds and human activities such as beach nourishment can dramatically alter beach widths in California. Our approach is to examine littoral cells with the greatest rates of beach widening, the Santa Barbara, Santa Monica and San Pedro littoral cells, by accounting for river inputs, nourishment projects, harbor bypassing and the net littoral volume change estimated from observed trends in shoreline position (refer to Methods; Table 1; Fig. 4). The available data are incomplete for these littoral cells, especially with respect to sediment introduced by river discharge or cliff erosion and lost offshore through submarine canyons. Thus, we focus on regions with constrainable sediment budgets that surround the most rapid widening beach segments (denoted with dashed lines in Fig. 4a-c) to evaluate if shoreline changes may be related to measurable changes in littoral sediment volumes.

Summing sediment inputs and outputs reveals net gains in sediment volume (+0.16 to +0.42 Mm3/yr) for all three sections of coast evaluated for sediment budgets, even under the range of conditions resulting from 2-σ uncertainties (Table 1). This result indicates that these coastal sections have been gaining millions of cubic meters of littoral sediment (+6.6 to +17 Mm3) over the 1984–2024 beach change records assessed here. The source of this sediment surplus varies across these sites: river supply and harbor sediment bypassing in the eastern portion of the Santa Barbara littoral cell, littoral drift in the Santa Monica littoral cell, and a combination of river supply (delivered by channel maintenance) and nourishment in the San Pedro littoral cell (Fig. 4a-c).

We compare these results of gaining sediment budgets with estimates of the measured volumetric changes across these beaches derived from our shoreline change measurements. Without repeated topographic and bathymetric survey data over the 1984–2024 measurements, we are limited to approximating these volumetric changes with simple assumptions about the beach profile shape, the integrated thickness of the active beach profile, and total uncertainty related to a worst-case scenario (refer to Methods). Using these approximations, we estimate that +0.14 to +0.21 Mm3/yr (±50% uncertainty) of new littoral sediment was incorporated into these beach sections from the shoreline change measurements (Table 1). Comparisons of the sediment budget and beach volume estimates provide evidence that the persistently widening beach segments of southern California are all areas with persistent increases in the coastal sediment volumes (Table 1). However, we should not expect the volumetric change estimates to compare perfectly owing to a number of factors, including but not limited to the possibility that the mean beach profiles may have changed over time, the potential for offshore sediment losses from the littoral cells, variations in sediment bulk density and grain-size distributions, and the potential for documentation errors and omissions during nourishment projects. In fact, the three coastal sections considered in detail here have widened at rates that are lower than expected from the sediment budgets, which may be indicative of offshore losses or submarine storage of sediment that was not captured by shoreline position measurements. Nevertheless, these budgets show that the supplies of sediment from rivers, littoral drift, and nourishment projects scale with the measured rates of shoreline change, providing evidence that seaward beach expansion has been occurring where millions of cubic meters of sediment have been accumulating along the coast (Table 1).

Attention now turns to longshore sediment transport, the primary mechanism by which sediment is distributed along the length of littoral cells and a fundamental characteristic of the littoral cells of southern California. We acquired estimates of wave height, period and direction at the 10 m isobath every 100 m along the coast from the CDIP-MOP nowcasting system, which were used to estimate the direction and magnitude of sediment transport using the CERC equation (refer to Methods). The predominant transport directions are presented in Figs. 4a-c and 5a-b, with arrows showing segments of the coastline where the longshore transport is either unidirectional or bidirectional. Bidirectional transport stems primarily from seasonal changes in the prevailing wave direction, with more westerly wave energy prominent in winter and spring and more southern wave energy prominent in summer and fall, with an overall effect of diffusive sediment transport. Moreover, whether any section of the coast experiences unidirectional or bidirectional transport further depends on the orientation of the coast, sheltering of wave action by offshore islands, and coastal bathymetry.

Littoral transport directions reveal that many of the rapidly widening beaches are areas of net sediment convergence along the littoral cells. For example, convergence in the littoral sediment transport is computed for the Huntington Beach segments of the San Pedro littoral cell, and this region of convergence corresponds with the most rapid rates of beach widening (Fig. 4c). Within the Santa Barbara and Santa Monica littoral cells, we do not compute littoral sediment transport convergence in the regions where the most rapid beach widening occurs (Fig. 4a, b). However, convergence of southward directed sediment transport in these cells is supported with sediment budget considerations. For example, convergence of the southward moving littoral sediment is enhanced at the McGrath State Beach segment of the Santa Barbara littoral cell owing to harbor bypass operations that do not keep up with sediment inputs (Fig. 4a). Similarly, convergence of southward transport of sediment in the Santa Monica littoral cell is likely caused by interruption of the littoral drift by coastal structures including Marina del Rey and the Santa Monica and Venice Breakwaters, which are not adequately captured in our longshore transport computations (Fig. 4b, e). In the end, the littoral sediment flux estimates combined with sediment budgets provide evidence that these widening beach segments have both net gains in sediment volume and net sediment transport convergence, resulting in the addition of millions of cubic meters of sand to these widening segments of beach (Fig. 4).

Lastly, we consider the effects of cross-shore exchange of littoral sediment on the beach trends reported above, because changes in incident wave energy over time can result in time-dependent changes to the shoreline position. Much of the west coast of North America experiences shoreline erosion during intervals of elevated incident wave energy, such as in 1998, 2010 and 2016, with shoreline recovery occurring over post-storm years. To examine whether the trends in beach area across the eight littoral cells of southern California can be explained by time-dependent cross-shore sediment exchange at the annual scale, we utilize mean annual wave power hindcasts from the ERA5 models because these models are the only complete hindcast covering our 1984–2024 measurement records (refer to Methods).

We find evidence that wave power induces significant effects on shoreline positions and integrated beach areas in southern California. For example, there are significant correlations between the annual records of wave power and beach area for most littoral cells of southern California (Table 2). These wave power correlations are strong for the littoral cells with weak to moderate temporal trends and weak for the cells with strong temporal trends. Overall, wave power explains between 0 and 51% (average = 31%) of the year-to-year variations in beach area across these eight littoral cells (Table 2). These correlation results can be compared to the time-dependent trends that explain between 0 and 89% (average = 47%) of the variations in the littoral cell beach area (Table 2). The wave power correlations all have inverse relationships, revealing that years with larger waves generally coincide with smaller beach areas whereas smaller waves coincide with broader beaches, consistent with wave-related cross-shore exchanges of sediment.

Because both time and waves exhibit significant correlations with beach area, we examine the combined effects of these factors using multivariable linear regression, which reveals that both variables generally contribute significantly to explaining beach area across the littoral cells (denoted with [time+waves] in Table 2). For example, over the broader region of southern California the r2 adj values increase from 0.52–0.56 for the single-variable linear regressions to 0.81 for the multivariable regression, and both variables (time and waves) are found to be significant at p<0.05 (Table 2). At the littoral-cell scale, the combination of time and waves explains between 30 and 94% (mean = 65%) of the beach area variability, and these correlations are substantial improvements over the single-variable correlations for most littoral cells (Table 2). Thus, we find that the combination temporal trends and time-varying wave power contribute to explain much of the changes to beach area across southern California.

Yet, we also find that wave power from the ERA5 hindcasts exhibits a weak decreasing trend over the 1984-2024 records (r2 adj = 0.083; p<0.05; Supplementary Fig. S5), so we evaluate whether these trends may explain a portion of the changes in beach area. The effects of waves on overall beach trends can be evaluated by comparing the single-variable temporal trends with the trends computed from multivariable regression. When the effects of waves are included, the temporal trends are found to be slightly lower than the trends from linear regression, although the two temporal trend estimates are not different within 2-σ uncertainty for any of the littoral cells (Table 2). Combined, this suggests that wave-related cross-shore exchanges of sediment are important in year-to-year variations in beach area, causing shoreline erosion during years with large waves and beach recovery during years with small waves, consistent with field and modeling results. However, cross-shore exchanges of sediment cannot explain the significant temporal trends exhibited in seven of the eight littoral cells of southern California, which combined with the sediment budget and longshore transport results highlighted above indicates that the shoreline response can be largely attributed to substantial increases in the littoral sediment volumes.

Methods
For the California study area, there have been regular field-based measurements of beach morphodynamics at a limited number of sites, which if combined represent only 2.5% of the study area’s shoreline. Because of the limited spatial extent of these data and the high variability of shoreline behavior across the study area, we have taken a remote sensing and signal processing approach to capture beach changes along the 1700 km coastline over a four-decade interval of time. To implement this strategy, we use the latest CoastSat satellite-derived shoreline position measurements and a time-series signal processing approach based on the decomposition analyses of Warrick et al. to reduce measurement errors. These remote sensing results are combined with assessments of wave-related shoreline change, longshore transport, and littoral sediment budgets to assess the patterns and causes of coastal change.

Shoreline change analyses utilized measurements from the CoastSat technique, which combines Landsat and Sentinel-2 satellite imagery, machine learning-enhanced radiometric analyses, and tidal models to estimate time-series of shoreline positions at regularly spaced transects. For each available and cloud-free satellite image, waterlines are mapped using radiometric properties of the infrared and optical bands, segmentation of the scene into water, whitewater, sand, and other land covers, an Otsu radiometric threshold for each image to delineate the waterline based on segmentation boundaries, and a global tidal model and local estimate of beach slope to correct the waterline measurements to a mean-sea-level shoreline position. Shoreline data for the California beach segments in our study were derived from a study of shoreline change across the Pacific Ocean with beach transect spacing at 100 m. We utilized the most recent release of these shoreline data at the time of our data analyses (version 1.5), which included updates in transect beach slopes and the length of record (1984–2024).

CoastSat data were used to compute mean annual shoreline positions and beach area changes for distinct segments of the California littoral cells. Signal processing techniques were used to produce regular interval time-series of shoreline positions, reduce the uncertainty in satellite-derived shoreline positions, and characterize the patterns, behaviors, and causes of beach change. The analysis approach was based on the newly developed time-series decomposition analyses of Warrick et al., which utilizes the seasonal-trend decomposition with locally estimated scatter plot smoothing (LOESS) (STL) technique and is summarized for two example beach segments in Supplementary Fig. S8. The code used to conduct this analysis is available from Warrick.

Preprocessing of the raw CoastSat shoreline position data included removal of extreme outliers, if they occurred, using a Hampel filter run across 5% subsets of data from each transect and removing all shoreline position values that were greater than 3 standard deviations from the local mean. Monthly mean shoreline positions were then calculated from the preprocessed values for each transect over the range of January 1984 through December 2024. These monthly shoreline position records were demeaned by subtracting average shoreline positions of the entire time series values on a transect-by-transect basis.

The demeaned monthly shoreline positions were then integrated into 329 beach segments of the California coast, 129 of which are in southern California. Each beach segment represents a unique geographical setting along the study area, and the geographic bounds for these beach segments were defined by the locations of features including headlands, breakwaters and groins, harbors, river mouths, piers, changes in shoreline orientation, or changes in shoreline change trends. We excluded all transect segments that included shoreline position irregularities caused by land-cast shadows, offshore rocks, coastal structures such as piers, and the opening and closing of river mouth inlets. These removed transects are consistent with those removed by the seasonal analyses of Warrick et al. Details of the geographic extent of these beach segments, including source CoastSat transect numbers and geographic bounds are tabulated in the data publication associated with this paper. For each beach segment, mean monthly shoreline positions were calculated from the demeaned monthly shoreline positions for all transects within the segment. Examples of monthly shoreline positions from two beach segments are provided in Supplementary Fig. S8a.

For each beach segment, the annual mean shoreline positions were calculated using a decomposition technique that removed seasonal effects in the shoreline position data. This ‘deseasonalizing’ of the monthly records was necessary owing to data gaps in the monthly records, especially during 1984–1999 when entire seasons of the records did not have shoreline measurements (Supplementary Fig. S9). Without corrections for the typical seasonality of each beach segment, the annual mean values or other averaging techniques could be biased if the shoreline data were sourced primarily from the portion of the year during which the beach was wider or narrower than normal.

The deseasonalization process followed the seasonal, trend decomposition with LOESS (STL) techniques proposed by Cleveland et al. and implemented by Warrick et al. to measure shoreline seasonal cycles for the California coast. First, a LOESS fit through each monthly shoreline time series was computed to determine the time-dependent trend of each beach segment (blue lines in Supplementary Fig. S8a). This LOESS low-pass fit used the parameterization of Warrick et al., which included a single-pass of the standard second order polynomial fit with a tri-cube weight function and a LOESS bandwidth (or ‘smoothing factor’) of 4 years. The LOESS low-pass was subtracted from each monthly shoreline time-series, resulting in detrended monthly shoreline positions for each beach segment. The regular seasonal cycles of the shoreline within each beach segment were then calculated by averaging all of the detrended shoreline values for each month of the year, following Warrick et al. To ‘deseasonalize’ the original monthly shoreline positions for each beach segment, these computed seasonal cycles were replicated year-after-year and subtracted from the mean monthly shoreline positions. The resulting shoreline records preserved annual to multi-annual trends while minimizing seasonal patterns as shown with example data in Supplementary Fig. S8b.

The final step in calculating mean annual shoreline positions for each beach segment was to average the deseasonalized shoreline positions over each ‘wave year’, which was defined as November 1 to October 31 (for example, wave year 2014 is equivalent to November 1, 2013 to October 31, 2014). The standard deviations of the available shoreline positions for each wave year were also computed from the monthly deseasonalized records, and the total 2-σ uncertainty for each wave year was calculated as the quadrature sum of 1.97 multiplied by the computed annual standard deviation values and the mean 2-σ measurement uncertainties at the annual scale, which was computed to be 5.8 meters as noted below. Lastly, for presentation and comparison purposes, the mean shoreline position of the first three years (1984–1986) of each beach segment was subtracted from the mean annual shoreline position records, so that each shoreline record tracked changes from the initial state of the time series (Supplementary Fig. S8c).

Shoreline changes within each beach segment were computed and presented in both units of change in shoreline position (m) and change in beach area (m2). Areal changes were computed by multiplying the shoreline position change by the beach length, the latter of which was calculated by the product of the number of transects in the beach segment and the transect spacing distance (100 m). Rates of change for each beach segment and integrated collections of beach segments were computed using linear regression. When integrating multiple beach segments into a regional or littoral-cell scale measurement of shoreline change, the 2-σ uncertainties for these beach sections were computed using addition in quadrature because each mean annual shoreline position at the segment scale is independent. Adjusted correlation coefficients (r2 adj) were used to describe linear regression to provide better comparisons with results when different numbers of predictors are used. Significances of the linear regressions were assessed with p-values, including characterization of regressions as highly significant (p<0.001), significant (p<0.05), and not significant (p>0.05).

For computations of absolute beach width and beach width change from the mean annual shoreline positions, we utilized the mapped back-beach locations derived and published for each CoastSat transect by Warrick and Buscombe. These locations were based on the geospatial locations of the seaward most occurrence of a cliff toe, dune toe, vegetation line, or an urban landscape feature (e.g., road, building, riprap, seawall).

The uncertainty between the satellite-derived shoreline positions and field-surveys of shoreline positions was evaluated to compute total uncertainty in the mean annual shoreline positions. For these comparisons, we used field-surveys of southern California beaches by the Coastal Processes Group at Scripps Institution of Oceanography and published in Ludka et al. We combined these field data to match beach segments used to characterize the CoastSat data, which resulted in three beach segments, Cardiff Beach, Solana Beach, and southern Torrey Pines represented by field-study sections C1-C2, S1-S3, and T2-T5, respectively, from the Ludka et al. data. Mean monthly shoreline values from the field surveys were available for many, but not all, of the time series interval, which were 8–15 yr long (Supplementary Fig. S10).

For each of these three beach segments, the mean annual shoreline positions were computed using the STL techniques described above for both the satellite-derived shorelines and the field-derived shorelines. A time-series comparison of the satellite- and field-derived mean annual shorelines is provided in Supplementary Fig. S11. One-to-one comparisons between these segment-averaged mean annual shorelines reveal that the RMSE between the data ranged between 1.9 and 3.4 m (average = 2.9 m; Supplementary Fig. S12). These comparisons also revealed that the linear regression slopes between the data ranged between 0.96 and 1.37 (average = 1.18). Only the linear regression slope from Cardiff Beach was significantly different than unity using 95% confidence intervals; the linear slopes for Solana Beach and Torrey Pines were not significantly different than one. Thus, there is a possibility for a subtle bias in the satellite-derived shoreline positions, such that they overestimate the total shoreline change exhibited on the beach, but there is also the possibility that this result occurred because the field-survey records do not include complete monthly records from which the comparisons were made (Supplementary Fig. S10). In the end, we utilize the mean estimate of 5.8 m for the mean 2-σ shoreline measurement error, which is several-times to an order-of-magnitude smaller than traditional shoreline mapping using historical maps and orthoimagery that have been used for previous assessments of California shoreline change.

We evaluate the influence of waves on mean annual shoreline positions within the southern California and its eight littoral cells by comparing annual shoreline measurements and total cumulative wave power estimates. These comparisons required wave data from 1984 to 2024, which limited the available data to coarse-resolution hindcast data from the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5). A severe limitation of the use of the ERA5 data is that they do not include wave refraction and shadowing effects caused by the complex southern California topography and bathymetry. For these reasons, we only make comparisons at the regional and littoral-cell scales and not at the local or beach-segment scales.

Total cumulative annual wave power was computed from hourly ERA5 hindcasts of significant wave heights (H) and dominant wave period (T) for offshore sites offshore of the southern California littoral cells, and locations of these sites are shown in Supplementary Fig. S5b. Because these sites are offshore of the continental shelf, we used the deep-water equations to compute wave power (P) from each hourly wave value:

P = \frac{\rho g^2}{64\pi} H^2 T (1)

where \rho is the seawater density (1025 kg/m3) and g is the gravitational constant (9.81 m/s2). To generate total cumulative annual wave power (PT), we integrated the computed values of P over each wave year, resulting in PT values in units of kW-hr/m. We find that hindcast estimates of wave power throughout southern California were strongly correlated to each other (r2>0.95) and different by simple linear scaling factors (Supplementary Fig. S5a). Thus, for correlation analyses with the beach area records (summarized below), we found negligible differences in results from the different ERA5 hindcast locations. Given these findings, we computed and used a mean wave power record from the three ERA5 records evaluated herein to represent the Southern California wave conditions. Although this technique does not incorporate the diversity in wave conditions that occur throughout the southern California region, we do not apply these ERA5 wave data to the local beach segment scales; rather, we apply the ERA5 wave data to bulk integrated measurements across the eight littoral cells and the entire southern California region.

The effects of PT on integrated net beach area of the southern California littoral cells were assessed with linear regressions with annual integrated beach area, and adjusted r2 values for these comparisons are reported in Table 2. To evaluate the potential for beach area to be influenced by a combination of temporal trends and the effects of waves, we conducted multivariable linear regressions with both measurement year and annual PT values. We report both the adjusted r2 and the temporal trend values from these regressions in Table 2.

Longshore transport processes were evaluated across southern California’s littoral cells based on the potential sediment transport (Q) and a derivative function, the littoral Peclet Number (Pe) as introduced by Kahl et al. The Peclet number provides a non-dimensional indicator of the relative strength and direction of sediment transport along the coast. We investigated the spatial evolution of the Peclet number to provide insight to the directionality and variability of longshore transport processes on a sub-littoral scale.

Potential sediment transport represents the volumetric flow rate of sand that is feasible given the available wave energy and was estimated using the CERC equation:

Q = K \gamma \sqrt{g} H_b^{2.5} \sin(2\theta_b) (2)

where Q is the transport potential, K is an empirical coefficient, g is the gravitational acceleration constant, H is the significant wave height, and \theta_b is the relative angle to the coast of waves at breaking, computed as the difference between shore normal direction and bulk wave direction (\theta_b = \theta_shore - \theta_bulk). Sediment and fluid characteristics were represented by:

\gamma = \frac{\sqrt{2\rho_w}}{16(\rho_s - \rho_w)\phi} (3)

where \rho_w and \rho_s represent water and sediment density, respectively, and \phi represents sediment porosity. Standard values were utilized for each variable as follows: K=0.39, g=9.8 m/s2, \rho_w=1024 kg/m3, \rho_s=2650 kg/m3, and \phi=0.6. Significant wave height and bulk wave direction values were extracted from CDIP-MOP hindcasts on an hourly timescale for all relevant transects; shore normal angle is provided from CDIP-MOP as a constant for each transect. Beginning at the start of the dataset availability in 2000, time series were downloaded from the CDIP THREDDS server via a data-scraping script developed in R. Potential transport rate was then estimated for each hour and averaged across the 24-year time-scale yielding a Summer (April–September), Winter (October–March), and annual mean rate (m3/year). The annual mean and annual standard deviation values were used to compute the littoral Peclet Number, Pe, representing the ratio of advective to diffusive transport processes for each coastal transect. Shoreline locations where Pe is greater than or equal to 0.5 or less than or equal to -0.5 are defined to be advection dominated, with transport predominantly in the upcoast or downcoast directions, respectively. Conversely, a Pe between -0.5 and 0.5 points to diffusive (i.e., bidirectional) dominated zones with transport in both the upcoast and downcoast directions. The resulting potential transport values and Peclet numbers shown in Supplementary Fig. S13 were used to inform the location, direction, and strength of longshore sediment transport, indicated with yellow arrows across Figs. 4 and 5.

Average annual rates of beach nourishment (Mm3/year) were calculated over the full study period (1984-2024) based on nourishment volumes and project sites recorded in the American Shore and Beach Preservation Association (ASBPA) database along with supplementary information from a range of open-source records summarized in Supplementary Table S2. Project sites generally corresponded to coastal municipalities, and municipal-scale nourishment rates were summed to arrive at totals for each littoral cell of the region. Additionally, nourishment volume known to be taken from river outlets or moved across harbors was classified as “bypassing.” Figs. 4 and 5 use arrows to identify nourishments associated with bypassing, river maintenance, and conventional placement. Summation of these rates of sediment movement for the purpose of the beach-segment-scale sediment budgets is detailed below.

Sediment budgets were generated for segments of the southern California coast with the greatest beach widening and narrowing rates using available records of river sediment discharge, dredging, beach nourishment, and littoral sediment transport. The sediment budgets were derived for the same interval of time as the shoreline records (1984-2024). River sediment discharge in southern California is inherently ephemeral, and we used data from Barnard and Warrick for the Santa Clara River, Warrick and Rubin for the Santa Ana River, and Patsch and Griggs for the creek and rivers of the Oceanside littoral cell. We used the bluff erosion rates summarized for the Oceanside littoral cell by Patsch and Griggs. Harbor dredging and bypassing and beach nourishment from river maintenance, construction projects, and offshore borrow sites were derived from the mean annual rates computed from the ASBPA National Beach Nourishment Database that are described in detail in the previous section. Lastly, littoral sediment transport into Santa Monica Bay was derived from the assessments of engineering reports summarized by Patch and Griggs. Beach-segment-scale sediment budgets are summarized in Table 1 for three portions of the southern California coast as denoted by dashed lines in Fig. 4. We used summation of the ASBPA data provided in Supplementary Table S1 for the presentation in Table 1 and Figs. 4 and 5. For the McGrath State Beach segment in the Santa Barbara littoral cell (Fig. 4a), the net change from harbor bypassing and dredging was computed by the difference between the sediment input at Ventura Harbor and the sum of the output for Port Hueneme and Channel Islands Harbor (e.g., 0.34 – (0.14 + 0.49) = 0.29 Mm3/yr). For the Santa Monica littoral cell sediment budget (Fig. 4b), the dredging of Marina del Rey (0.06 Mm3/yr) was included as a sediment output from the northern beaches, but the remaining nourishment projects were not included in the sediment budget because they were conducted outside of the sediment budget study area. For the San Pedro littoral cell (Fig. 4c), the Surfside-Sunset, Huntington Beach and Bolsa Chica nourishment rates were combined (0.14+0.21+0.013 = 0.36 Mm3/yr) because these projects are conducted at or near the northwestern end of the study area. The Seal Beach, Long Beach and San Pedro nourishment rates were excluded, because these beach segments are separated from the area of interest from harbor breakwaters. Lastly, the Newport Beach nourishment projects (0.06 Mm3/yr) have been derived from maintenance of sand deposited in the Santa Ana River to reduce the impacts of channel sedimentation. Although there was not a formal sediment budget constructed for the Oceanside cell, we present a summation of the ASBPA data in Fig. 5 that includes dredging from Dana Point Harbor from the Capistrano Beach rate (0.0044 Mm3/yr), total beach nourishment in the erosional segments of the northern littoral cell as the sum of San Clemente, Doheny Beach, San Clemente Pier and North Beach nourishments (0.0003+0.0024+0.0038+0.0010 = 0.0075 Mm3/yr), and sediment bypassing around Oceanside Harbor as the Oceanside nourishment rate (0.18 Mm3/yr).

Uncertainties in the sediment budget elements were assessed at the 2-σ level. Accumulation of uncertainty from multiple sediment budget elements was computed using the quadratic sum of the uncertainty elements, because the uncertainty elements are independent. For river sediment discharge, we used 33% uncertainty for 2-σ uncertainty, considering the sediment mass balance of Barnard and Warrick. The ASBPA National Beach Nourishment Database does not include uncertainty assessments, so we used a conservative value of 30% for 2-σ uncertainty to incorporate the potential for incomplete or erroneous project records and the potential for changes in bulk density between the source, delivered, and native littoral sediment. Calculated rates of littoral sediment transport into northern Santa Monica Bay were reported to be 0.145, 0.206 and 0.306 Mm3/yr by Patsch and Griggs, so we use the mean and two times the standard deviation of 0.22 ± 0.14 Mm3/yr. This rate of sand input is consistent with sediment supplies from erosion in the watersheds and along the sea cliffs of the Santa Monica Mountains, which would feed this littoral sediment transport.

The sediment budgets were compared with the volumetric change estimates of beach segments. These volumetric changes followed the calculations of Warrick et al., for which the overall beach profile is assumed to remain constant over the decadal time scales provided by the satellite-derived shoreline data. Under these conditions, the volumetric change is equivalent to the product of the mean rate of shoreline change (m/yr), the beach segment length (m), and the total beach thickness (m), which is equivalent to the height over which sediment is spread between the depth of closure and the upper beach surface. We use a total beach thickness estimate of 12 m for southern California, which approximates the mean conditions of 8-to-10 m depth of closure and 2-to-4 m of beach elevation. Uncertainty in these volumetric change calculations was assessed by considering a worst-case beach profile change, the largest sediment addition event from southern California’s largest sediment source: the winter 2005 events at the Santa Clara River. This event discharged approximately 5 million m3 of littoral grade sediment to the coast and dramatically altered the topography and bathymetry of beach profiles near the river mouth, including a massive alteration of the submarine longitudinal profile from the deposition of a 4–10 m thick submarine bar.

Under this condition with a highly unsteady beach profile, we find that our satellite-derived shorelines recorded an average of 66.3 m of shoreline widening from this event over the integrated 3.3 km beach segment length. Using the assumption of a 12 m thickness of the beach, we compute 2.6 million m3 of net volumetric gain in littoral sediment from the satellite-records, which is 52% of the actual amount (i.e., 48% error). This estimated volume is less than the actual measured volume owing to the abundance of sediment in the submarine portion of the profile, a bathymetric feature that the shoreline position measurements are unable to capture. Although our simple estimate is off by ~50%, we note that the submarine bar of sediment was eroded over the subsequent 5 yr, resulting in a beach profile that returned to its original shape, consistent with the assumptions of our calculations. Regardless, even under the event-response conditions highlighted above, which capture a worst-case scenario during our shoreline position records, we find that our volume estimate is within 50% of the actual value. Using this example and the understanding that most of our shoreline data is derived from beaches without these types of morphodynamics, we have included ±50% total uncertainty for the shoreline-derived littoral sediment volumes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Net Widening of Southern California Beaches\nWarrick et al\nThe authors describe a new method to analyze a 41yr satellite-derived shoreline time series of shoreline position estimates\nalong the CA coast. The new method breaks the mobile sand-gravel portions of the the CA coastline into 330 segments of\nvarying lengths delineated by natural and human coastal barriers that can block or significantly hinder longshore transport.\nSeparating CA into 3 regions: south, central, and north CA using Pt Conception and SF Bay as regional boundaries. They\nfind that the regional average shoreline change in southern CA (129 segments) is distinct from the other regions, with a\nstatistically significant positive trend in beach width. Their segmented method reveals that roughly 2/3 of the southern CA\nregional average beach widening is owing to just 4 outlier (in a good way) segments, representing just 7% of the total length\nof southern CA’s sandy coast. These segments have widened dramatically owing to a combination of beach nourishments\nand longshore drift retention by coastline barriers. More generally, they link the wide range of different segment changes with\nlocal deepwater and nearshore wave hindcast-driven cross-shore and longshore transport, historical nourishment and\nbypassing records, and sand budget estimates.\nThe paper is thorough, well written, and contains valuable new information for coastal scientists, engineers, managers and\npolicymakers. I recommend publication but ask the authors to consider the following comments and possible revisions.\n1. The title of the paper\nThe compact title leaves a first impression that southern CA beach widening, i.e. from an increase in overall beach volume\n(vs. volume-conserving beach rotations), has been widely distributed across the region, rather than primarily the result of a\nsmall number of rapidly widening segments. Given the more outlier-driven nature of the net volume increase-driven net\nwidening, and the sand management importance of the longshore sand redistribution that has occurred thoughout the\nregion, perhaps a title like,\n“Concentrated Beach Widening and Widespread Sand Redistribution in Southern California”\nwould speak more to the 2 important conclusions of the paper?\n2. Beach areas\nThe paper favors a less common \"beach area\" metric over the more common use of mean beach width of mean shoreline\nposition change, preumably because this is explicity conserved when integrating over variable length new-method-segment\nresults. Nevertheless, because area and width are simply related by dArea = 100*Ntransects * dWidth , as you state in the\npaper, and I don’t know what the heck a 2M m^2 change in socal beach area really means for starters, it would be helpful if\nyou gave both area and width quantities throughout in the text (eg. the beach width change in brackets next to the area or, to\nmy taste, vice versa), and in the figures where possible (eg. as new right y-axes labels in Fig 3).\n3. Total beach area\nI may have missed it, but because you have back beach locations for all the CoastSat transects, and the latest version of\nCoastSat has better geospatial referencing, I presume you can guesstimate the total area and true mean beach width of\nsocal beaches? If so, you could also present that number and your area change numbers as a percent change of the total\nbeach area, and actual mean beach width as well? Those would be interesting percentages to put the millions of square\nmeters of change into context.\n4. Segment math\nThe upside of segments is that they really get the reader to appreciate the length scales you have to pay attention to (short!)\neven when thinking about long-term beach behavior. A downside of using variable length shoreline segments as the basis\nof a regional analysis is the loss of explicit longshore shoreline change conservation with somewhat subjectively-driven\nalongshore averaging variabilty. Some of the segments are tiny, some are pretty long, so when you say things like ‘X% of the\nsegments widened’ it has no real quantitative meaning as far as I can tell? I would avoid non-quantitative segment stats if\nyou can, unless I am missing something. I worry about the little guy - the small segments loosing their collective regional\nvoice in the analysis. Do they add up to a significant part of the 320km? Are they preferentially dropped when you filter by\nsegment trend significance in some parts of the analysis?\n5. Time series start and end points\nlines 119-124 state:\nThe net widening trend in southern California is much stronger than northern California, and it can be approximated using\nlinear regression (±95% c.i.) to be +56,000 ± 17,000 m2/yr, which is equivalent to +2.3 ± 0.7 million m2 of beach area formation\nover the 41-yr measurement record. An endpoint approach provides similar results, for example, by comparing the first and\nlast three years of the 41-year time series (i.e., 1984-1986 and 2022-2024), which results in an additional 2.25 million m2 of\nnew beach\narea across southern California.\n--\nI would avoid manipulating/multiyear averaging the ends of the time series if you can. It implies you think the annual values\nhave too much uncertainty to be interpreted individually, like their uncertainty is worse than the regression thinks, but then\ntreat regression p values < 0.05 as gospel.\nGiven that the southern CA-wide average annual beach area or beach width (ie. Figure 3i) can vary significantly on 4-5 year\nENSO timescales and beyond, the “end point approach” result seems highly dependent on when your time series starts and\nends? If you had written this paper in 2013-2015 (the max area year), then your end point beach area would have been\nfantastic. If your time series ended shortly after the 2016 El Nino, not so much. The trend vs end point result seems like a\ncoincidence?\nBeing a devil’s advocate with the end point game, imagine if the CoastSat time series had happened to started at the 1990\npeak in Fig 3i (Landsat 1 launch delayed further!). We would be saying there has been no hint of a trend in the satellite\nrecord. I think that is why some smart people invented statistics - to keep us from playing end point games to feed our own\nnatural biases. The real take home message is there has not been a statistically detectable LOSS of regionally averaged\nbeach width since 1984, as many fools had assumed there would be in the PreVossian Era (my hand is raised…seems like\nthat was the Stone Age now).\nOn the same uncertain end point topic, in Fig 3 you calculate the cumulative change relative to a 1984-1986 mean starting\npoint. That will “lift up” the plots a little because CoastSat shows a regionally-averaged retreating shoreline in the first couple\nyears, so you’ve suppressed the first dip a bit yes? Are any of your results truly sensitive to whether you do that our not?\nAs you state clearly in the paper, the story has mostly been an amazing run of sand redistribution on O(few beach segment)\nspace scales + 4 outlier positive segments thanks to us humans.\n6. Trend averaging\nlines 145-148 state:\nSumming these beach segment trends using weighting by shoreline length produces a mean shoreline change rate of +0.17\nm/yr, which is equivalent to +6.9m of average beach widening over the integrated 320-km study area during the 41-yr\nrecords.\n--\nIt is my understanding that you cannot legally add regression trends together, even with the segment length weighting.\n(regress(X)+regress(Y))/2 is not generally equal to regress((X+Y)/2)? I’m unsure throughout the paper where and when you\nare integrating trends and when you are integrating shoreline segment time series before calculating a trend. The latter is\ncertainly safer than the former even if they turn out to be very close in the end sometimes with correlated data?\n7. Segment analysis\nI did my best to understand how you derive a single annual shoreline position time series for each coastal segment.\nProbably no set of supplemental plots can easily convey what the methodology is up to all the time. So be it, if it makes\nthings better. Are you able to show that it buys you something worthwhile for annual time series estimates over just data\nanalysis 101 annual (Nov-Oct) time averaging of transects across a segment with cursory QC of the raw CoastSat time\nseries (eg. matlab rmoutliers followed by linear interpolation gap filling)? At the larger littoral cell or southern CA averaging\nlevels, if you calculate a regional mean time series from your new method segment single time series, and then the\nequivalent using all the original CoastSat transects reduced to the simple annual means, are they very different?\nIf answering this would require unreasonable additional work to answer, please just say so and disregard. I am not\nquestioning the motivation for, or the veracity of, the new method. CoastSat errors on small time-space scales is a thing, but\nsimpler annual averages start to quickly tame it when validated with surveys, so it would be useful to know in what situations\nand/or time-space scales the \"loss of a simple sense of how annual-spatial means are being derived for a section of\ncoastline\" is worth the improved accuracy of a more bespoke data-taming methodology, but that can wait for another time.\n8. Littoral Cell and All southern CA time series\nAlong with a righthand shoreline position change Y-axis, could you add a second color shading to Fig 3 that is just the 4\noutlier segments in their appropriate littoral cells and then all in 3i? It would support your narrative that the 4 segments are a\nbig part of the cumulative change.\n9. Longshore Transport\nLines 564-579 state:\nPotential sediment transport represents the volumetric flow rate of sand that is\nfeasible given the available wave energy and is estimated using the CERC9 equation:\n(cid:1) = (cid:2)(cid:3)√(cid:4)(cid:5)^5/2 (cid:6)(cid:7)(cid:8)(2(cid:9)(cid:10)) [3]\nwhere Q is the transport potential, K is an empirical coefficient, g is the gravitational\nacceleration constant, H is the significant wave height, and θb is the approach angle of\nwaves relative to the shore normal direction, (θshore normal - θpeak).\n--\nI suggest that you scrap θpeak (the mean wave direction at the peak period) and then scrape the CDIP-MOP “Bulk Sxy\nangle” (waveDm) from the netcdf files on the CDIP THREDDS server. θpeak is biased towards swell directions in socal and\nhas skewed your transport results. CERC longshore E_flux is closely related to Sxy_total= E_total*sin(2*(Dnormal-\nwaveDm)). waveDm is much more consistent with what CERC Eq 3 (cid:9)(cid:10) wants to be. Given his publication history on\nlongshore transport, I believe Dr. Vitousek will agree with this recommendation.", "reviewer_2": "The manuscript titled ‘Net Widening of Southern California Beaches’ is well written and presented and I found the science behind it rather solid. However, even though I have no major concerns about the quality of the research, I cannot recommend it for publication in NCOMMS. The reason is that I do not find the work presented to be relevant to the scope of a high IF journal like NCOMMS.\nThe manuscript discusses shoreline change dynamics in Southern California and tries to link the observations with possible drivers of change. That makes it a very interesting piece of work for a journal like Marine Geology, Geomorphology, Ocean and Coastal Management, JGR (among others), but not NCOMMS. The reason is that even though the work is solid, it lucks any feature that would make it stand out from the several other good papers out there. The spatial extent is quite limited, there are no new insights or knowledge presented that would be relevant to a broader audience (beyond SoCal stakeholders and a sub group of the coastal research community). The drivers of change are known, the methods used also rather standard and experienced colleagues could interpret the observations in similar fashion without most of the interesting and solid work done. Finally, the interpretation of the shoreline change is not so detailed and most of the important knowledge gaps remain open: what are the real drivers of the changes? Which temporal scales are more responsible for the overall trend; storms or long term effects? What is the relative importance of cross- vs long-shore transport, vs sediment sources and sinks?\nSome more detailed comments on the methods:\n- The authors imply that they estimate beach volumetric change by considering a constant beach profile in time and if I understood correctly also in space. But this assumption is rather weak in systems that undergo a constant change. There, mean profiles can change due to the overall trend, but also due the introduction of different sediments from beach nourishment. Finally, human interventions like spreading the sediment can make the beach wider without any volumetric change involved.\n- The authors seem to largely neglect cross-shore change which is important for the sediment budget.", "reviewer_3": "I would like to comment the authors on their open approach to quantification of\nuncertainty in SDS and SDS-derived metrics. The authors apply a novel approach to\nSDS time series that minimizes the effects of sparse and irregular data, a common\nissue with satellite-based studies. They also perform a detailed and thorough\nestimation of possible sediment inputs/outputs to the Southern California littoral\nsystems (within the limitation of available data) from different sources including\nbeach nourishments, dredging, river inputs, and sediment transport and\nredistribution between cells.\nResults highlight major gaps and challenges in the understanding and management\nof coastal systems and provide evidence of the opportunity to improve coastal\nmanagement and develop more economically and environmentally effective coastal\nmanagement solutions. Furthermore, although their approach relies on the\navailability of extensive and detailed datasets that may not be broadly available, the\nmethodology followed and discussion provide the framework for assessing and\noptimizing coastal sediment management practices that can lead to more resilient\ncoasts, more sustainable management interventions, and better use of public funds.\nThis paper is well structured and well written, the methods are clear and replicable,\nand the figures produced are of very high quality and informative. This work is\ntherefore highly relevant and suitable for publication with only minor editing.\nIntroduction\nRV1. Referring to the statement: “… based on the decomposition analyses of\nWarrick et al.31 to reduce the effects of measurement errors, which are\ngenerally 10-15 m (1-σ) for synoptic observations32” : Although the\nbenchmarking paper reports accuracies in the order of 10-15m RMSE (except\nfor one site), several studies have shown that these uncertainties are potentially\nhigher, depending on coastal setting and latitude/geographical locations.\nAlthough this may not be the case for Southern California, this statement\nimplies that SDS accuracy is 10-15 m in all environments around the globe. I\ntherefore feel that a statement recognising this fact is warranted, but could be\nas simple as, for example: “… for synoptic observations in similar\nenvironments” or soothing to that effect.\nResults\nRV2. Southern California Littoral cells: The names of the different cells do appear in\nthe Fig2 caption, but which cell is which is not evident to readers unfamiliar with\nthe region. I was wondering if there is a way clarifying which section is which in\nthe text without hindering the flow of this section?\nRV3. “The combination of time and waves explains between 30 and 94% (mean =\n65%) of the beach area variability at the littoral cell scale (Table 2).”. Earlier,\nyou provide a value for the % variability wave power explains over the entire\nregion (0 -51%; mean = 31%). I suggest also providing the equivalent for time\nalone here to enable the direct comparison between the three.\nMethods\nRV4. Referring to the statement: “segmentation of the scene into water, whitewater,\nand sand land cover”: I believe strictly speaking, segmentation is into water,\nwhitewater, sand, and other land cover.\nRV5. “…we find negligible differences in results from the different ERA5 hindcast\nlocations.”: I appreciate you recognise that the ERA5 wave data used may not\nrepresent local wave conditions well. Do you have some understanding of\nwhere your sediment transport results based on ERA5 may be over(under)\nestimating transport rates? Where might any such differences be important if at\nall?\nRV6. You make a commendable effort to account for uncertainty associated with the\ndifferent elements of this work. However, the way uncertainty in volumetric\nchange is calculated (2-σ uncertainty in SDS trend*[beach thickness =3.0m],\nonly accounts for uncertainty in shoreline change while introducing a new\nmostly arbitrary parameter. Changes in the beach profile whether interannual or\nconsistent trends are not accounted for. Though I appreciate some level of\nsubjective/informed decisions may be necessary in broadscale approaches and\ndo not necessarily compromise the validity or value of results, some\nquantification of the variability and long-term trends in beach profiles and how\nthese might influence results would be useful.\nDiscussion\nRV7. “With the continued supply of sediment to southern California littoral cells from\nrivers and bluff erosion21,39,41,51, continued exposure of the coastal to\nchanging wave”. Should it be coast instead of coastal?\nFigures\nRV8. Fig4: I assume the dashed white line in 1-c represent the location for panels d-\nf? If yes, could you please include a statement in he caption? If not, please\nindicate location of d-f on a-c.\nRV9. Fig3: please indicate location of d-f on a-c.\nSupplementary material\nRV10. Table S1: all values in the right hand column are NaNs…\nRV11. Table S2: caption: “based on the ASBPA Beach Nourishment Database14 and\nadditional insights from local records15–19.” ?"}, "author_response": "REVIEWER COMMENTS\nReviewer #1\nI would like to comment the authors on their open approach to quantification of\nuncertainty in SDS and SDS-derived metrics. The authors apply a novel approach to\nSDS time series that minimizes the effects of sparse and irregular data, a common\nissue with satellite-based studies. They also perform a detailed and thorough\nestimation of possible sediment inputs/outputs to the Southern California littoral\nsystems (within the limitation of available data) from different sources including\nbeach nourishments, dredging, river inputs, and sediment transport and\nredistribution between cells.\nThank you for recognizing the unique approach to our data analyses. We are\nencouraged that one of the novel elements of this paper, and one of the reasons it\nmay provide broad interest to the readers of Nature Communications, is this\nnovel data analysis approach. As noted more fully below we have updated the\nMethods section to provide specific details of our analysis techniques, so they can\nbe easily transferred to other sites throughout the world.\nResults highlight major gaps and challenges in the understanding and management of\ncoastal systems and provide evidence of the opportunity to improve coastal\nmanagement and develop more economically and environmentally effective coastal\nmanagement solutions. Furthermore, although their approach relies on the\navailability of extensive and detailed datasets that may not be broadly available, the\nmethodology followed and discussion provide the framework for assessing and\noptimizing coastal sediment management practices that can lead to more resilient\ncoasts, more sustainable management interventions, and better use of public funds.\nThis paper is well structured and well written, the methods are clear and replicable,\nand the figures produced are of very high quality and informative. This work is\ntherefore highly relevant and suitable for publication with only minor editing.\nThank you for these positive words.\nIntroduction\nRV1. Referring to the statement: “… based on the decomposition analyses of Warrick et\nal.31 to reduce the effects of measurement errors, which are generally 10-15 m (1-σ) for\nsynoptic observations32” : Although the benchmarking paper reports accuracies in the\norder of 10-15m RMSE (except for one site), several studies have shown that these\nuncertainties are potentially higher, depending on coastal setting and\nlatitude/geographical locations. Although this may not be the case for Southern\nCalifornia, this statement implies that SDS accuracy is 10-15 m in all environments\naround the globe. I therefore feel that a statement recognising this fact is warranted,\n1\nbut could be as simple as, for example: “… for synoptic observations in similar\nenvironments” or soothing to that effect.\nThe reviewer is correct that there are sites for which SDS measurement\nuncertainties are greater than 10-15 m. For example, beaches with offshore bars\nthat are exposed subaerially during lower tidal stages can have 10s of meters of\nshoreline position error. That noted, there are also locations, such as simple\nsteep beaches, that have errors lower than the range we provided. Combined, this\nis one of the reasons that the original text used the phrase, “which are generally\n10-15 m.” That noted, we have revised our statement to include a qualification\nfor beach type as suggested. This should help with the understanding and\napplication of these results.\nResults\nRV2. Southern California Littoral cells: The names of the different cells do appear in\nthe Fig2 caption, but which cell is which is not evident to readers unfamiliar with the\nregion. I was wondering if there is a way clarifying which section is which in the text\nwithout hindering the flow of this section?\nYes, great suggestion. We would like the littoral cells to be as clear as possible for\nthe readers. We attempted to add the cell names to the Fig. 2, but the text would\nnot fit in the space provided. Thus, we continue to number the cells in the figure\nand include the littoral cell names in the figure caption. Additionally, we have\nadded littoral cell names and the cell numbers from Fig. 2 to Figure 3. We have\nalso added the cell numbers to Figures 4 and 5. We hope that this will help\n‘connect the dots’ between the cell numbers and names for readers.\nRV3. “The combination of time and waves explains between 30 and 94% (mean = 65%)\nof the beach area variability at the littoral cell scale (Table 2).”. Earlier, you provide a\nvalue for the % variability wave power explains over the entire region (0 -51%; mean\n= 31%). I suggest also providing the equivalent for time alone here to enable the direct\ncomparison between the three.\nYes, excellent suggestion. We have revised the manuscript to include a complete\nset of the correlation analysis results.\nMethods\nRV4. Referring to the statement: “segmentation of the scene into water, whitewater,\nand sand land cover”: I believe strictly speaking, segmentation is into water,\nwhitewater, sand, and other land cover.\nreading of this section.\nRV5. “…we find negligible differences in results from the different ERA5 hindcast\nlocations.”: I appreciate you recognise that the ERA5 wave data used may not\nrepresent local wave conditions well. Do you have some understanding of where your\nsediment transport results based on ERA5 may be over(under) estimating transport\nrates? Where might any such differences be important if at all?\nThis is an excellent question about the limitations of the ERA5 wave hindcast\ndata. General understanding of the wave fields in the Southern California Bight is\nthat they are influenced strongly by wave direction and topographic/bathymetric\ncomplexities throughout the region[6,7]. The ERA5 data do not incorporate these\nspatial complexities, but they were the only available dataset to compare with our\n1984-2024 shoreline records.\nThe inadequacy of the ERA5 hindcast data to provide local-scale wave conditions\nwas the primary reason that we did not use the beach segment data for our\nstatistical comparisons. Rather, we used the integrated littoral cell and total\nintegrated southern California region as comparative areas. Although we did\ncommunicate this adequately (given the reviewer’s comment, see above), we did\nnot effectively communicate the limitations of these comparisons. We have\nrevised the presentation of this data source to include limitations, which include\nthe effects of shoreline orientation and offshore topography and bathymetry on\nthe relative levels and year-to-year variations of wave power delivered to the\nintegrated shoreline of the littoral cells.\nHowever, a complete quantitative analysis of these over(under) estimating\npatterns would be a unique research project that is far beyond the scope of this\npaper and would require investigations that include mode integration, calibration\nand validation, similar to the work that was required to develop the 2000-2025\nCDIP wave hindcasts from other data sources by O’Reilly and others[7].\nThus, we have made the presentation clearer about the potential uncertainties in\nthe available data, and we highlight our assumptions that the southern California\nlittoral cells may include wave power variabilities that are caused by the region’s\ngeography.\nRV6. You make a commendable effort to account for uncertainty associated with the\ndifferent elements of this work. However, the way uncertainty in volumetric change is\ncalculated (2-σ uncertainty in SDS trend*[beach thickness =3.0m], only accounts for\nuncertainty in shoreline change while introducing a new mostly arbitrary parameter.\nChanges in the beach profile whether interannual or consistent trends are not\n3\naccounted for. Though I appreciate some level of subjective/informed decisions may be\nnecessary in broadscale approaches and do not necessarily compromise the validity or\nvalue of results, some quantification of the variability and long-term trends in beach\nprofiles and how these might influence results would be useful.\nThank you for these suggestions and concerns. First, we acknowledge that the\ngreatest changes to beach profiles will be over the seasonal scales and have\ncharacterized these patterns for California in recent papers[8,9]. Because of the\nhigh variability at seasonal scales, the focus for the manuscript in question was\ntime scales over which these seasonal effects would be minimized.\nThe annual-scale data that were generated for our manuscript allow for a\ncomparative perspective over annually integrated time scales. An annually\nintegrated beach profile would eliminate seasonal variabilities, which are known\nto be statistically significant for at least 90% of California’s beaches[8]. That is,\nwe should expect that the year-to-year mean annual beach profiles (if they were\nconsistently measured for a beach) would have more uniform shapes compared\nto the more variable seasonal profiles (such as bar evolution over the ‘summer’\nvs. ‘winter’ profiles). This is the underlying basis for the first-order assumption\nthat the beach profile shapes are approximately similar over the integrated\nannual increments of time used in the volume change estimates[10]. Clearly, this\nassumption has limitations because beach profile shapes are known to evolve\nover multi-year increments of time, especially in the evolution of submarine\nbars[11,12].\nIn light of this, we revised our assessment of uncertainty for the volumetric\ncalculations by including a data analysis of a southern California location with the\ngreatest known multi-year beach profile evolution. As detailed in the response to\nthe Editor above, we show that this worst-case scenario from real beach profile\ndata results in at most 50% error in our volume estimates. [The worst case is a\nmassive sediment deposition event from the largest sediment supply in southern\nCalifornia, the Santa Clara River, during the largest recorded event that resulted\nin a massive alteration in the profile in the form of a submarine bar – these\nconditions are highly unusual and have not been met at any other California site\nduring our 1984-2024 records[4,5]]. Thus, we report these calculations in the\nrevised manuscript and revise the total uncertainty of our estimates to this 50%\nlevel, even though the actual uncertainty is likely much lower for most beach\nsegments (i.e., annual-scale profile shape changes are likely considerably lower\nthan the massive changes to the river mouth sediment bar highlighted in our\nworst-case scenario).\nLastly, we include further characterization and justification of the parameters\nused for the volume estimates in the revised manuscript, and we provide new\ndescriptions if not simple warnings in the manuscript (as noted in the response to\nthe Editor) about the weakness of these estimates. For example, we provide\n4\ninformation and citations about the use of 12 m for the typical range of beach\nthicknesses (i.e., subaerial beach heights of 2-4 m plus depth of closure estimates\nof 8-10 m, results in total vertical thickness of the active beach of ~12 m). That\nnoted, the resulting littoral sediment volume estimates from the shoreline\npositions are useful for order-of-magnitude comparisons with other sediment\nbudget calculations, and as such they serve as only one (albeit a weak one) of\nseveral lines of evidence for our conclusions.\nDiscussion\nRV7. “With the continued supply of sediment to southern California littoral cells from\nrivers and bluff erosion21,39,41,51, continued exposure of the coastal to changing\nwave”. Should it be coast instead of coastal?\nYes, thank you. Changes made.\nFigures\nRV8. Fig4: I assume the dashed white line in 1-c represent the location for panels d-f?\nIf yes, could you please include a statement in the caption? If not, please indicate\nlocation of d-f on a-c.\nThank you for catching this. We described the white dashed lines in the text of\nthe manuscript, but not the figure caption. This is fixed. The lines show the\ngeographic extent of the sediment budget calculations.\nRV9. Fig3: please indicate location of d-f on a-c.\nWe believe the reviewer is referring to Fig. 4, not Fig. 3. If so, these locations are\nadded with letters on the seaward sides of the beaches.\nSupplementary material\nRV10. Table S1: all values in the right hand column are NaNs…\nThank you for identifying this potential problem. However, all the northern and\ncentral California values were NaNs, the southern California values were 1-8\n(denoting the littoral cells; bottom of the table). That said, these data have been\nmoved into the published USGS Data Product, which can be found here:\nhttps://doi.org/10.5066/P139YRVP. Because this new data publication captures\nall of the data in Table S1, the table was removed from the Supplemental\nMaterials.\nadditional insights from local records15–19.” ?\nYes, thank you. Corrections made.\nThe authors describe a new method to analyze a 41yr satellite-derived shoreline time\nseries of shoreline position estimates along the CA coast. The new method breaks the\nmobile sand-gravel portions of the the CA coastline into 330 segments of varying\nlengths delineated by natural and human coastal barriers that can block or\nsignificantly hinder longshore transport. Separating CA into 3 regions: south, central,\nand north CA using Pt Conception and SF Bay as regional boundaries. They find that\nthe regional average shoreline change in southern CA (129 segments) is distinct from\nthe other regions, with a statistically significant positive trend in beach width. Their\nsegmented method reveals that roughly 2/3 of the southern CA regional average beach\nwidening is owing to just 4 outlier (in a good way) segments, representing just 7% of\nthe total length of southern CA’s sandy coast. These segments have widened\ndramatically owing to a combination of beach nourishments and longshore drift\nretention by coastline barriers. More generally, they link the wide range of different\nsegment changes with local deepwater and nearshore wave hindcast-driven cross-\nshore and longshore transport, historical nourishment and bypassing records, and\nsand budget estimates.\nThe paper is thorough, well written, and contains valuable new information for\ncoastal scientists, engineers, managers and policymakers. I recommend publication\nbut ask the authors to consider the following comments and possible revisions.\nThank you.\n1. The title of the paper. The compact title leaves a first impression that southern CA\nbeach widening, i.e. from an increase in overall beach volume (vs. volume-conserving\nbeach rotations), has been widely distributed across the region, rather than primarily\nthe result of a small number of rapidly widening segments. Given the more outlier-\ndriven nature of the net volume increase-driven net widening, and the sand\nmanagement importance of the longshore sand redistribution that has occurred\nthoughout the region, perhaps a title like, “Concentrated Beach Widening and\nWidespread Sand Redistribution in Southern California” would speak more to the 2\nimportant conclusions of the paper?\nThank you for this suggestion. We appreciate the reviewer’s concern about being\na clear as possible in the title. Although we tried to develop a better title, co-\nauthor consensus was that the existing title was both compact and thoroughly\ncomplete. The key to our decision was the use of “net” in the existing title:\n“Net Beach Widening of Southern California Beaches”\nBecause it is both the first word of the title (giving it prominence) and the\ndefining concept of the work (an integration across the beaches of the study\narea).\n7\n2. Beach areas. The paper favors a less common \"beach area\" metric over the more\ncommon use of mean beach width of mean shoreline position change, preumably\nbecause this is explicity conserved when integrating over variable length new-method-\nsegment results. Nevertheless, because area and width are simply related by dArea =\n100*Ntransects * dWidth , as you state in the paper, and I don’t know what the heck a\n2M m^2 change in socal beach area really means for starters, it would be helpful if\nyou gave both area and width quantities throughout in the text (eg. the beach width\nchange in brackets next to the area or, to my taste, vice versa), and in the figures\nwhere possible (eg. as new right y-axes labels in Fig 3).\nYes, we agree that the use of area (sq. meters) is somewhat unconventional in\ncoastal studies. One way to calibrate your eye to these numbers is by using a\nbeach towel conversion factor (~2 sq. meters), which would suggest that the 2\nmillion sq. meters of new beach area has increased total potential beach usage by\n~ 1 million sunbathers. Similar conversion factors could be used for plover\nnesting sites or other ecological factors using their areal footprints.\nThe strength of using beach area is the ability to combine results across beach\nsegments and littoral cells with simple addition. That is, if Littoral Cell A\nexpanded by 500,000 sq.m. and Cell B expanded by 200,000 sq.m., then there is\na total of 700,000 sq.m. of new beach area in the combined cells. Units of beach\nwidth expansion across these two cells cannot be combined by simple addition,\nbecause they need to be weighted by the beach length of each cell. Thus, there is\na simplicity in the use of beach area, if you can transition toward this type of unit.\nTo help readers with the area units, however, we have revised the results to\ninclude both area and average beach width (i.e., length) metrics as suggested by\nthe reviewer. Figures 1 and 3 have been replicated in the Supplemental Materials\nbut converted into units of average length. These added figures are noted in the\nfigure captions and the main text. Additionally, we have revised the text by\nincluding several more conversions of the areal change results into average beach\nwidth changes. These are found through the Results section. We hope this makes\nthe results easier to understand and compare with other studies.\n3. Total beach area. I may have missed it, but because you have back beach locations\nfor all the CoastSat transects, and the latest version of CoastSat has better geospatial\nreferencing, I presume you can guesstimate the total area and true mean beach width\nof socal beaches? If so, you could also present that number and your area change\nnumbers as a percent change of the total beach area, and actual mean beach width as\nwell? Those would be interesting percentages to put the millions of square meters of\nchange into context.\ncalculations and results to the text as suggested. In southern California there was\n~23 million sq. m of beach in 1984, and our measurements suggest that this area\nexpanded by ~10% (or 2.3 million sq.m) over the pending 41 years. This addition\nto the results should help readers understand the magnitude of the coastal\nresponses.\n4. Segment math. The upside of segments is that they really get the reader to\nappreciate the length scales you have to pay attention to (short!) even when thinking\nabout long-term beach behavior. A downside of using variable length shoreline\nsegments as the basis of a regional analysis is the loss of explicit longshore shoreline\nchange conservation with somewhat subjectively-driven alongshore averaging\nvariabilty. Some of the segments are tiny, some are pretty long, so when you say\nthings like ‘X% of the segments widened’ it has no real quantitative meaning as far as I\ncan tell? I would avoid non-quantitative segment stats if you can, unless I am missing\nsomething. I worry about the little guy - the small segments loosing their collective\nregional voice in the analysis. Do they add up to a significant part of the 320km? Are\nthey preferentially dropped when you filter by segment trend significance in some\nparts of the analysis?\nAnother excellent suggestion. We agree that any presentation of the number or\npercentage of beach segments may provide misleading information. We have\nrevised the manuscript and removed all discussion of the number or percentages\nof the beach segments. All results are presented in units of the total beach length\n(km). For example, we now state that, “beach segments with significant widening\ntrends (p<0.05) represent 155 km of the 320-km total shoreline length (49%)\nassessed in our analyses of southern California.” Thank you for helping us\nimprove the data presentation.\n5. Time series start and end points\nlines 119-124 state: “The net widening trend in southern California is much stronger\nthan northern California, and it can be approximated using linear regression (±95%\nc.i.) to be +56,000 ± 17,000 m2/yr, which is equivalent to +2.3 ± 0.7 million m2 of\nbeach area formation over the 41-yr measurement record. An endpoint approach\nprovides similar results, for example, by comparing the first and last three years of the\n41-year time series (i.e., 1984-1986 and 2022-2024), which results in an additional\n2.25 million m2 of new beach area across southern California.”\nI would avoid manipulating/multiyear averaging the ends of the time series if you\ncan. It implies you think the annual values have too much uncertainty to be interpreted\nindividually, like their uncertainty is worse than the regression thinks, but then treat\nregression p values < 0.05 as gospel.\nmanuscript and removed all endpoint approaches to calculation of the trends. All\ntrends are based on linear regressions and the related statistics.\nGiven that the southern CA-wide average annual beach area or beach width (ie. Figure\n3i) can vary significantly on 4-5 year ENSO timescales and beyond, the “end point\napproach” result seems highly dependent on when your time series starts and ends? If\nyou had written this paper in 2013-2015 (the max area year), then your end point\nbeach area would have been fantastic. If your time series ended shortly after the 2016\nEl Nino, not so much. The trend vs end point result seems like a coincidence?\nYes, good point. This was another important reason that we removed all of the\nendpoint trend analyses and results from the revised paper. Thank you for\nhelping us see that these endpoint calculations were a weakness of the data\nanalyses.\nBeing a devil’s advocate with the end point game, imagine if the CoastSat time series\nhad happened to started at the 1990 peak in Fig 3i (Landsat 1 launch delayed further!).\nWe would be saying there has been no hint of a trend in the satellite record. I think that\nis why some smart people invented statistics - to keep us from playing end point games\nto feed our own natural biases. The real take home message is there has not been a\nstatistically detectable LOSS of regionally averaged beach width since 1984, as many\nfools had assumed there would be in the PreVossian Era (my hand is raised…seems\nlike that was the Stone Age now).\nAs the reviewer properly notes, trend results will be highly dependent on the time\nintervals evaluated. In fact, one can find statistically significant intervals of\nwidening and narrowing in the integrated southern California data, if short\nincrements of time are considered. For example, the 2005-2010 interval is one\nwith statistically significant beach narrowing for southern California, whereas\n2010-2015 is significantly widening (see Fig. 3i). This is one of the reasons that\nwe used linear regression on the entire data set for trend computations. We note\nthat these time series likely did not start on a low point, because some of the\nstorms of record were during the 1982-’83 winter, whereas our data start in mid-\n1984, an interval of time for which we see significant recovery of beach width\n(e.g., compare with other storm-recovery intervals, such as 1998-1999, 2010-2011\nand 2016-2017; Fig. 3i).\nWe wanted to explore the worst-case scenarios that the reviewer provides (e.g., a\nstart date during a peak beach wide point like 1990). Southern California beach\narea trends continue to be positive (i.e., widening) and statistically significant in\nthis case. For the 1990 start date, the trend (57,100 m^2/yr) is slightly greater\nthan the trend for the entire data (56,300 m^2/yr), although the difference is not\n10\nsignificant at the 95% c.i. (btw… more on start date effects in the next response\nnote). Thus, we agree that the statistical techniques are the most valuable in\nevaluating changes in these shoreline data, largely owing to the metrics of\ngoodness of fit and uncertainty that can help readers understand how to interpret\nthe underlying data. And these results reinforce the removal of the endpoint\ntrend calculations from the manuscript.\nOn the same uncertain end point topic, in Fig 3 you calculate the cumulative change\nrelative to a 1984-1986 mean starting point. That will “lift up” the plots a little because\nCoastSat shows a regionally-averaged retreating shoreline in the first couple years, so\nyou’ve suppressed the first dip a bit yes? Are any of your results truly sensitive to\nwhether you do that our not? As you state clearly in the paper, the story has mostly\nbeen an amazing run of sand redistribution on O(few beach segment) space scales + 4\noutlier positive segments thanks to us humans.\nThis is an important point. Thank you for raising it. Please note that the data are\nplotted with respect to the initial condition (1984-1986) set to zero, which was\ndone to provide a standard and comparative initial point. However, the trends\nare computed with linear regression, for which the y-axis offset does not\ninfluence the resulting slope. That is, the initial condition of the shoreline\npositions could be set to -1000 meters (or +1000 meters), and the linear slope of\nthe time series would be the same. Our calculation of the total change in beach\narea is based wholly on the linear regression slope multiplied by the interval of\ntime (41 years). Thus, initial conditions will not influence that result.\nThe reviewer brings up an important point about the sensitivity to dips (and\nrises) in the data, especially during the initial conditions (late 1980s). We would\nnote that if Landsat 5 had been launched 1 or 2 years earlier, we would have\ncaptured the high erosion winter of 1982-83[13], which would have included a\ndeeper dip in the earliest portion of the record. Thus, we can assume that the\ntrends would have been even higher if our records started in 1982 or 1983 owing\nto a larger potential ‘dip’ effect.\nTo evaluate the potential scale of this effect, we tested the sensitivity of the initial\nyear used for the record and computing linear regression slopes for the southern\nCalifornia data. These results are tabulated here:\nInitial Year Linear Trend Percent of\n(m2/yr) Original Trend\n1984 56,392 100%\n1985 54,978 97.5%\n1986 56,465 100.1%\n1987 55,831 99.0%\n1988 56,157 99.6%\n1989 54,974 97.5%\n11\n1990 57,123 101.3%\n1991 62,589 111.0%\n1992 59,143 104.9%\n1993 55,310 98.1%\nThus, the range of computed trends varies marginally (-2.5% to 11%) with\ndifferent initial years. This provides increased confidence that any perceived dips\nin the initial southern California records have minor (at best) effects on the trend\nresults.\n6. Trend averaging. lines 145-148 state: “Summing these beach segment trends using\nweighting by shoreline length produces a mean shoreline change rate of +0.17 m/yr,\nwhich is equivalent to +6.9m of average beach widening over the integrated 320-km\nstudy area during the 41-yr records.”\nIt is my understanding that you cannot legally add regression trends together, even\nwith the segment length weighting. (regress(X)+regress(Y))/2 is not generally equal to\nregress((X+Y)/2)? I’m unsure throughout the paper where and when you are\nintegrating trends and when you are integrating shoreline segment time series before\ncalculating a trend. The latter is certainly safer than the former even if they turn out to\nbe very close in the end sometimes with correlated data?\nThank you for bringing this up. I hindsight we realize that we did not\ncommunicate our methods adequately, and in response, we have revised the\nMethods section and descriptions in the Results.\nIn the end, we are only providing trends calculated from single time series\nrecords. For littoral cells, we have combined all the representative beach\nsegments into a single time series record for the integrated cell. This time series\nrecord is then analyzed for trends using linear regression. Similarly, an\nevaluation of the combination of multiple littoral cells is provided by first\ndeveloping a single integrated time series of beach area for these cells and then\nanalyzing by linear regression. Thus, the combination of regression trends was\nnot conducted by summation of regression results.\nWe apologize that this was unclear in the previous version of the manuscript.\nIt should be much clearer in the revised manuscript.\n7. Segment analysis. I did my best to understand how you derive a single annual\nshoreline position time series for each coastal segment. Probably no set of\nsupplemental plots can easily convey what the methodology is up to all the time. So be\nit, if it makes things better. Are you able to show that it buys you something\nworthwhile for annual time series estimates over just data analysis 101 annual (Nov-\nCoastSat time series (eg. matlab rmoutliers followed by linear interpolation gap\nfilling)? At the larger littoral cell or southern CA averaging levels, if you calculate a\nregional mean time series from your new method segment single time series, and then the\nequivalent using all the original CoastSat transects reduced to the simple annual\nmeans, are they very different?\nIf answering this would require unreasonable additional work to answer, please just\nsay so and disregard. I am not questioning the motivation for, or the veracity of, the\nnew method. CoastSat errors on small time-space scales is a thing, but simpler annual\naverages start to quickly tame it when validated with surveys, so it would be useful to\nknow in what situations and/or time-space scales the \"loss of a simple sense of how\nannual-spatial means are being derived for a section of coastline\" is worth the\nimproved accuracy of a more bespoke data-taming methodology, but that can wait for\nanother time.\nThis is a very important point. Given the reviewer’s comment about an inability\nto replicate the methods, we have completed updated the Methods section to\ninclude a step-by-step process for how the mean annual shoreline position values\nwere calculated. We believe that this technique should be relatively easy to follow\nnow, but if it is not, please point out how it is unclear. We would like our analysis\ntechnique to be something others can mimic, test, and modify as necessary, and a\nclear description of it is essential.\nRegarding the comparison with other techniques, we do see this as somewhat of a\nbig ask, however, we have provided new information about why we developed our\n‘deseasonalize’ technique. Key elements of the techniques include: (i) the\nreduction in scatter that may be introduced from the seasonality of the shoreline\nposition (highlighted in Supp. Fig. S9) and (ii) the removal of seasonal bias from\nundersampling in the original data (highlighted in Supp. Fig. S10). The reason\nthat this latter effect is important can be seen in the large amount of\nundersampling in the pre-2000 data when only one satellite (Landsat 5) was\ncollecting data. During this era, there was commonly whole seasons without data\ncollection (e.g., the Eureka cell had no winter season data for most years, as did\nmany of the rest of California during years; Supp. Fig. S10). If there was no data\ncollected during the 4-6 months with the narrowest beach conditions, then mean\nannual values would be biased high (wide). Removing the regular seasonal\ncomponent of the time series is a standard method – and the best one we can find\n– for removing this kind of potential bias.\n8. Littoral Cell and All southern CA time series. Along with a righthand shoreline\nposition change Y-axis, could you add a second color shading to Fig 3 that is just the 4\noutlier segments in their appropriate littoral cells and then all in 3i? It would support\nyour narrative that the 4 segments are a big part of the cumulative change.\nfor a relatively busy plot. To help with the communication of the important of the\nrapidly widening beach segments, we have revised the text to include new\ndescriptions and comparisons of the data. We hope that these help the reader\nunderstand the magnitude of these effects.\n9. Longshore Transport. Lines 564-579 state:\n“Potential sediment transport represents the volumetric flow rate of sand that is\nfeasible given the available wave energy and is estimated using the CERC9 equation:\n= √ ^5/2 (2 ) [3]\nwhere Q is the transport potential, K is an empirical coefficient, g is the gravitational\n𝑄acc𝐾e𝛾ler𝑔a𝐻tion con𝑠𝑖s𝑛tan𝜃t,𝑏 H is the significant wave height, and θb is the approach angle of\nwaves relative to the shore normal direction, (θshore normal - θpeak).”\nI suggest that you scrap θpeak (the mean wave direction at the peak period) and then\nscrape the CDIP-MOP “Bulk Sxy angle” (waveDm) from the netcdf files on the CDIP\nTHREDDS server. θpeak is biased towards swell directions in socal and has skewed\nyour transport results. CERC longshore E_flux is closely related to Sxy_total=\nE_total*sin(2*(Dnormal- waveDm)). waveDm is much more consistent with what\nCERC Eq 3 wants to be. Given his publication history on longshore transport, I\nbelieve Dr. Vitousek will agree with this recommendation.\n𝜃𝑏\nAbsolutely. Thank you! Corrections made and Methods, Figures and\nSupplemental Materials have all been updated with the revised calculations. You\nwill note subtle differences in the littoral transport directions.\nThe manuscript titled ‘Net Widening of Southern California Beaches’ is well written\nand presented and I found the science behind it rather solid. However, even though I\nhave no major concerns about the quality of the research, I cannot recommend it for\npublication in NCOMMS. The reason is that I do not find the work presented to be\nrelevant to the scope of a high IF journal like NCOMMS.\nThe manuscript discusses shoreline change dynamics in Southern California and tries\nto link the observations with possible drivers of change. That makes it a very\ninteresting piece of work for a journal like Marine Geology, Geomorphology, Ocean\nand Coastal Management, JGR (among others), but not NCOMMS. The reason is that\neven though the work is solid, it lucks any feature that would make it stand out from\nthe several other good papers out there. The spatial extent is quite limited, there are no\nnew insights or knowledge presented that would be relevant to a broader audience\n(beyond SoCal stakeholders and a sub group of the coastal research community). The\ndrivers of change are known, the methods used also rather standard and experienced\ncolleagues could interpret the observations in similar fashion without most of the\ninteresting and solid work done. Finally, the interpretation of the shoreline change is\nnot so detailed and most of the important knowledge gaps remain open: what are the\nreal drivers of the changes? Which temporal scales are more responsible for the overall\ntrend; storms or long term effects? What is the relative importance of cross- vs long-\nshore transport, vs sediment sources and sinks?\nThank you for your assessments of how this research fits into the broader context\nof coastal science. Although these conclusions differ markedly with Reviewers #1\nand #2, we respect the struggle this reviewer had with fitting a regionally based\nstudy into the subset of science that is globally important. Obviously, there are\nmany ways that this importance could be examined and evaluated.\nWe would suggest that several elements help make our work relevant to a broader\naudience. First, we provide novel analysis tools for evaluating a new a growing\ndata set (satellite-derived shorelines, or SDS), and these tools can be applied\nworldwide. Although the computation and use of SDS has become fairly\nmature[14,15], there have not been significant advances in the analysis tools to\nbetter interpret these SDS data. In fact, several top coastal colleagues continue to\ncomplain openly that SDS data are too noisy and therefore unusable. Our\ntechniques cut through that noise of SDS data using novel signal processing tools,\nresulting in findings that are unique and unexpected.\nSecond, although California is a small region of the world’s coast, it has served as\nan important site in the development of foundational littoral science[16–19].\nThus, it is important to highlight when there is change to this foundational\nunderstanding, which we argue has occurred in our work.\nrelevant to the broader scientific community. We will highlight that a series of\n‘global scale’ studies of shoreline change that have been published in the top tier\nof scientific journals[20–22], because all of these articles have been called into\nserious question by the research community[23–25]. This suggests that although\nthe SDS data have become global in scale, coastal researchers have not developed\nthe appropriate tools to evaluate these data, and the ‘global’ results that have\nbeen published in a series of high impact papers may have either insignificant or\nerroneous conclusions. The point being that the geographic extent of a study\nshould play a secondary control on its relevance. Rigor and novelty should be the\nultimate metric of relevance. For example, some of the most influential and\nrecent geoscience research in high-impact journals has come from small-scale-to-\nregional studies[26–29].\nSome more detailed comments on the methods:\n- The authors imply that they estimate beach volumetric change by considering a\nconstant beach profile in time and if I understood correctly also in space. But this\nassumption is rather weak in systems that undergo a constant change. There, mean\nprofiles can change due to the overall trend, but also due the introduction of different\nsediments from beach nourishment. Finally, human interventions like spreading the\nsediment can make the beach wider without any volumetric change involved.\nThank you for this comment. We have completely revised the presentation,\nuncertainty analysis and use of these computations as noted in the responses to\nthe Editor and Reviewers. Your comments were consistent with the others, so we\nwill not repeat the details of these changes here.\n- The authors seem to largely neglect cross-shore change which is important for the\nsediment budget.\nWe have included a detailed analysis of cross-shore exchange at the annual scale\nacross the littoral cells. We do not need to address seasonal-scale cross-shore\nexchanges – which are both large in the study area and something we have\npublished on recently[8,9] – owing to the annualization of the shoreline position\ndata. That is, our technique for computing mean annual shoreline positions\nremoves these seasonal effects. In the end, we find that waves can explain a\nsignificant amount of the cross-shore exchange of sediment (as represented in\nthe shoreline position[18]), but that these cross-shore exchanges do not explain\nthe underlying time-dependent trends in the data. These results are summarized\nin the statistical results shown in Table 2, which have not been changed in any\nmanner in the revisions here.\n1. Warrick, J.A., and Milliman, J.D. (2003) Hyperpycnal sediment discharge from semiarid\nsouthern California rivers: Implications for coastal sediment budgets. Geology, 31 (9), 781.\n2. Warrick, J.A., and Farnsworth, K.L. (2009) Sources of sediment to the coastal waters of the\nSouthern California Bight, in Earth Science in the Urban Ocean: The Southern California\nContinental Borderland, Geological Society of America.\n3. Inman, D.L., and Jenkins, S.A. (1999) Climate Change and the Episodicity of Sediment Flux of\nSmall California Rivers. The Journal of Geology, 107 (3), 251–270.\n4. Barnard, P.L., and Warrick, J.A. (2010) Dramatic beach and nearshore morphological changes\ndue to extreme flooding at a wave-dominated river mouth. Marine Geology, 271 (1–2), 131–\n148.\n5. Warrick, J.A. (2020) Littoral Sediment From Rivers: Patterns, Rates and Processes of River\nMouth Morphodynamics. Front. Earth Sci., 8, 355.\n6. Adams, P.N., Inman, D.L., and Graham, N.E. (2008) Southern California Deep-Water Wave\nClimate: Characterization and Application to Coastal Processes. Journal of Coastal\nResearch, 244, 1022–1035.\n7. O’Reilly, W.C., Olfe, C.B., Thomas, J., Seymour, R.J., and Guza, R.T. (2016) The California\ncoastal wave monitoring and prediction system. Coastal Engineering, 116, 118–132.\n8. Warrick, J.A., Buscombe, D., Vos, K., Kenyon, H., Ritchie, A.C., Harley, M.D., Janda, C.,\nL’Heureux, J., and Vitousek, S. (2025) Shoreline Seasonality of California’s Beaches. JGR\nEarth Surface, 130 (2), e2024JF007836.\n9. Warrick, J.A., Buscombe, D., Vos, K., Ritchie, A.C., and Battalio, B. (2025) Seasonal rotation of\nCalifornia pocket beaches. Earth Surf Processes Landf, 50 (8), e70115.\n10. Warrick, J.A., Vos, K., Buscombe, D., Ritchie, A.C., and Curtis, J.A. (2023) A Large Sediment\nAccretion Wave Along a Northern California Littoral Cell. JGR Earth Surface, 128 (7),\ne2023JF007135.\n11. Ruggiero, P., Kaminsky, G.M., Gelfenbaum, G., and Cohn, N. (2016) Morphodynamics of\nprograding beaches: A synthesis of seasonal- to century-scale observations of the Columbia\nRiver littoral cell. Marine Geology, 376, 51–68.\n12. Stevens, A.W., Ruggiero, P., Parker, K.A., Vitousek, S., Gelfenbaum, G., and Kaminsky, G.M.\n(2024) Climate controls on longshore sediment transport and coastal morphology adjacent\nto engineered inlets. Coastal Engineering, 194, 104617.\n17\n13. Dingler, J.R., and Reiss, T.E. (2002) Changes to Monterey Bay beaches from the end of the\n1982–83 El Niño through the 1997–98 El Niño. Marine Geology, 181 (1–3), 249–263.\n14. Vos, K., Harley, M.D., Splinter, K.D., Simmons, J.A., and Turner, I.L. (2019) Sub-annual to\nmulti-decadal shoreline variability from publicly available satellite imagery. Coastal\nEngineering, 150, 160–174.\n15. Vitousek, S., Buscombe, D., Vos, K., Barnard, P.L., Ritchie, A.C., and Warrick, J.A. (2023) The\nfuture of coastal monitoring through satellite remote sensing. Camb. prisms Coast. futures,\n1, e10.\n16. Inman, D.L., and Chamberlain, T.K. (1960) Littoral sand budget along the southern California\ncoast. 245–246.\n17. Inman, D.L., and Brush, B.M. (1973) The Coastal Challenge. Science, New Series, 181 (4094),\n20–32.\n18. Yates, M.L., Guza, R.T., and O’Reilly, W.C. (2009) Equilibrium shoreline response:\nObservations and modeling. Journal of Geophysical Research, 114 (C9).\n19. Komar, P.D., and Inman, D.L. (1970) Longshore sand transport on beaches. J. Geophys. Res.,\n75 (30), 5914–5927.\n20. Vousdoukas, M.I., Ranasinghe, R., Mentaschi, L., Plomaritis, T.A., Athanasiou, P., Luijendijk,\nA., and Feyen, L. (2020) Sandy coastlines under threat of erosion. Nat. Clim. Chang., 10 (3),\n260–263.\n21. Almar, R., Boucharel, J., Graffin, M., Abessolo, G.O., Thoumyre, G., Papa, F., Ranasinghe, R.,\nMontano, J., Bergsma, E.W.J., Baba, M.W., and Jin, F.-F. (2023) Influence of El Niño on the\nvariability of global shoreline position. Nat Commun, 14 (1), 3133.\n22. Nienhuis, J.H., Ashton, A.D., Edmonds, D.A., Hoitink, A.J.F., Kettner, A.J., Rowland, J.C., and\nTörnqvist, T.E. (2020) Global-scale human impact on delta morphology has led to net land\narea gain. Nature, 577 (7791), 514–518.\n23. Cooper, J.A.G., Masselink, G., Coco, G., Short, A.D., Castelle, B., Rogers, K., Anthony, E.,\nGreen, A.N., Kelley, J.T., Pilkey, O.H., and Jackson, D.W.T. (2020) Sandy beaches can survive\nsea-level rise. Nat. Clim. Chang., 10 (11), 993–995.\n24. Warrick, J.A., Buscombe, D., Vos, K., Bryan, K.R., Castelle, B., Cooper, J.A.G., Harley, M.D.,\nJackson, D.W.T., Ludka, B.C., Masselink, G., Palmsten, M.L., Ruiz De Alegria-Arzaburu, A.,\nSénéchal, N., Sherwood, C.R., Short, A.D., Sogut, E., Splinter, K.D., Stephenson, W.J.,\nSyvitski, J., and Young, A.P. (2024) Coastal shoreline change assessments at global scales.\nNat Commun, 15 (1), 2316.\n18\n25. Zăinescu, F., Anthony, E., Vespremeanu-Stroe, A., Besset, M., and Tătui, F. (2023) Concerns\nabout data linking delta land gain to human action. Nature, 614 (7947), E20–E25.\n26. Trimble, S.W. (1997) Contribution of stream channel erosion to sediment yield from an\nurbanizing watershed. Science, 278, 1442–1444.\n27. Barksdale, M.B., Hein, C.J., and Kirwan, M.L. (2023) Shoreface erosion counters blue carbon\naccumulation in transgressive barrier-island systems. Nat Commun, 14 (1), 8425.\n28. Ohenhen, L.O., Shirzaei, M., Ojha, C., Sherpa, S.F., and Nicholls, R.J. (2024) Disappearing\ncities on US coasts. Nature, 627 (8002), 108–115.\n29. Vacchi, M., Shaw, T.A., Anthony, E.J., Spada, G., Melini, D., Li, T., Cahill, N., and Horton, B.P.\n(2025) Sea level since the Last Glacial Maximum from the Atlantic coast of Africa. Nat\nCommun, 16 (1), 1486.\nNature Communications\nRE: Nature Communications manuscript NCOMMS-25-54103A\nDear Editors,\nThank you for providing detailed peer reviews and your own assessments of our Nature\nCommunications manuscript, “Net Widening of Southern California Beaches.\"\nYour decision on the manuscript was to invite additional revisions. We are pleased to\nprovide our revised manuscript and this point-by-point response to the review\ncomments.\nIn the materials below, you will find the original review comments in italics font and our\nresponses and manuscript changes in regular font. We look forward to our continued\ncollaboration with you on this review process.\nSincerely,\nJonathan A. Warrick, PhD\nU.S. Geological Survey\nEDITOR:\nThank you again for submitting your revised manuscript \"Net Widening of Southern\nCalifornia Beaches\" to Nature Communications. We have now received reports from\nthe reviewers who evaluated the original version. On the basis of their comments\n(copied below), we have decided to invite an additional revision of your work.\nYou will see that, while the reviewers find that your revisions improved the\nmanuscript, some important points remain to be addressed. We would like the authors\nto emphasise the novelty and contribution of their work, not only in the Abstract, but\nparticularly in the Discussion. With regards to field data, would they have changed the\nresults have the authors considered field data in their study? This is a relevant point\nthat should be addressed in the response letter and within the main text. Please revise\nyour manuscript, addressing all the remaining issues raised by the reviewers.\nThank you for this synthesis and these suggestions. As recommended, we have\nincluded revised Abstract, Introduction and Discussion sections to include more\nemphasis on the novelty of this work. The focus on these revisions is that the\nmost heavily urbanized and dammed section of study area (southern California)\nhas seen the largest gains in beach area. This novelty stems from the fact that\nthese kinds of land use changes generally result in reductions of sediment supply\nand increases in coastal erosion on a global basis, therefore, the southern\nCalifornia region is truly unique. Although these findings were already part of\nthe manuscript, we give them more emphasis in the revised manuscript, which\nshould assist with relating the overall novelty of the work.\nThe request for further consideration of the available field data is also relevant to\na study such as ours, but unfortunately there is a lack of regular (i.e., seasonal)\ncoastal topography of the beaches in the study area. Exceptions include a few\nbeaches in the San Diego region (SIO studies) and Ocean Beach near San\nFrancisco (USGS study). These beaches represent ~2.5% of the California beach\nshoreline length, and the available time series data are only 30-50% as long as\nthe satellite-based records. Additionally, our recent work (Warrick et al., 2025,\nJGR-ES) suggests that these beaches have unique shoreline change patterns\ncompared to the diversity of coastal settings in the study area, so they cannot be\nused for ‘representative’ study sites for California or even their littoral cells. Thus,\nwe use these field data for uncertainty analyses (for which our analysis\ntechniques do extremely well – see Methods section and Supplemental Materials\nFigures). We have made these details clearer in the opening paragraphs of the\nrevised manuscript, where we directly address the availability of field data for\nassessments.\nLastly, as noted below, we have addressed the complete set of reviewer\ncomments. Thank you for this opportunity to resubmit this study to Nature\nCommunications.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have carefully considered and addressed all comments and suggestions\nand have provided detailed responses to reviewers’ comments. The authors have\nrevised their assessment of uncertainty for their volumetric calculations and further\nemphasised the degree of uncertainty for these calculations in the revised manuscript\nproviding a full justification for and explanation of its relevance. They have introduced\nsubstantial revisions, including extensively restructuring of the manuscript. The\nrevised version strengthens the narrative and brings to light the broader implications\nand relevance to sediment management, adaptation strategies and coastal resilience.\nThey have further provided additional detail in the methods section on their shoreline\nanalysis approach which allows for replication of their method.\nOverall, I find this is a manuscript of very high quality that represents a significant\ncontribution in the field. In my view, it is ready for publication, and I am looking\nforward to seeing it published in due course.\nThank you. We look forward to seeing this work published too.\nReviewer #2 (Remarks to the Author):\nI thank the authors for considering my questions and suggestions thoughtfully. I look\nforward to the paper’s publication.\nA few final comments as the authors finalize their manuscript\n1. The title of the paper\n“Concentrated Widening of Southern California Beaches” seems like a better\ndescription of your revised manuscript and it’s primary conclusion: that sand has been\nmostly migrating and widening/narrowing at sub-littoral cell scales owing to multiple\nfactors. While I think this finding of the paper will “age well”, I’m not so sure about the\ncurrent title once the old assumption of net (as in almost-everywhere-net) beach loss\nover the last few decades is behind us. Net widening has a bait-and-switch feel to it\neven by the end of the abstract.\nThe suggestion is to change the term “net” to “concentrated” in the title. While\nboth terms provide accurate descriptions of our findings, their meanings are\nquite different.\nThe adjective ‘net’ may be defined as: “remaining after the deduction of all\ncharges, outlay, or loss” or “excluding all nonessential considerations: basic,\nfinal.” [Merriam-Webster dictionary, 2025]. Please note that this definition is not\nconsistent with the suggested definition of this term, which necessitates all case-\nby-case (or beach-by-beach) results to be consistent for the use of the term ‘net.’\nFor this definition of ‘net’ to be true, every beach would have to show net\nwidening independently before the term could be applied. This situation is not\nonly highly improbable, especially for a large study area such as ours, but it is\nalso inconsistent with the most common usage and definitions of the word ‘net.’\nFor example, if a company shows a “net profit”, this does not mean that all\nunits/projects of the company were profitable. Rather, the term is understood to\nmean that overall the sum of the finances was net positive. A consistent\napplication is used here, the net beach area has increased, even though there is a\ndiversity of beach trends within the study area.\nThe adjective ‘concentrated’ may be defined as: “contained or existing or\nhappening together in a small or narrow space or area.” The challenge with the\nadjective ‘concentrated’ is that it provides no understanding/information about\nthe magnitude of the noun in question (i.e., beach change). Rather, it simply\ndenotes that the change has a limited spatial extent. In contrast, the word ‘net’\nprovides information about the overall summation of change.\nWe contend that the total (or sum of the) change is a more important result than\nthe spatial extent/variability of the changes. To limit reader confusion on our use\nof ‘net’ (which we portend is completely consistent with its definition), please\nnote that the Abstract clearly states that “…several beaches experienced severe\nerosion…” and that “…sediment is not effectively distributed to vulnerable\nbeaches.” These statements provide clear information that our results are in no\nway “almost-everywhere-net” as the reviewer contends. As such, we disagree that\nthere is any hint of “bait-and-switch” in our presentation.\nIn the end, the title will remain because it is accurate and captures the most\nimportant results of our work. We have added a date range to the title, however,\nfor clarity.\n2. beach area vs. beach width , and beach expansion vs beach widening\nIt seems widening is used interchangeably with beach width and beach area, while\nexpansion is used at times but only with beach area. 2D=expansion=area and\n1D=widening=width would be the ideal, but technically area change in this case is\nreally also =widening because the shoreline dimension is fixed. Perhaps it would be\nbest to just stick with widening everywhere?\nThank you for this suggestion. We have given a detailed review and assessment of\nword usage and agree with the reviewer that consistent terms should be used.\nBecause we will cite both beach area and beach width results in the paper, we\nhave revised the paper to use ‘widening’ to represent changes to beach width\nand ‘growth’ to represent changes to beach area.\n3. beach width plots\nThank you for adding in the supplementary section. I’ll make one last lobbying effort\nto have you put the littoral cell beach width plots (Fig S2) in a 2nd column next to the\nbeach area plots in Fig 3. I think they are far more interesting together because the\ncells have such different alongshore scales. eg. the Santa Barbara has a big net beach\narea signal, but a modest mean beach width signal, because it is a big cell.\nThank you for this suggestion. We provided the average beach width data and\nplots in the Supplemental as suggested during the previous round of reviews,\nhowever, there seems to be adequate interest in having these data side by side.\nAlthough these plots are somewhat redundant (the beach area data are divided by\na constant, the total beach length, to result in average beach width), we have\ncreated a double panel figure that has both data sets. This revised figure is\nprovided in the revised manuscript as Fig. 3 and the Supplemental Fig S2 has\nbeen removed.\nminor Supp Fig S2 edit: The caption refers to r_adj correlation coefficients but there\nare none.\nThank you for catching this. This figure was integrated into Fig. 3, so the review\ncomment is no longer applicable to the manuscript. R-adj values are provided in\nFig. 3.\n4. Plotting results relative to a three year 1984-86 mean starting point.\nThanks for explaining this did not influence the trend analysis. Might be good to say\nthat when you describe what you are doing at lines 532-536? eg. “Lastly, for\npresentation and comparison purposes (but not in the trend analysis), the mean\nshoreline position…”.\nIntroducing selective post-analysis interannual time averaging still comes across as a\nlittle under-motivated with the wording you have. And some of those early years have\nabove average ERA5 wave power. If the early annual data is too spotty to make a\nstable starting year all by itself by your standards, why not start everything with the\nfirst year you think is stable? As you note, the overall trend analysis is not very\nsensitive to the starting details.\nWe have revised the introduction to these data to include a statement of the effect\nof start date on trend analyses. This includes a summary of the data analyses\npresented in the previous round of reviews, which shows that start date has a\nnegligible impact on the results and is now included as Supp. Table S1. This\nshould provide the necessary clarification about the sensitivity to start date.\nUnfortunately, the remaining comments are somewhat conflicting. On one hand,\nit is concluded that, “the overall trend analysis is not very sensitive to the starting\ndetails” as we detailed in the previous review. But then, it is suggested that we\nshould “start everything with the first year you think is stable.” In response to\nthis, it could be asked: If it does not matter which year is the first, why should we\nrevise the data presentation and change the first year?\nThis type of revision would require an adequate definition of a “stable year” for\nthe California study area, but the relative stability of the shorelines varies\ngeographically. For example, our findings suggest that there are multiple causes\nof shoreline change in the data (erosion during large storms, multi-year recovery\nbetween storms, large differences in storm exposure across the study area,\nwidening following river sediment input and beach nourishment, longshore\nconvergence of sediment, sediment accretion waves and erosional waves moving\nalong the coast, etc.). These factors result in a wide diversity of time-dependent\npatterns in shoreline change (e.g., refer to the beach segment time series data\nplotted in the Supplemental Materials Fig. S2, S3, S4, S6 and S7).\nThus, it seems justified to use the most complete data available (1984-2024) for\noverall completeness and a revised (and better) description of the implications of\nstart dates in trend results, which is now included in the text and a table in the\nSupplemental Materials.\nReviewer #3 (Remarks to the Author):\nI have carefully read the revised manuscript and the exchanges with the editor and the\nother reviewers. Unfortunately, my overall opinion remains unchanged. While I\nconsider this to be a solid piece of work that would have value if published as a\ntechnical report or in a specialized journal, I do not think it merits publication in\nNature Communications. I recognize that the other reviewers hold different views, and\nI also asked the editor for guidance on how to proceed, but I did not receive a response.\nIdeally, I would have preferred to see the opinion of an additional colleague rather\nthan revisiting the paper myself; nevertheless, I will provide my reply to the authors.\nThank you for providing a summary of this opinion, which as noted differs from\nthe other two reviewers and the editor. A key element of this comment is the\nfinding that the paper does not “merit publication in Nature Communications”\neven though the paper is described to be a “solid piece of work.”\nThese comments are difficult to respond to, largely because there are no specific\ndetails about what would and would not merit publication in Nature\nCommunications. The Nature portfolio does provide some guidance in its Aims\nand Scope statements for each journal, but these details were not addressed.\nThus, we have not been provided with the grounds for which merit was not\nachieved.\nAs detailed above in the response to the Editor, we will note that we modified the\nAbstract, Introduction and Discussion to provide a better description of the\nnovelty and utility of the study. These new materials and emphases should\nprovide a stronger case for the merit of this work.\nNovelty and significance\nA main limitation of the work is that none of its central conclusions appear to be new\nto the community. The paper presents some marginal methodological refinements, but\nnot genuine advances beyond what is already well established. The authors themselves\nacknowledge in their response that “the computation and use of SDS has become fairly\nmature [14,15],” but then argue that their use of signal processing tools yields “unique\nand unexpected findings.” I challenge the authors to clarify what is truly unique here,\nsince the processes they describe are standard material in textbooks on coastal\nprocesses.\nA central comment is that “none of (the) central conclusion appear to be new to\nthe community,” but this statement does not provide specific details about which\nconclusions are being referred to. There are several conclusions of the paper,\nranging from site and regionally specific findings to overall processes that dictate\ncoastal change patterns. It would have been helpful to know which of these\nconclusions were being referred to. There is also no evidence for making this\nstatement about the redundancy of the conclusions. For example, it would have\nbeen helpful to have citations of peer-reviewed papers that have the same/similar\nfindings or redundant results from the study area. With more specific\ninformation, we would have been able to address the comments that we have not\nprovided “genuine advances beyond what is already well established.” That is,\nwithout further information we cannot guess what is meant by “what is already\nestablished.” Perhaps, it is longshore transport? Or, the effects of large wave\nevents? Or, the accumulation of remote sensing measurements to produce net\nbeach area change results in the study area? Or, cumulative coastal change\npatterns and effects in the California study area?\nIn contrast, we contend that we provide new and novel findings. For example, our\npaper provides references from several studies that have concluded the opposite\nfindings of ours (i.e., that southern CA beaches are experiencing “chronic\nerosion”). Out paper shows that there has been net beach widening. We also show\nhow the southern California region contrasts with global-scale understanding\nabout the impacts of dams on rivers. This unique finding occurs for southern\nCalifornia because of the sediment management, coastal development, natural\nsediment supply, and natural longshore transport in this system. Additionally, we\nprovide a new data analysis approach (deseasonalization), which to our\nunderstanding is unique and useful to the coastal sciences. Thus, our conclusion\nis that our paper is substantially different from others and therefore new to the\nexisting literature.\nTo help readers further understand the newness and uniqueness of the work we\nhave included revisions of the Abstract, Introduction, and Discussion to highlight\nthe impacts of dams and urbanization generally across coastal watersheds\nthroughout the world and how the results of this study are contrary to this\ncollective understanding. This is not a new element of the manuscript, because it\nwas highlighted in the Discussion previously, but it is emphasized more\nthoroughly in the revised manuscript to help readers understand additional\nelements of uniqueness of the study.\nIt is also noted that we state that “the computation and use of SDS has become\nfairly mature,” which is used to suggest that the results that we develop could not\nbe new or unique. Two different things are being conflated. The first is\n“computation and use of SDS” and the latter is “new understanding of coastal\nprocesses and patterns.” The maturity of the development and use of SDS data (a\nmeasurement tool) does not preclude insights from these data. On the contrary,\nnew insights are being developed regularly from the fairly mature SDS tools. For\nexample, over the past couple of years and with the use of SDS data, our research\ngroup has discovered:\n-Massive sediment accretion waves related upland watershed land use impacts\n(Warrick et al., 2023, JGR-ES).\n-Spatially variable seasonal shoreline patterns, including great diversity in the\ntiming of seasonal beach narrowing, which is dependent on coastal geography\n(Warrick et al., 2025, JGR-ES)\n-Two dominant and spatially independent patterns in seasonal beach rotation\nalong California (Warrick et al., 2025, ESPL).\n-Net beach widening along the most heavily dammed and urbanized section of\nthe California coast (this paper).\nMany other groups are discovering similar unique coastal patterns and processes\nthrough the use of the recently developed SDS data (e.g., Bishop-Taylor et al.,\n2023; Vitousek et al, 2023; Vos et al., 2023; O’Reilly et al., 2025)\nThus, we would suggest that maturity of the SDS techniques is a prerequisite for\nnew and novel observations of coastal morphodynamics. That is, the\nmeasurement tools need to be mature before new and novel understanding can\nbe built with these tools. This is consistent with a broad set of sciences (medicine,\nclimate, genetics, physics, etc.); for which measurement techniques must be\nmature before novel findings/understanding can be made. There are many\nsources of information about the relationships between measurement tools and\nscientific advancements, perhaps the most approachable would be the 10-episode\nBBC series, “The Day the Universe Changed” or the materials of the Harvard\nUniversity’s Collection of Historical Scientific Instruments\n(https://chsi.harvard.edu/).\nThus, we disagree with the contention that our findings cannot be unique because\nour measurement methods (i.e., SDS) are relatively mature, and we provide\ndetails of how our observations are unique.\nScale and methodology\nRegarding the small- vs. large-scale issue, I fully agree that local studies can be\nimpactful. However, I do not believe this manuscript achieves that. In fact, it appears\nto combine the shortcomings of both scales:\nUnlike strong local studies, the authors do not employ accurate site-specific data.\nInstead, they rely on stylized beach profiles and global ERA-5 reanalysis, rather than\nlocal measurements or regionally calibrated models. This is not far from the\nmethodologies applied by some of the large scale studies the authors seem to be critical\nof.\nFirst, we would like to be clear about large-scale studies: We site many global\nscale studies in our work (such as Vos et al. 2023, Lujiendijk et al., 2018, and\nSyvitski et al. 2007) that have included rigorous analysis and detailed treatment\nof large data sets. These studies are essential to understanding physical\nprocesses, morphodynamics, and the diversity of patterns around the world.\nHowever, we, like many of our coastal colleagues, are highly critical of other\nglobal-scale studies that make conclusions from data poor statistical basis, have\ninadequate sampling resolution, or include incorrect baseline assumptions\n(Cooper et al., 2020, Nature Climate Change; Warrick et al., 2024, Nature\nCommunications). Thus, we appreciate global-scale studies, especially those that\nprovide scientific rigor, uncertainty assessments, and novel synthesis.\nSecond, there is the suggestion that our paper only relied on “stylized beach\nprofiles and global ERA-5 reanalysis” and that it did not include “local\nmeasurements or regionally calibrated models”. This is incorrect. Specifically, we\nused local measurements from regionally calibrated models (the CDIP wave\nreanalysis of waves along the inner continental shelf of California), shoreline\npositions and orientations at 100-m shoreline spacing, a sediment transport\nmodel, and a novel scaling attribute, and the Peclet Number to evaluate the\nimplications of longshore sediment transport patterns. These analyses were\nconducted at the local scale (100-m spacing along the entire southern California\nstudy area). These accurate and site-specific analyses are characterized in the\nMethods, Results, Discussion sections, detailed in the Supplemental Material\nsection, and referred to in the Abstract. These small-scale longshore transport\ncalculations provide critical findings, which is one of the reasons that they are\nhighlighted in the Abstract.\nIn addition to these site-specific measurements, we also provided several other\nlocal to regional analyses, including historical littoral sediment budgets, sediment\nvolumetric calculations, and storm-related change assessments using ERA-5 data\n(the latter two of which are referred to in the review comment, but the techniques\ninclude many other input datasets than beach profiles and ERA5 wave\nassessments). Thus, we would suggest that in combination, we have done much\nmore work and present much more data than “the large-scale studies the authors\nseem to be critical of.”\nInstead, I believe that the authors could have done much more. For a site as important\nas California—indeed recognized as foundational in littoral science—I would expect\nregionally tailored reanalyses and/or field data of waves ocean circulation and,\nideally, repeated surveys (LiDAR, drones, or DGPS) that provide direct topographic\ninformation. These could demonstrate actual changes in beach morphology and\nvolume and allow in depth interpretation. Without such data, reported increases in\nbeach area could equally reflect shoreline accretion, bluff erosion, longshore transport,\nor beach nourishment—each of which carries very different implications for coastal\nprocesses.\nAs noted above, we did much more than characterized in these comments,\nincluding the specific reanalysis and wave analyses asked for. These detailed\nanalyses are important elements of the findings of our work and were (and still\nare) highlighted in the Abstract.\nRegarding repeated field surveys, we utilized the complete set of data available\nfor the study area, which represents only ~2.5% of the total shoreline length.\nAs noted by Warrick et al. (2025) JGR-ES these field study sites are generally not\nrepresentative of the diversity of shoreline settings and time-dependent change\ndynamics of the California coast. Furthermore, these field data represent only 30-\n50% of the time duration available from the satellite records. For these reasons,\nthe field data were used only to verify the satellite data; they could not be relied\nupon for littoral-scale analyses of increases/decreases in beach area.\nSimilarly, there are limited lidar surveys of the complete study area (1998, 2009-\n2011, and 2016). Although these data are useful in computing long-term cliff\nerosion (Swirad and Young, 2022, Geomorphology), they have marked\ndifferences in the season of collection, which results in an inability to apply the\nlidar data to morphodynamic patterns or trends of the beaches. That is, seasonal\nand event-based patterns in the lidar data dominate the signals (for example, the\n2016 lidar survey was designed to capture the extent of large-scale erosion\nimmediately following a year with big storm impacts), resulting in linear trends\ninconsistent with more detailed field or satellite-based observations. Thus, we\nunfortunately do not have an adequate lidar data set for beach morphodynamics\nacross the study area.\nAlso, we provided analyses of the major causes of beach width changes including\naccretion, longshore transport and beach nourishment with independent\nassessments and calculations. This contrasts with the assessment that we were\nunable to identify the causes of beach widening. The paper provided tables,\nfigures and supplemental information that summarized these causes of coastal\nchange. Cliff erosion is not a factor for the widening beaches as these sites are\nlargely along urbanized coastal sections.\nThus, we included (and continue to include) a wide range of data and analyses\nthat were asked for in these comments.\nIn the absence of detailed local information, many of the apparent findings may\nsimply result from the limited size of the study area and the relatively short\nobservation record, which are not sufficient to suppress the influence of outliers. This is\na well-known weakness of small-scale or short-term studies. For example, there is only\none location, driving the overall accreting trend for the whole area. And (as discerned\nby the discussion with the other reviewers), adding one of more decades of observation\ncould potentially result in a different, or even the opposite trend.\nWe will direct readers to our detailed response to Reviewer No. 2 comments in\nthe previous round of reviews (and the brief reference to this work in the\ncomments to Reviewer #2 above). We analyzed these data, and we found that\nmeasurement intervals and other factors were not important in the data and the\ncomputed trends. The spatial concern about “only one location driving the overall\naccreting trend” is inconsistent with the mathematics of summation and\nmeasurements of areas. Each beach segment is a summation of the 100-m spaced\ntransects with the segment, and each littoral cell is the summation of the\nsegments. The southern CA results are a summation of the region’s littoral cells.\nUncertainty of each of these elements and summations are provided at the 2-\nsigma level (data published and available from the USGS as noted in the\nmanuscript), so the influence of a single outlier location would be flagged by high\nlevels of uncertainty, resulting in a summation that was not significantly different\nthan zero. We note that these high levels of uncertainty do not occur in the results\n(e.g., Fig. 1 and 2).\nIn the end, we provide the most comprehensive (spatially and temporary) study\nof the broader California coast and its southern California region to date.\nPrevious studies (e.g., Hapke et al., Amrouni et al., Yates et al., Kahl et al.,\nBarnard et al. (2x), and Ludka et al.,) analyze much shorter/less complete time\nseries over much shorter lengths of coast. Thus, we can conclude that our study\nwas certainly not “small-scale or short-term” when compared to existing studies."}]

Decision letter
Editors
Nature Communications
RE: Nature Communications manuscript NCOMMS-25-54103A
Dear Editors,
Thank you for providing detailed peer reviews and your own assessments of our Nature
Communications manuscript, “Net Widening of Southern California Beaches."
Your decision on the manuscript was to invite additional revisions. We are pleased to
provide our revised manuscript and this point-by-point response to the review
comments.
In the materials below, you will find the original review comments in italics font and our
responses and manuscript changes in regular font. We look forward to our continued
collaboration with you on this review process.
Sincerely,
Jonathan A. Warrick, PhD
U.S. Geological Survey
EDITOR:
Thank you again for submitting your revised manuscript "Net Widening of Southern
California Beaches" to Nature Communications. We have now received reports from
the reviewers who evaluated the original version. On the basis of their comments
(copied below), we have decided to invite an additional revision of your work.
You will see that, while the reviewers find that your revisions improved the
manuscript, some important points remain to be addressed. We would like the authors
to emphasise the novelty and contribution of their work, not only in the Abstract, but
particularly in the Discussion. With regards to field data, would they have changed the
results have the authors considered field data in their study? This is a relevant point
that should be addressed in the response letter and within the main text. Please revise
your manuscript, addressing all the remaining issues raised by the reviewers.