The physical scenario is based on an idealized river mouth following previous works by Nienhuis et al. (2016) and Ruiz-Reina and López-Ruiz (2021). The scenario follows a typical idealized river mouth like those of previous studies (Jiang et al., 2024; Jiménez-Robles and Ortega-Sánchez, 2018; Jiménez-Robles et al., 2016; Matsoukis et al., 2023). This scenario, shown in Fig. 1a, is representative of several real study areas such as the coastal areas surrounding the mouth of the Guadiaro River (Cadiz, Spain) (Roman-Sierra et al., 2008), the Misa River (Senigallia, Italy) (Melito et al., 2020) or the Maipo River (Central Chile) (Roco et al., 2024). The wave climates of these areas are similar to the wave conditions used in this work.

The geometry of the domain is rectangular, with a length of 4500 m on the X axis and 5000 m on the Y axis (Fig. 1a). The river segment modeled is 2500 m long and 200 m wide, featuring a Gaussian cross section (Fig. 1c) with a 5 m depth at the thalweg and a 0.2 % slope. Specifically, the segment serves as a numerical representation of a larger system, where the 2500 m open upstream boundary (explained in Sect. 2.4) is placed beyond the 1000 m tidal excursion limit to prevent tidal wave reflections from influencing the imposed discharge condition. The river reaches the continental shelf at 2 m below mean sea level (houtlet=2 m b.m.s.l.). The beach on both sides of the river also has a slope of 0.2 % up to the point of interaction with the beach face at 3 m a.m.s.l. The geometry of the study area was designed to keep much of the beach dry, so that the berm located at 3 m a.m.s.l. prevents overtopping by moderate swells. From there, the beach face and the continental shelf were defined by a modified Dean profile, as proposed by Falqués and Calvete (2005), with Dean parameter A=0.21, based on a uniform, non-cohesive D50=1 mm. This sediment size is typical of the Spanish Mediterranean coast (Bergillos et al., 2016; Durán et al., 2016) and will be used for the entire topo-bathymetry for simplicity. The shelf reaches a depth of 51 m b.m.s.l. at the offshore boundary where the hydrodynamic boundary conditions are defined (Flow module of Delft3D) and a depth of 258 m b.m.s.l. where the wave boundary conditions are imposed (Wave module). This profile, unlike the classic Dean profile, makes the transition from the continental shelf to the beach smoother, eliminating abrupt changes.

The defined bathymetry was smoothed to produce more realistic experiments (Fig. 1a and b). For this purpose, a first Delft3D real-time simulation was performed with 6 months of medium energy wave climate (from the same data point, Sect. 2.2) and with the same synthetic tidal forcing and flow rate as used in the rest of the experiments (see Sect. 2.2 for further details). This wave climate is characterized by a mean wave height of 1.1 m, a mean peak period of 5.6 s, and two arrival directions at 0 and 22.5° (following the reference of Fig. 1a). At the end, the bathymetry reached a quasi-steady behavior, defined as the basic state, which was then used for the remaining simulations. For more information see Aragón et al. (2023).

The wave data were provided by the MeteOcean Research Group of the University of Genoa (Italy) (Besio et al., 2016; Cassola et al., 2016; Lira-Loarca et al., 2023; Mentaschi et al., 2013, 2015) at a point located 15.2 km off the Mediterranean coast of Cadiz (Spain). The dataset consists of hourly information on the significant wave height (Hs), the peak period (Tp), and the mean incoming wave direction. The wave directions were modified to fit into the orientation of the defined physical scenario, following the system of reference shown in Fig. 1a. The study uses idealized conditions, both morphological and climatic, which are nevertheless based on real-world data and potentially representative of other bimodal wave climates. Given that the wave data are hourly, all results in this study are presented on the same time scale to ensure consistency.

Six different 1-year (from 1 October to 30 September) wave conditions were used for the morphodynamic experiments, using the basic state as the original topo-bathymetry: (1) 1980–1981, (2) 1990–1991, (3) 1999–2000, (4) 2000–2001, (5) 2010–2011 and (6) 2019–2020. While mean Hs values were similar (1.00–1.11 m), the most extreme waves (e.g. Hs 99th) showed greater variability, ranging from 2.89 to 3.76 m (Table 1). This selection focuses on a single representative site to evaluate the methodology's sensitivity to inter-annual variability in wave energy distribution, rather than climatic shifts across different locations. This approach ensures consistency in regional characteristics while maintaining a manageable computational cost for high-resolution event-based analysis.

For the astronomical tidal forcing, a synthetic tide with M2 (1 m amplitude and 180° phase) and S2 (0.25 m amplitude and 90° phase) components was defined. This synthetic tide was based on a semidiurnal tidal regime. The resulting tidal range was selected as it effectively represents a realistic average condition, bridging the gap between a micro-tidal regime and a meso-tidal regime. Consequently, this tidal range condition is applicable to a wide array of worldwide coastal areas (Short, 1991; Whitfield and Elliott, 2012). For the river input, a constant flow rate of 10 m3s-1 was selected based on the ecological and mean flows of Andalusian rivers with similar characteristics. Information related to the mean flow can be found in Ruiz-Reina (2021). The ecological flow data were obtained from Annex V of the hydrological plans for the Tinto, Odiel, and Piedras; Mediterranean Basins; and Guadalete-Barbate basins (Junta de Andalucía, 2023a, b, c), and the Guadalquivir basin (Confederación Hidrográfica del Guadalquivir, 2023).

The hydrodynamic and morphodynamic simulations were conducted using the high-resolution, fluid dynamics Delft3D model (Lesser et al., 2004), which was developed to simulate coupled unsteady flows, sediment transport phenomena, and associated bathymetric changes. The model has been extensively used to analyze river mouth hydro-morphodynamics (Boudet et al., 2017; Edmonds and Slingerland, 2007; Gao et al., 2019; Nardin and Fagherazzi, 2012; Nienhuis et al., 2016). Following the similarity of the study zone to those in other works such as Lamb et al. (2012) and Jiménez-Robles et al. (2016), the effects of wind, Coriolis force, density stratification, and buoyancy (e.g., affecting hyperpycnal or hypopycnal behavior) were neglected as in the aforementioned works. Accordingly, and following similar works (Broaddus et al., 2025; De Goede, 2020; Hopkins et al., 2018; Mariotti and Murshid, 2018; Nienhuis et al., 2016; Xie et al., 2024), depth-averaged simulations were performed to balance computational feasibility with the required accuracy for morphodynamic simulations.

The numerical domain was defined with two rectangular variable grids, as shown in Fig. 1e. The Flow domain is nested within a larger Wave grid. The Flow grid was defined with cell sizes ranging from 22m×22m to 180m×120m, and the Wave grid with cells ranging from 66m×66m to 1300m×730m. This configuration was developed to ensure proper nesting and minimize numerical instabilities. Four open boundaries were considered in the computational grid (Fig. 1a). The astronomical tidal conditions were imposed at the offshore boundary, with the ocean tide and waves propagating from the seaward boundary towards the coast and river mouth. The two cross-shore boundary conditions were defined as Neumann-type, with null alongshore gradient of the water level to avoid numerical inaccuracies (Roelvink and Walstra, 2004). Finally, at the upstream boundary of the river channel, a constant discharge of 10 m3s-1 was used (as explained in Sect. 2.2). Equilibrium sediment concentrations were defined for the transport boundary conditions. The bed friction was defined using a constant Chézy coefficient of 65 in both directions, the horizontal eddy viscosity was defined as 2 m2s-1, and the horizontal eddy diffusivity was defined as 10 m2s-1. These values are equivalent to those used by Nienhuis et al. (2016) and Ruiz-Reina and López-Ruiz (2021). Stability and accuracy requirements were fulfilled using a time step of 6 s (Deltares, 2026) due to the grid size and the bathymetry. The initial conditions were defined as water at rest (cold-start) and null global suspended sediment concentration. A morphological 12 h spin-up interval was used before considering the bathymetry update.

A sensitivity analysis was conducted through eight additional simulations to assess the robustness of the methodology with respect to key model parameters. While maintaining all other parameters constant, the uniform horizontal eddy viscosity was tested at values of 1 and 5 m2s-1, and two Chézy roughness coefficients (60 and 70, in both directions) were evaluated for comparison with the original values. These four sensitivity scenarios were performed for two representative experiments (1980–1981 and 1999–2000). The match percentages obtained for both the T-POT and the optimal POT combination were identical across all sensitivity scenarios (Table S4 in the Supplement), confirming that the identified optimal parameter combination is independent of these numerical settings. To consider the possible influence of the wave climate variability on the morphological evolution, the experimental design is based on six different 1 year wave conditions defined in Sect. 2.2. Each experiment was performed with the same initial baseline bathymetry (basic state, see Sect. 2.1) and these results will be further analyzed in the following sections.

Furthermore, it should be noted that this paper only considers results from numerical models of an idealized coastal area, due to the requirement for synchronized hourly resolution of climate and morphological data. Although morphological changes can occur within hours during energetic conditions (Castelle and Harley, 2020; Nienhuis et al., 2016), existing monitoring technologies often lack the temporal and quantitative precision necessary for hourly volumetric change analysis, making it difficult to perform a validation against field data (see Sect. 3). Nevertheless, as previously mentioned, the proposed methodology has been applied to two real-world coastal areas for demonstration purposes, as detailed in Sect. 5.

The first step is the identification of the ME generated during the morphodynamic simulations. The total volume change within the control volume (CV) (Fig. 1a), which accounts for both overall erosion and accretion, was used to characterize these morphological variations. Each CV has an area of 0.33 km2, the offshore boundary is set at 10 m b.m.s.l. and the beach boundary is located at 3 m a.m.s.l. (a schematic representation is shown in Fig. 1d). The volume is calculated in relation to the baseline depth of 10 m b.m.s.l., which is deeper than the closure depth (at 7 m b.m.s.l.) estimated using the Hallermeier (1980) equation. Following Ortiz and Ashton (2016), using a baseline deeper than the closure depth allows the analysis to capture all the sediment mobilization predicted by the model, as it accounts additional physical processes (tidal currents and river discharge) that are not considered in the Hallermeier formula. This choice was also supported by the numerical grid itself. Using a specific grid line ensures a more consistent and robust boundary for the volume change calculations.

The volume in the CV at hour i (voli) of the simulation is calculated as expressed in equation: 9voli=∑nAn⋅(10-hni)i=1…tf Where tf is the end time of the simulation in hours. Thus, each hourly volume of the CV is calculated as the sum, in the n grid cells of the CV, of the product of the cell area (An) and the difference between the depth -10 m and the depth of the cell at hour i (hni).

The identification of the ME is assessed by the volumetric change every 48 h (Δvoli, Eq. 10). Δvoli was obtained for each hour as the absolute difference between the CV volume 24 h before and after. After a sensitivity study, it was found that this 48 h interval allows considering both the cumulative effect of existing hydrodynamic events (e.g. storms) and the delay in the response time that often exists between the starting of a hydrodynamic event and the morphological response of the beach. 10Δvoli=voli+24-voli-24 Once the volumetric variations have been obtained for each CV and for each experiment, the ME is calculated using the POT method previously explained. Different ME are obtained for each experiment and CV, depending on whether the 90th or 95th percentile is used as the threshold (resulting in ME90 and ME95, respectively).

Wave height is frequently used to represent and define extreme events leading to erosion/retreat. However, morphological changes can also occur due to accretion, for instance associated with the recovery after a storm or to the longshore sediment transport. For an overall and continuous analysis of the coastal behavior, the recovery (accretion) of the coast is as important as the erosion (Castelle and Harley, 2020). To include such changes, a more complex description of the wave energy is required, and other proxies could be used. Wave power could better define overall morphological changes as it includes not only wave height but also wave period. Another advantage of wave power is that it can be decomposed into longitudinal and transverse components, which includes the influence of the incoming wave direction. Due to the morphology of the study area and its exposure to different directions, the LWP was tested as a proxy to identify the morphological changes in addition to the traditional wave height POT analysis. To achieve this, the LWP was calculated every hour using all the available climate data, as described below.

Wave Power (or wave energy flux, P) in deep water, is defined as the wave energy flux per unit of crest length for a fixed duration, d. The wave energy flux can be calculated considering the mean energy density, E, and the group wave celerity, cg, as: 11P=E⋅cg=116ρgHs2⋅gTp4π where ρ is the water density, g is the gravitational acceleration, Hs is the significant wave height and Tp is peak wave period.

The LWP can be calculated as follows: 12LWP=E⋅cg⋅sin⁡(θ)=116ρgHs2⋅gTp4π⋅sin⁡(θ) Where θ is the direction, following the direction criteria defined in Fig. 1a.

Therefore, it is possible to distinguish between CE related to wave height or longitudinal wave power. Both are calculated using the same procedure explained above, the POT method, which gives 45 possible combinations of CE for each year that can generate changes in the coast.

When a ME and a CE occur at the same time, or close to each other, this coincidence is referred to as a match. Since matches refer to the morphological response with respect to a given CE, it is necessary to consider that the coast has a morphological response time (time lag) to a maritime forcing. In this study, it was observed that the largest morphological changes occur within the first 24 h after a CE. Thus, it is considered a match if there is a CE and the corresponding ME is up to 24 h later, in at least one of the CVs. For each possible ME (ME90 and ME95), the variable (Hs or LWP) and the corresponding POT combination with the highest percentage of agreement across all simulations is chosen as the selected pair that best represents the morphological changes in the study area. Note that a 100 % match would mean that all ME would be associated with a CE. To confirm the correct functioning of the selected POT combination, the percentage of the volume mobilized is calculated as the ratio between the volume mobilized by all ME (ME90 or ME95) and the volume mobilized only by the ME identified by the CE (LWP or Hs).

The selection of the optimal POT combination is determined using a statistical approach involving a Composite Index (CI). This index was computed to rank the 45 POT combinations by synthesizing two normalized metrics: mean performance (Mnorm) and consistency (Cnorm). The CI is calculated separately for each ME percentile (ME90 and ME95), using the match results obtained earlier. Therefore, a single final match value (either Mean or Consistency) will be obtained for each POT combination, aggregating the data from the six wave conditions and the two CV. Both metrics are scaled to 0–1 range using a min-max scaling, where 1 will be the best performance. The CI is then calculated as a weighted average: 13CI=0.7⋅Mnorm+0.3⋅Cnorm This weighted approach assigns a higher weight to performance (70 %) than to consistency (30 %), ensuring the selected POT combination achieves high accuracy along with coherent behavior. The normalized mean performance (Mnorm) quantifies the average match across all the wave conditions and both CV for each POT combination. It was normalized using a min-max scaling as follows: 14Mnorm=Mc-MminMmax-Mmin Where Mc denotes the average match for a specific POT combination, calculated across all wave conditions and both CV, and Mmin and Mmax correspond to the minimum and maximum of these average match values, respectively, when considering all 45 POT combinations. The normalized Consistency (Cnorm) coefficient quantifies the relative consistency using the Relative Consistency (RC=σ/μ) coefficient, which is intentionally inverted to prioritize low variability (i.e., high consistency). To calculate it, the standard deviation (σ) and the mean match (μ) for each POT combination are needed, each calculated across the six wave conditions and the two CV. 15Cnorm=1-RC-RCminRCmax-RCmin This inversion ensures that the most consistent combinations receive scores approaching 1, allowing for a coherent combination within the CI.

The results of all experiments show the formation of a delta at the river mouth (Fig. 3). The dynamics of the delta induce asymmetric behavior in the two CVs. They can both act as a source (or sink) of material to (or from) the delta.

In each experiment, this asymmetry manifests such that one CV (typically the SCV) experiences a net loss of material to the delta, while the other (typically the NCV) simultaneously undergoes accretion from it. Consequently, the differential morphodynamic response is primarily driven by the Yearly Mean Wave Direction (YMWD, Table 2), which shifts the direction on the longitudinal sediment transport. This mechanism, in conjunction with coast-delta-tide interactions, results in the observed asymmetric morphodynamic response. Overall, all simulations demonstrate a similar morphology: the formation of a northward-rotated ebb delta. This pattern is a direct result of the longitudinal sediment transport generated by the prevailing wave direction of the selected wave conditions.

Before quantifying the matching results by comparing the ME and CE criteria, an analysis of the results obtained was carried out by identifying the ME and their correspondence with the CE obtained by using the T-POT. As an example, this section will refer to the 1980–1981 simulation, as can be seen in Fig. 4 for the ME90 and Fig. 5 for the ME95.

Focusing only on the results relative to the 90th percentile (Fig. 4), chronologically the first ME, in mid-October, is identified only by the LWP, indicating that the longshore wave power alone was sufficient to exceed the ME threshold despite relatively low wave heights. The reverse occurs in late November, when the wave height conditions are energetic enough to mark a CE that does not correspond to any ME. Similarly to October, in early December 1980, there is an ME on the south beach that corresponds to a CE by the LWP but is not identified by the wave height alone. The strongest match is observed later in December 1980, when a higher-energy storm occurs and the year's largest ME is identified simultaneously by both CEs (LWP and wave height). However, in late December 1980, a ME in both CVs aligns with a CE defined solely by negative LWP, representing northward-directed waves (negative α, Fig. 1a), despite a low wave height. Then, in early to mid-January, an unidentified ME can be observed, followed by another that is only identified by the wave height. Next, from late January to early May, there is a general simultaneous identification of ME by both CE. This pattern holds until mid-May when there is a ME on the south beach that does not correspond to a CE, whether defined by LWP or by wave height. Then, in August 1981, the only ME identified by the wave height criteria that is not considered an event by the LWP is observed. When this ME was analyzed, the importance of cross-shore transport in this event was highlighted. During the rest of the summer some ME were detected but no CE was identified.

A further in-depth analysis was performed by observing whether ME and CE occurred simultaneously (i.e. when the red dots coincided with the black dots). The quantification of the ME is performed simultaneously in both CVs, so that an ME is considered to have occurred if there is a ME in at least one of the two CVs. As shown in Table 3, there are a total of 16 ME for the 90th percentile (Fig. 4a and b, in black). Of these ME, eight are detected by the LWP-based T-POT CE (Fig. 4c, in red) and six by the Hs-based T-POT CE (Fig. 4d, in red). This indicates better performance of the LWP when compared to the wave height, as all identified LWP-CE coincide with ME. In addition, the T-POT of the wave height identifies three other CE that are not associated with any nearby morphological change.

The same analysis was performed for the 95th percentile results (Fig. 5). As the percentile value for ME calculation increased, the number of detected ME decreased (nine ME in total across both CVs). Of these nine ME, six (five) coincide with a T-POT CE based on the LWP (Hs), which also indicates better performance of the LWP compared to the wave height criterion. In this case, both variables have identified CEs that are not associated with any ME: 2 overestimations of LWP and 4 of Hs. This result also suggests that LWP offers a slightly improved performance over Hs in defining a morphological event.

For the remaining experiments, the results can be found in Supplement (Figs. S1–S10), with a summary given in Table 3.

It is noteworthy that for ME90 (ME95), all CE detected by the LWP have a corresponding ME in five (three) out of six experiments. In contrast, for the wave height criterion, this correspondence occurs in only one (zero) out of six experiments. Consequently, the overestimation of events when using wave height is higher than that obtained using LWP. However, for the T-POT analysis, no experiment achieved a complete alignment across all ME metrics. To further evaluate the performance of LWP and Hs, the rates of overestimation and underestimation were analyzed. Here, overestimation refers to a CE with no corresponding ME, while underestimation refers to an ME that remains undetected by any CE. These rates are closely linked to the choice of percentile (ME90 or ME95). As shown in Table 3, the use of ME90 results in a greater number of events, leading to a near-perfect match where almost all CE correspond to an ME. Conversely, because ME95 identifies fewer events, it fails to capture all CE, resulting in higher underestimation.

A direct magnitude comparison between the wave height and LWP thresholds can be misleading since LWP represents only a fraction of the total wave power. Furthermore, the identification of ME events only by wave height (late November, mid-January, and late August) underscores the importance of cross-shore sediment transport, a process where Hs is the primary driver of bed shear stress and sediment resuspension. The absence of CE by LWP-ME match, alongside the existence of a CE by Hs-ME match, suggests that the wave conditions were conducive to significant cross-shore morphodynamic change (e.g., beach erosion/accretion). However, as the cross-shore wave power is directly related to the wave height, the LWP is a key indicator to identify more ME than using just Hs or other cross-shore limited indicator that will not account with longshore variability on sediment transport/deposition.

The results presented in this section are divided into three parts: (1) match percentage values, (2) a statistical approach to find the optimal POT combination(s), and (3) a final study of the mobilized sediment. In general, higher matches (darker green color in Figs. 6 and 7) are found for combinations 1–30 (relative to the 95th and 96th percentiles), and even higher for combinations 1–14 (95th percentile). It can also be seen that ME95 (Fig. 7) has a higher percentage of matches than ME90 (Fig. 6) due to the lower number of ME.

On average, LWP performs better than wave height in terms of match, as the CE by LWP identifies more ME. Nevertheless, for Experiment 5 (2010–2011), the wave height has a better performance, especially for the NCV. On the other hand, Experiment 6 (2019–2020) has the worst performance for wave height, also in the NCV. Although the choice of the best variable may depend on the wave behavior, LWP performed generally better than wave height for the set of experiments used. When comparing both CVs, the NCV had a more diverse behavior in the results than the SCV, which could indicate a shadow effect from the delta. The two experiments that have more extreme behavior are Experiment 5 (2010–2011) and Experiment 6 (2019–2020), and the difference was concentrated at the NCV, while for the SCV the results are not so different. Experiments 5 and 6 are similar in terms of YMWD (Table 1), but the wave conditions for the year 2010-2011 (Experiment 5) generated an added 31.8 % of total wave power. On the other hand, Experiment 5 is the one with more T-POT days, while Experiment 6 is the one with fewer T-POT days (Table 1).

When comparing the performance of the POT combinations through statistical analysis, it is noteworthy that the Combination 11, or POT(95,4,6) (characterized by a 95th percentile, 4 d for the independence criterion, and 6 h of minimum duration), is the top-performing one among all tested combinations. Moreover, as can be seen in Figs. 8 and 9, Combination 11 is the highest-ranked for both ME possibilities. The mean match percentages for ME90 (Fig. 8) are smaller than for ME95 (Fig. 9). However, a similar trend can be found in both, as the combinations with a higher threshold identify fewer ME. Moreover, as seen in Figs. 8d and 9d, the TOP 10 of the POT combinations for ME90 and ME95 are in the combinations 1–14 (95th percentile). Therefore, using the 95th percentile for the threshold seems to outperform the results, whether for wave height or LWP.

Another observation that arises from the results is that for combinations with a minimum storm duration of 48 h (combinations 5, 10, 15, 20, 25, 30, 35, 40, and 45), the match rate decreases drastically, as few storms exhibit such persistence. However, for combinations 15, 30, and 45, as the independence criterion increases to 4 d, the percentage of match slightly increases. This trend suggests that the effectively identified ME are short-lived, high-intensity impulses rather than sustained, long-duration ME. For the optimal combination, the average percentage of matches ranged between 56.1 % and 86.1 % for the different considered criteria and CV with maximum values reaching up to 80 % in some cases.

In terms of matches, the best pair was determined to be LWP and Combination 11. This can be supported by looking at the mobilized volume (Table 4). In general, the percentage of mobilized volume is higher for the ME identified by the CE by LWP criterion than for those identified by wave height. This percentage is systematically higher for ME95, reaching more than 90 % in some cases. The percentages corresponding to Combination 11 are generally higher than those of T-POT, indicating that this combination not only finds a higher match in each case, but also achieves better performance in terms of sediment volume, as it considers more ME. This improved performance in mobilized volume suggests that when the LWP is large enough to capture a CE, there is a much greater correspondence with actual morphological change within the 24 h following the onset of the CE.

However, when analyzing mobilized sediment (Table 4), LWP shows better performance than wave height in the SCV for both ME90 and ME95. Conversely, in the NCV, LWP only excels for ME90, while wave height provides superior results for ME95 (by a mean of approximately 5 % for both POT combinations). This discrepancy is likely because longshore sediment transport is truncated by the delta, thereby restricting northward sediment transfer.

The Punta Umbría Inlet (hereinafter PUI) consists of a NW-SE trending channel, 8 km in length and 0.5 km in width, with a maximum depth of 12 m b.m.s.l. Characterized as an ebb-tidal system, it features minor ebb channels, shoals, and frontal lobes. The model utilizes a spatially distributed D50 sediment grain size, ranging from 0.5 to 4 mm. This is defined via a grid-based input file to reflect the natural variability of the seabed. Due to long-standing navigational difficulties associated with shoal development, a jetty was constructed at the inlet, reaching 4 m b.m.s.l. The numerical setup and validation procedures follow Zarzuelo et al. (2019) in their entirety. A comprehensive description of the model performance is available in that study. The control volume used to apply the methodology is located in the channel (Fig. S11 in the Supplement). The match values obtained for the T-POT are 43.4 % and 41.4 % for the LWP-ME90 and LWP-ME95, respectively, and 42.7 % and 45.1 % for the Hs-ME90 and Hs-ME95, respectively. For the optimal combination (Combination 11, POT(95,4,6)), the match results for LWP increase to 47.4 % (ME90) and 47.5 % (ME95), while the Hs matches remain identical to those of the T-POT. This suggests that the Combination 11 improves upon the T-POT for LWP while maintaining the same accuracy for Hs.

The numerical setup for the Guadiana estuary follows the configuration described in López-Ruiz et al. (2020), where a comprehensive description of the model's calibration and performance is available. The study area encompasses the ebb-tidal delta of the Guadiana River, located at the southern border between Spain and Portugal. The region is characterized by a semi-diurnal mesotidal regime, with a mean tidal range of 2 m. Similar to the PUI, the river mouth is stabilized by a jetty system, and the main channel undergoes periodic dredging to maintain navigability. Sediment distribution in the area exhibits the high variability typical of deltaic environments, with grain sizes ranging from fine to coarse sands. The model utilizes a spatially distributed D50 sediment grain size, ranging from 1 to 10 mm. This is defined via a grid-based input file to reflect the natural variability of the seabed.

Two simulations covering different periods are available for this study area, hereafter referred to as Guadiana 1617 (from July 2016 to June 2017) and Guadiana 1718 (from June 2017 to December 2018). The control volume used to apply the methodology is located in the ebb delta, within an area comparable to the one analyzed by Garel et al. (2019) to unravel the sediment transport patterns in the delta (Fig. S12 in the Supplement). For Guadiana 1617, the match values obtained for the T-POT are 63.5 % and 62.9 % for the LWP-ME90 and LWP-ME95, respectively, and 64.3 % and 63.9 % for the Hs-ME90 and Hs-ME95, respectively. For the Combination 11, the match results for LWP increase to 67.5 % (ME90) and 67.9 % (ME95), while the Hs matches remain identical to those of the T-POT. For the Guadiana 1718 period, the T-POT achieved match values of 72.3 % and 90.5 % for LWP (ME90 and ME95, respectively), and 73.1 % and 90.5 % for Hs. The Combination 11 improved the LWP-ME90 match to 79 % and the Hs-ME90 to 74.4 %, while maintaining identical results for all ME95 events. Notably, almost all ME95 occurred during Storm Emma (February 2018), which heavily impacted the South Atlantic coast of the Iberian Peninsula (Málvarez et al., 2021).

In many coastal regions, longshore processes can induce significant morphological changes. This is especially relevant in coastal zones adjacent to inlets, which are highly sensitive to shifts in wave direction and wave regime (Stevens et al., 2024). Castelle et al. (2020) highlighted that modest variations in wave obliquity can induce substantial beach rotation and planform changes in embayed beaches, where shifts as small as 5–10° in the dominant wave direction can induce complete reversals in sediment transport patterns. Similarly, Loureiro et al. (2012a, b) found that wave direction shifts of as small as 10° can transition beach systems from an equilibrium state to erosive conditions, especially when these shifts coincide with high-energy wave events. The modeling results described in Sect. 4.1 confirm this sensitivity, showing that all simulations produced asymmetric delta formation with northward rotation (Fig. 3), directly reflecting the prevailing wave direction of the selected yearly wave conditions (Table 1, YMWD).

The effect of wave obliquity is further magnified in areas with complex nearshore bathymetry, where directional changes in wave approach angles can create localized erosion hotspots and accelerate sediment transport rates even under moderate wave energy conditions (Horta et al., 2018). According to these authors, wave obliquity can influence beach circulation patterns, which can rapidly shift from longshore-dominated to rip-dominated systems depending on changes in offshore wave direction and tidal conditions. These circulation transitions can lead to relevant morphological responses even when wave heights remain relatively constant. This directional sensitivity, coupled with complex local interactions between the ebb delta, the discharge, the tides, and the nearshore currents, results in the observed morphodynamic asymmetry between the two control volumes. It is also the driving mechanism behind alongshore changes to coastal morphology that simpler indicators like Hs alone might not report.

Despite these documented impacts, most of the research examining the impact of longshore transport on coastal evolution has focused on monthly to multi-year timescales (e.g., Fernández-Fernández et al., 2020; Kahl et al., 2024; Roelvink et al., 2020; Stevens et al., 2024), without analyzing how these directionally sensitive processes operate at the event time scale. The methodology of this work, based on the LWP (Eq. 12), which integrates directional information, provides a more process-relevant proxy for the coastal forces driving event-based morphological change, enabling the detection of significant coastal responses that would be missed by conventional Hs approaches. Furthermore, by applying the LvC (Longshore vs. Cross-shore) index proposed by López-Dóriga and Ferreira (2017), it was found that the NCV exhibits a more cross-shore dominated in experiment 2 (1990–1991) with an LvC=0.18, and in experiment 5 (2010–2011) with LvC=0.01, both of which correspond to a higher Hs-match. Conversely, the SCV shows systematically higher LvC across most experiments, indicating a greater degree of longshore dominance, which corresponds with a higher LWP-match. The calculated LvC indices for all experiments and control volumes are provided in Table S5 in the Supplement.

The methodology proposed in this paper expands beyond typical erosive storms as the sole contributor to significant short-term morphological changes. It integrates other CE capable of inducing substantial short-term morphological change through both erosion and sedimentation processes. This approach directly addresses the limitations identified in Sect. 4.2, where traditional wave height-based methods failed to capture many ME. A major advantage of the method is its generalizability. Although developed for an idealized coastal zone described in Sect. 2.1, it can be applied to different geographical and geomorphological contexts. The approach addresses both erosion and sedimentation processes simultaneously, providing a comprehensive view of morphological changes, a significant improvement over existing methods that typically focus on either erosion or sedimentation in isolation, as noted by Mentaschi et al. (2018) in their global assessment of coastal erosion and accretion patterns.

The methodology is innovative in its use of deep-water variables to enhance flexibility and applicability across diverse coastal environments (Sect. 2.2). The method also accounts for post-storm recovery processes, an essential component for understanding long-term coastal dynamics that is frequently overlooked in traditional storm impact assessments. This recovery aspect is particularly important, as shown by Málvarez et al. (2021) who documented both the immediate erosional impacts of storms and the subsequent recovery periods in developed coastal systems.

One of the key elements of the methodology is the use of the LWP, which has been under-utilized in previous coastal morphodynamic studies. As defined in Sect. 3.2 (Eq. 12), this parameter provides important insights into the impact of directional wave energy, complementing the traditional Hs analyses. The results in Sect. 4.3 demonstrate that LWP can outperform wave height in identifying morphological events. Furthermore, the optimization approach analyzing multiple POT combinations (Table 2) allows for the identification of the most effective parameter set for detecting morphological changes. As shown in Table 4, the optimized POT combination not only achieved higher match percentages in all scenarios (Figs. 8 and 9) but also captured a greater proportion of the total sediment volume mobilized during morphological events, particularly for ME95, where it exceeded 90 % in several cases. This demonstrates a more robust linkage between the identified climatic forcing (LWP-CE) and the coastal response. It should be noted, however, that the superior performance of LWP does not preclude the utility of Hs as a morphological proxy. In coastal settings characterized by a low LvC index, where cross-shore processes dominate sediment transport, Hs remains a more accurate indicator of morphologically significant events, suggesting that LWP serves as a complementary proxy rather than a total replacement.

While the methodology offers several advances, it also has specific limitations that require further refinement. The use of deep-water variables, while providing flexibility and ease of application, may introduce uncertainties when applied to complex coastal sites with intricate bathymetry or varying nearshore wave conditions. This limitation can be particularly evident in areas where wave transformation between offshore and nearshore zones is complex and nonlinear, as observed by Loureiro et al. (2012b) in their study of geologically constrained morphological variability on embayed beaches. The robustness of the methodology is further supported by its transition from idealized configurations to real-world scenarios (Sect. 5). Although the method was developed following established idealized frameworks (e.g., Jiménez-Robles et al., 2016; Ruiz-Reina and López-Ruiz, 2021), its successful validation in calibrated environments with real-world forcings, which include wind, variable river discharge, and full tidal regimes, addresses the applicability to complex coastal zones and supports the robustness of the methodology under more complex forcing conditions.

This methodological approach differs from traditional Hs-based POT techniques used in coastal engineering and opens new research directions to verify its performance under different conditions. It represents the first application of LWP-based POT, making direct comparison with traditional Hs-based POT analyses challenging. The results demonstrate that climatic events can be effectively characterized through a composite LWP-based POT analysis, incorporating established parameters such as independence criteria and event duration. While the specific parameter combination identified appears robust for the detection of morphological events in the study area, it is likely to be site-dependent, requiring site-specific calibration in future applications. Future validation efforts may become feasible as emerging monitoring technologies (e.g., continuous video systems, high-resolution remote sensing) develop the capability to provide the required temporal resolution for morphological measurements, across a wider range of LvC conditions.

The approach here presented has significant management applications by enabling advanced forecasting of coastal morphological changes. This ability to anticipate coastal responses represents a critical advance in management practices, considering that coastal zones provide essential protection against erosion and flooding, which is critical for adaptation and planning decisions (Toimil et al., 2023). Current approaches to coastal protection against erosion and extreme flooding often involve the definition of setback buffer zones where permanent construction is prohibited (Karditsa and Poulos, 2024; Sanò et al., 2011). Sanò et al. (2011) specifically emphasized that effective setback definition requires dynamic assessment methods that account for both long-term trends and event-driven morphological changes, a gap that the methodology presented here directly addresses.

Nahon et al. (2022) also found that increased rates of LWP (along with the associated longshore currents and sediment transport), resulted in the erosion of barrier spits on the updrift margin of a tidal inlet. All the above statements and existing results prove the need to include the longshore component of sediment transport (which is directly associated with LWP) in 3D analyses of short-term morphological changes in coastal areas. Therefore, accurately quantifying sediment transport requires the inclusion of wave direction. LWP captures this directional effect and offers a more complete description of the coastal forces driving morphodynamic change. While previous studies address shoreline retreat assessment, many rely on large-scale analysis that neglects cumulative storm impacts, storm duration, and spacing effects (Monioudi et al., 2023; Xie et al., 2024). The new methodology addresses these gaps and could assist managers and policymakers in identifying at-risk beaches, estimating losses in beach carrying capacity and economic value, and prioritizing effective adaptation responses.

From an operational management perspective, coastal zone concessions do not usually consider the available beach area or the expected shoreline evolution. Palazón et al. (2018) documented this disconnection in southeastern Spain, where 62 % of the concessions were inappropriately located in relation to coastline dynamics, leading to both infrastructure damage and reduced user satisfaction as the freely usable beach surface area is reduced. This approach allows for a more detailed analysis of individual storm impacts but also to impacts resulting from longshore shifts, thereby improving the coastal area management. Celedón et al. (2023) identified similar challenges in their assessment of risk hotspots for storm events in coastal regions with high morphodynamic variability, emphasizing the need for event-based forecasting capabilities such as those provided by the here presented method. Understanding which CE are likely to trigger significant changes in specific coastal areas enhances the preparedness of coastal managers and facilitates preventive actions such as timely asset removal or targeted short-term nourishment to mitigate erosion (Málvarez et al., 2021).

Ultimately, by providing a tool to link specific climatic forcing patterns (including directional shifts that may be altered by climate change) with morphological responses, this method can contribute to the development of more effective, process-based coastal adaptation strategies, including early warning systems or the strategic implementation of nature-based solutions.