A new methodology is proposed to estimate storm demand and dune recession by clustered and non-clustered events, to determine if the morphological response to storm clusters results in greater beach erosion than that from individual storms that have the same average recurrence interval (ARI) or return period. The method is tested using a numerical morphodynamic model that combines both cross-shore and longshore beach profile evolution processes, forced by a 2D wave transformation model, and is applied as an example within a 20 km long coastal cell at an erosion hotspot at Old Bar, NSW mid-north coast, Australia. Wave and water level data hindcast in previous modelling (Davies et al., 2017) were used to provide two thousand different synthetic wave and tide records of 100 years duration for input to a nested nearshore 2D SWAN model that provides wave conditions at the 12 m depth contour. An open-source shoreline evolution model was used with these wave conditions to model cross-shore and longshore beach profile evolution, and was calibrated and verified against long-term dune recession observations. After a 50 year model spin up, 50 years of storm demand (change in sub-aerial beach volume) and dune toe position were simulated and ranked to form natural estimators for the 50, 25, 16, 12.5 and 10 year return period of individual events, together with confidence limits. The storm demand analysis was then repeated to find the return period of clustered and non-clustered morphological events. Morphological clusters are defined here by considering the response of the beach, rather than the forcing, with a sensitivity analysis of the influence of different recovery thresholds between storms also investigated. The new analysis approach provides storm demand versus return period curves for the combined population of clustered and non-clustered events, as well as a curve for the total population of individual events. In this approach, non-clustered events can be interpreted as the response to isolated storms. For clustered and non-clustered morphological events the expected storm demand for a 50-year return period is approximately 25% greater than that for individual events. Alternatively, for clustered and non-clustered events the magnitude of the storm demand that occurs at a return period of 17 years is the same as that which occurs at a return period of 50 years for individual events. However, further analysis shows that for a 50-year return period, the expected storm demand for the population of non-clustered events is similar to that of the clustered events, although the size of the population of the latter is much greater. Hence, isolated storms can generate the same storm demand as storm clusters, but there is a much higher probability that a given storm demand is generated by a morphologically clustered event.

提出了一种新方法来估计集群和非集群事件的风暴需求和沙丘衰退，以确定对风暴集群的形态响应是否导致比具有相同平均重现间隔 (ARI) 的单个风暴更大的海滩侵蚀或回归期。该方法使用数值形态动力学模型进行测试，该模型结合了跨海岸和沿海海滩剖面演化过程，由二维波变换模型强制，并在老巴侵蚀热点的 20 公里长海岸单元内作为示例应用，新南威尔士州中北部海岸，澳大利亚。先前建模中的波浪和水位数据后报（Davies 等人，2017) 用于提供 100 年持续时间的 2000 种不同的合成波浪和潮汐记录，以输入嵌套的近岸 2D SWAN 模型，该模型提供 12 m 深度等值线的波浪条件。开源海岸线演化模型与这些波浪条件一起用于模拟跨海岸和长岸海滩剖面演化，并根据长期沙丘衰退观测进行校准和验证。在 50 年的模型启动后，对 50 年的风暴需求（亚空中海滩体积的变化）和沙丘位置进行了模拟和排序，以形成个体 50、25、16、12.5 和 10 年重现期的自然估计量。事件以及置信限。然后重复风暴需求分析以找出聚集和非聚集形态事件的重现期。这里通过考虑海滩的响应而不是强迫来定义形态集群，还研究了风暴之间不同恢复阈值的影响的敏感性分析。新的分析方法为聚集和非聚集事件的组合提供了风暴需求与重现期的曲线，以及单个事件的总人口的曲线。在这种方法中，非集群事件可以解释为对孤立风暴的响应。对于集群和非集群形态事件，50 年重现期的预期风暴需求比单个事件的需求高约 25%。或者，对于集群和非集群事件，发生在 17 年重现期的风暴需求量级与发生在 50 年重现期的单个事件的风暴需求量级相同。然而，进一步的分析表明，对于 50 年的重现期，非聚集事件群体的预期风暴需求与聚集事件的群体相似，尽管后者的群体规模要大得多。因此，孤立风暴可以产生与风暴集群相同的风暴需求，但给定风暴需求由形态学集群事件产生的可能性要高得多。非集群事件群体的预期风暴需求与集群事件相似，但后者的群体规模要大得多。因此，孤立风暴可以产生与风暴集群相同的风暴需求，但给定风暴需求由形态学集群事件产生的可能性要高得多。非集群事件群体的预期风暴需求与集群事件相似，但后者的群体规模要大得多。因此，孤立风暴可以产生与风暴集群相同的风暴需求，但给定风暴需求由形态学集群事件产生的可能性要高得多。