Beach profile data sets provide valuable insight into the morphological evolution of sandy shorelines. However, beach monitoring schemes often show large variability in temporal and spatial intervals between beach profiles. Moreover, beach profiles are often incomplete (i.e. only a part of the profile is measured) and data gaps are unavoidable. The resulting irregular sets of beach profiles complicate statistical analysis and previous studies on the morphological evolution and the effects of external influences have often omitted incomplete beach profiles. In this perspective, a statistical model is suggested to study beach profiles and to identify the effects of external influences. To be precise, the statistical model can be used (1) to determine the temporal and spatial variability of beach profiles while accounting for autoregressive dependencies in space and time, (2) to identify effects of external influences, (3) to predict complete beach profiles at unknown locations (i.e., interpolation between beach profiles), and (4) to forecast complete beach profiles accounting for external influences, such as storm events or nourishments. To illustrate the applicability of this model to irregular beach profile data, this state-of-the-art functional, spatiotemporal model was applied to beach profiles of the island of Sylt, Germany. In a first case study on submerged beach profiles, a decreasing temporal dependency between the profiles in the offshore direction was revealed, highlighting that less frequent measurements of offshore areas would suffice. A second analysis of the emerged beach profiles revealed the general effect of storm conditions (wave heights $>$ 5 m) on subsequently measured beach profiles, which was statistically significant, and the profiles eroded with approximately 0.2–0.7 m in height. In summary, this study proposes and explores the application of a state-of-the-art statistical model to investigate beach profile changes from increasingly diverse and large profile data in coastal engineering and management.

海滩剖面数据集提供了对沙质海岸线形态演变的宝贵见解。然而，海滩监测方案通常显示海滩剖面之间的时间和空间间隔存在很大差异。此外，海滩剖面通常是不完整的（即仅测量剖面的一部分）并且数据差距是不可避免的。由此产生的一组不规则的海滩剖面使统计分析变得复杂，以前关于形态演化和外部影响影响的研究往往忽略了不完整的海滩剖面。从这个角度来看，建议使用统计模型来研究海滩剖面并确定外部影响的影响。准确地说，统计模型可用于 (1) 确定海滩剖面的时间和空间变异性，同时考虑空间和时间的自回归依赖性，(2) 识别外部影响的影响，(3) 预测未知位置的完整海滩剖面（即，海滩剖面之间的插值），和 (4) 预测考虑外部影响的完整海滩剖面，例如风暴事件或营养。为了说明该模型对不规则海滩剖面数据的适用性，将这种最先进的功能性时空模型应用于德国叙尔特岛的海滩剖面。在对水下海滩剖面的第一个案例研究中，揭示了离岸方向剖面之间的时间依赖性下降，强调不太频繁地测量近海区域就足够了。对出现的海滩剖面的第二次分析揭示了风暴条件的一般影响（波浪高度$>$5 m) 在随后测量的海滩剖面上，这是统计显着的，剖面侵蚀了大约 0.2-0.7 m 的高度。总之，本研究提出并探索了应用最先进的统计模型来调查海岸工程和管理中日益多样化和大型剖面数据的海滩剖面变化。