The use of surface elevation table (SET) instruments to monitor elevation changes at low elevation coastal locations has steadily increased in recent years. A primary focus in the analysis of SET data is the estimation of the overall rate of elevation change, and numerous approaches have been used for this purpose. In this work, we compare and contrast several methods used for estimating the true rate of elevation change at SET station locations, including a novel approach proposed in this work that incorporates spatial dependence. We also discuss theoretical properties of one class of estimators, and undertake a comprehensive simulation study. Additionally, we present two case studies where we illustrate these differences using real SET data. All methods considered here tend to produce similar point estimates, but some confidence interval procedures can generate intervals with empirical coverage rates lower than specified. However, the best analysis approach is likely dependent upon selecting the method that best coincides with the true underlying process. Thus, we do not uniformly recommend one approach for all situations. Instead, we suggest carefully weighing potential advantages and disadvantages of each method before conducting analysis, while keeping in mind the ways in which modeling assumptions may impact this decision.

近年来，使用地表高程表 (SET) 仪器监测低海拔沿海地区海拔变化的情况稳步增加。SET 数据分析的一个主要重点是估计整体高程变化率，为此目的使用了许多方法。在这项工作中，我们比较和对比了几种用于估计 SET 站位置真实高程变化率的方法，包括本工作中提出的一种结合空间依赖性的新方法。我们还讨论了一类估计器的理论性质，并进行了全面的模拟研究。此外，我们提出了两个案例研究，我们使用真实的 SET 数据来说明这些差异。这里考虑的所有方法都倾向于产生相似的点估计，但是一些置信区间程序可以生成经验覆盖率低于指定的区间。然而，最好的分析方法可能取决于选择最符合真实基础过程的方法。因此，我们并不统一推荐一种适用于所有情况的方法。相反，我们建议在进行分析之前仔细权衡每种方法的潜在优缺点，同时牢记建模假设可能会影响此决策的方式。