Generalized additive models are increasingly used to identify and describe environmental trends. A major advantage of these models, as compared to simpler statistical tools such as linear regression or Mann-Kendall tests, is that they provide estimates of prevailing levels and trend magnitudes at any given point in time instead of an overall measure. For multiple time series, this versatility has to be followed by flexible visualization methods that can summarize and visualize trend analysis results for many series simultaneously. Here, we propose several types of visualizations and illustrate the methods by showing trends in variables related to the recovery from acidification in Swedish riverine data over the period 1988–2017. By this, we show that generalized additive models, together with a small number of selected plots, can comprehensively illustrate prevailing trends and summarize complex information from multiple series.

通用添加剂模型越来越多地用于识别和描述环境趋势。与简单的统计工具（例如线性回归或Mann-Kendall检验）相比，这些模型的主要优势在于，它们可以提供任何给定时间点的主流水平和趋势幅度的估计，而不是整体度量。对于多个时间序列，此多功能性之后必须是灵活的可视化方法，该方法可以同时汇总和可视化多个序列的趋势分析结果。在这里，我们提出几种类型的可视化方法，并通过显示1988-2017年瑞典河流数据中与酸化恢复相关的变量趋势来说明方法。通过这种方式，我们证明了广义可加模型以及少量的选择图，