A toolbox for visualizing trends in large-scale environmental data

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