A toolbox for visualizing trends in large-scale environmental data. (February 2021)

• Record Type: Journal Article
• Authors: von Brömssen, Claudia; Betnér, Staffan; Fölster, Jens; Eklöf, Karin
• Is Part Of: Environmental modelling & software. Volume 136(2021)
• Publication Date: 2021-02
• Subjects: Generalized additive models -- Visualization of trends -- Surface waters -- Acidification -- Chemical recovery; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104949
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Generalized additive models can greatly enhance environmental trend analysis. Complex information in multiple series can be summarized by a small number of plots. Visualization of periods of change simplifies identification of driving forces.