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A toolbox for visualizing trends in large-scale environmental data

von Brömssen, Claudia and Betnér, Staffan and Fölster, Jens and Eklöf, Karin (2021). A toolbox for visualizing trends in large-scale environmental data. Environmental Modelling and Software. 136 , 104949
[Journal article]

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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.

Authors/Creators:von Brömssen, Claudia and Betnér, Staffan and Fölster, Jens and Eklöf, Karin
Title:A toolbox for visualizing trends in large-scale environmental data
Series Name/Journal:Environmental Modelling and Software
Year of publishing :2021
Volume:136
Article number:104949
Number of Pages:12
Publisher:ELSEVIER SCI LTD
ISSN:1364-8152
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Environmental Sciences (social aspects to be 507)
Keywords:Generalized additive models, Visualization of trends, Surface waters, Acidification, Chemical recovery
URN:NBN:urn:nbn:se:slu:epsilon-p-111017
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-111017
Additional ID:
Type of IDID
DOI10.1016/j.envsoft.2020.104949
Web of Science (WoS)000616063500004
ID Code:22755
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Energy and Technology
(NL, NJ) > Dept. of Aquatic Sciences and Assessment
Deposited By: SLUpub Connector
Deposited On:12 Mar 2021 13:04
Metadata Last Modified:12 Mar 2021 13:11

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