Abstract

Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country.

Keywords

Geographically weighted regression; Temporal trend assessment; Brownification; Surface waters; Low temporal monitoring frequency

Published in

Environmental Monitoring and Assessment
2023, Volume: 195, number: 5, article number: 547
Publisher: SPRINGER

SLU Authors

• von Brömssen, Claudia

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

• Fölster, Jens

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    

• Eklöf, Karin

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Environmental Sciences


Publication identifier

DOI: https://doi.org/10.1007/s10661-023-11172-2

Permanent link to this page (URI)

https://res.slu.se/id/publ/122062