Modelling and visualising trends of extreme values in acidifying variablesOlivetti, Leonardo; Von Brömssen, Claudia;
The southern regions of Sweden, Norway and Finland are among the areas in Europe most affected by surface water acidification (European Environment Agency, 2016). Since the 1980s, some steps have been taken to contrast this issue, which have contributed to a steady recovery of the water chemistry (Erlandsson et al., 2010). Among others, the Geneva Convention on Long Range Transboundary Air Pollution and its associated protocols have made an important contribution to the reduction of sulfate emissions (ibid.). In addition, monitoring programs have been put in place to detect patterns of acidification, and predict the long-term path to full recovery (European Environment Agency, 2016; Fölster et al., 2014). A number of models have been developed in order to study the long-term trends in the levels of PH and other acidifying variables of relevance (Moldan et al., 2013; Wright and Cosby, 2003). Those models are useful to evaluate the overall trend in the levels of water acidity, and discuss whether the process of recovery is going in the right direction. However, a limitation of those models is that they might overlook the occurrence of episodic acidification, which can have severe impacts on local ecosystems (Baker et al., 1996; Heard et al., 1997; Laudon, 2008). Sudden changes in the levels of acidity can be deadly to a large number of fish species, and undermine long-term biodiversity (ibid.). The purpose of this research is to contribute to fill this knowledge gap, by developing a framework to analyse, graphically and numerically, trends in the occurrence of episodic acidification. By combining the use of generalised additive models and quantile regression, models able to incorporate both seasonal and long-term time trends are developed. Patterns in episodic acidification are then illustrated with the help of visual tools first introduced by von Brömssen et al. (2021).
Published inRapport (Institutionen för energi och teknik, SLU) 2021, number: 121
Publisher: Department of Energy and Technology, Swedish University of Agricultural Sciences
UKÄ Subject classification
Probability Theory and Statistics
URI (permanent link to this page)