Euro-limpacs Deliverables


Report on the effect of climate change on recovery from acidification

This report contains a description of different statistical techniques for the analysis of long−term data which have been applied or are going to be applied to study sites in WP4. Focus of Task 2.1 in WP4 is on detection of trends in long data series: direction, magnitude, and relationships with climate data.
Direction and magnitude of trends are related to recovery (or lack of it) from acidification. They can be detected and quantified with traditional methods like linear regression or Seasonal Kendall Test (SKT). The main interest in EUROLIMPACS is on how climate drivers (temperature, precipitation) may interact with long?term trends accelerating/delaying recovery or determining abrupt changes in the data series (as a consequence of extreme events e.g. heavy rains, droughts).

In section A of this report, a summary of the main methods currently in use within WP4 for time series data analysis is presented. These techniques are available in the most commonly used statistical packages, with the exception of structural time series models (available in Brodgar: ( The choice of a particular technique is generally dependent upon the data characteristics (time series length and frequency, number of missing data, seasonal pattern, etc.).

In section B some case studies are presented. Some of the previously described techniques are applied to the NO3 time series in some rivers in Northern Italy (case study 1), TOC time series in Finnish lakes (case study 2), nutrient transport from large rivers in Sweden to the Baltic sea (case study 3), and trends of nitrate and sulphate concentrations in the Bohemian Forest (Czech Republic) and the Tatra Mountains (Slovakia) (case study 4).

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