Water Quality in Swedish Lakes and Watercourses : Modeling the Intra-Annual Variability

Abstract: Water quality is of great importance for ecosystems and society. This thesis characterized and modeled the variation in several key constituents of Swedish surface waters, with particular consideration given to intra-annual variability and sensitivity to climate change. Cyanobacterial data from 29 lakes and basins as well as total organic carbon (TOC) from 215 watercourses were used. Extensive data on catchment characteristics, morphometry, discharge, temperature and other water chemistry data were also analyzed. Models characterizing the seasonality in cyanobacterial concentration and relative cyanobacterial abundance were developed with common lake variables. Concentrations of TOC, iron and absorbance were simulated using discharge, seasonality and long-term trend terms in the Fluxmaster modeling system. Spatial patterns in these model terms were investigated, and the sensitivity of cyanobacteria and TOC to future climate was explored.Nutrients were the major control on cyanobacterial concentration seasonality, while temperature was more important for relative cyanobacterial abundance. No cyanobacterial blooms occurred below a total phosphorus threshold of 20 µg l-1. Discharge and seasonality explained much of the intra-annual variability in TOC, but catchment characteristics could only explain a limited amount of the spatial patterns in the sensitivity to these influences. North of Limes Norrlandicus the discharge term had a larger impact on the TOC concentration in large catchments than in small catchments, while south of Limes Norrlandicus the seasonality had a larger impact in small catchments than in larger catchments. According to the climate change scenarios, both TOC and cyanobacterial concentrations will be higher in the future. The cyanobacterial dominance will start earlier and persist longer. The spring TOC concentration peak will come earlier. The changes in TOC loads are more uncertain due to predicted declines in discharge.Parsimonious statistical regression models could explain observed variability in cyanobacteria and TOC. For predictions, these models assume that future aquatic ecosystems will exhibit the same sensitivity to major drivers as in the past. If this proves not to be the case, the modeling can serve as a sentinel for changing catchment function as indicated by degradation in model performance when calibrations on older data are used to model later observations.