Browsing by Subject "boreal lake"

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  • Arola, Aleksi (Helsingin yliopisto, 2021)
    Freshwater ecosystems are an important part of the carbon cycle. Boreal lakes are mostly supersaturated with CO2 and act as sources for atmospheric CO2. Dissolved CO2 exhibits considerable temporal variation in boreal lakes. Estimates for CO2 emissions from lakes are often based on surface water pCO2 and modelled gas transfer velocities (k). The aim of this study was to evaluate the use of a water column stratification parameter as proxy for surface water pCO2 in lake Kuivajärvi. Brunt-Väisälä frequency (N) was chosen as the measure of water column stratification due to simple calculation process and encouraging earlier results. The relationship between N and pCO2 was evaluated during 8 consecutive May–October periods between 2013 and 2020. Optimal depth interval for N calculation was obtained by analysing temperature data from 16 different measurement depths. The relationship between N and surface pCO2 was studied by regression analysis and effects of other environmental conditions were also considered. Best results for the full study period were obtained via linear fit and N calculation depth interval spanning from 0.5 m to 12 m. However, considering only June–October periods resulted in improved correlation and the relationship between the variables more closely resembling exponential decay. There was also strong inter-annual variation in the relationship. The proxy often underestimated pCO2 values during the spring peak, but provided better estimates in summer and autumn. Boundary layer method (BLM) was used with the proxy to estimate CO2 flux, and the result was compared to fluxes from both BLM with measured pCO2 and eddy covariance (EC) technique. Both BLM fluxes compared poorly with the EC flux, which was attributed to the parametrisation of k.
  • Voutilainen, Ari; Arvola, Lauri Matti Juhani (2017)
    In order to improve our understanding of the connections between the biological processes and abiotic factors, we clustered complex long-term ecological data with the self-organizing map (SOM) technique. The available 21-year long (1990–2010) data set from a small pristine humic lake, in southern Finland, consisted of 27 meteorological, physical, chemical, and biological variables. The SOM grouped the data into three categories of which the first one was the largest with 12 variables, including metabolic processes, dissolved oxygen, total nitrogen and phosphorus, chlorophyll a, and taxonomical groups of plankton known to exist in spring. The second cluster comprised of water temperature and precipitation together with cyanobacteria, algae, rotifers, and crustacean zooplankton, an association emphasized with summer. The third cluster was consisted of six physical and chemical variables linked to autumn, and to the effects of inflow and/or water column mixing. SOM is a useful method for grouping the variables of such a large multi-dimensional data set, especially, when the purpose is to draw comprehensive conclusions rather than to search for associations across sporadic variables. Sampling should minimize the number of missing values. Even flexible statistical techniques, such as SOM, are vulnerable to biased results due to incomplete data.