Limnologists and water resources managers have traditionally perceived lakes as discrete geographical entities. This has resulted in a tendency for scientific lake studies to concentrate on lakes as individuals, with little connection either to each other or to large-scale driving forces. Since the 1990s, however, a shift in the prevailing paradigm has occurred, with lakes increasingly being seen as responding to regional, rather than local, driving forces. The seminal work on regional coherence in lake behaviour was that of Magnuson et al. (1990), who showed that many features of lakes within the same region respond coherently to drivers such as climate forcing and catchment processes. From this study it emerged that the degree of coherence among lakes is greatest for those properties most directly affected by climate forcing. Specifically, the physical properties of lakes tend to vary in a more coherent way than their chemical and biological properties (see also Kratz et al., 1998). Further overviews of the topics of coherence and climate-driven variability, focusing mainly on North American lakes, have been given by Magnuson et al. (2006a, b). In this chapter, we will examine the phenomenon of spatial coherence among time-series of some important physical, chemical and biological lake variables at regional and supra-regional scales in Europe. Here, spatial coherence is defined as the degree of correlation between time-series of measurements made simultaneously at different locations (in contrast to temporal coherence, which is defined as the degree of correlation between time-series of measurements made at one location but at different times). The concept of coherence in this context is not well-defined in a mathematical sense. It can be parameterised in various ways: for instance as themean coefficient of determination (r2) calculated between all possible lake pairs, or as the mean coefficient of determination calculated between each lake and the mean time-series of all other lakes (e.g., Livingstone and Dokulil, 2001), or as an intraclass correlation coefficient (e.g., Rusak et al., 1999). Regardless of the parameterisation employed, appropriate methods to remove the effect of daily and annual cycles were used in all examples mentioned in this chapter before computing coherence, so that, for instance, shared seasonal patterns among lakes do not affect the results. In the following, we use the term ¿regional¿ to refer to spatial scales corresponding to a circle of about ten kilometres up to several hundred kilometres in diameter, and the term ¿supra-regional¿ to refer to spatial scales any larger than this.

LIVINGSTONE David;  ADRIAN Rita;  ARVOLA Lauri;  BLENCKNER Thorsten;  DOKULIL Martin;  HARI Renata E.;  GEORGE Glen D.;  JANKOWSKI Thomas;  JARVINEN Marko;  JENNINGS Eleanor;  NOGES Peeter;  NOGES Tiina;  STRAILE Dietmar;  WEYHENMEYER Gesa; 

2010-01-26

Springer Science+Business Media B.V.

JRC54777

978-90-481-2945-4,   

https://publications.jrc.ec.europa.eu/repository/handle/JRC54777,   

10.1007/978-90-481-2945-4_17,