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INTRODUCTION
Over the last years, high resolution grids of various climatic parameters have been developed at global scale (e.g., Hijmans et al. 2005; Mitchell & Jones 2005; Sheffield et al. 2006) providing significant information for regions with no meteorological stations. The availability of such data stimulates interest for the implementation of various statistical techniques, which have already been used to capture the spatial and seasonal patterns of precipitation or other climatic parameters using data directly from meteorological stations (Wackernagel 2003; Chen et al. 2009; Buttafuoco et al. 2011; Marchetti et al. 2015).
The development of gridded climatic data is usually performed either using climatic models (e.g., general circulation models) (Sheffield et al. 2006; Shaffrey et al. 2009; Watanabe et al. 2010) in combination with downscaling techniques (Wilby & Wigley 1997), or using interpolation techniques (Ninyerola et al. 2000; Boer et al. 2001; Hijmans et al. 2005). Advanced interpolation techniques take into account not only the site-specific observations from stations and their location but also the effects of topography and other parameters (Ninyerola et al. 2000; Hijmans et al. 2005), while climatic models include also the effects of ocean and air masses’ circulation (Sheffield et al. 2006; Shaffrey et al. 2009; Watanabe et al. 2010).
Gridded multi-temporal climatic datasets can be used in order to define regions of similar spatiotemporal variability based on one or more climatic parameters using techniques such as cluster analysis in GIS environment. Such techniques have been used on land surface temperature (LST) gridded data in an attempt to define sub-regions with different seasonal LST variability, to assess its sensitivity to climatic change and to support environmental analysis (Miliaresis 2009, 2012; Miliaresis & Partsinevelos 2010; Maeda & Hurskainen 2014). In addition, Miliaresis (2013, 2014) proposed a method for standardizing multi-temporal LST imagery in order to reveal and describe thermal anomalies using elevation, latitude and longitude dependencies using correlation and principal component analysis (PCA). These techniques have also been adopted in order to analyze the spatiotemporal patterns of precipitation and reference crop evapotranspiration allowing the terrain segmentation of a territory based on the spatiotemporal variation of the two climatic parameters (Demertzi et al. 2014; Aschonitis et al. 2016).
The climatic regionalization/segmentation of a territory based on the spatiotemporal variation of a gridded climatic...