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Advances in Meteorology
Volume 2016 (2016), Article ID 3092671, 14 pages
Research Article

Terrain Segmentation of Greece Using the Spatial and Seasonal Variation of Reference Crop Evapotranspiration

1Department of Life Sciences and Biotechnology, University of Ferrara, Via L. Borsari 46, 44121 Ferrara, Italy
2Environmental Conservation & Management, Faculty of Pure and Applied Sciences, Open University of Cyprus, Latsia, P.O. Box 12794, 2252 Nicosia, Cyprus
3Department of Hydraulics, Soil Science and Agricultural Engineering, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece

Received 13 May 2015; Accepted 8 September 2015

Academic Editor: Jorge E. Gonzalez

Copyright © 2016 Vassilis Aschonitis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The study presents a combination of techniques for integrated analysis of reference crop evapotranspiration () in GIS environment. The analysis is performed for Greece and includes the use of (a) ASCE-standardized Penman-Monteith method for the estimation of 50-year mean monthly , (b) cross-correlation and principal components analysis for the analysis of the spatiotemporal variability of , (c) -means clustering for terrain segmentation to regions with similar temporal variability of , and (d) general linear models for the description of based on clusters attributes. Cross-correlation revealed a negative correlation of with both elevation and latitude and a week positive correlation with longitude. The correlation between and elevation was maximized during the warm season, while the correlation with latitude was maximized during winter. The first two principal components accounted for the 97.9% of total variance of mean monthly . -means segmented Greece to 11 regions/clusters. The categorical factor of cluster number together with the parameters of elevation, latitude, and longitude described satisfactorily the through general linear models verifying the robustness of the cluster analysis. This research effort can contribute to hydroclimatic studies and to environmental decision support in relation to water resources management in agriculture.