Author(s): Ildiko Pelczer; Judith Ramos; Ramon Dominguez; Fernando Gonzalez
Linked Author(s): Fernando Salazar González
Keywords: Clustering; Hierarchical algorithms; Non-hierarchical algorithms; Regionalization
Abstract: This paper applies cluster analysis to achieve a regionalisation of the Sonora River Basin in the Sonora State, Mexico, into homogeneous zones. The identification of homogeneous zones is fundamental for the study of the climatic variability throughout the Basin. There are a lot of researches looking at this topic to analyse the frequency of rain and flood events, some of them attempt to analyse other variables, which can be very significant in the definition of similar areas. In order to get a good regionalisation it is important to answer two questions: 1) how many clusters are required according to the type of data or objective and 2) how good is the clustering itself. To answer these questions, hierarchical and non-hierarchical algorithms were applied to six experiments based on the data sets for precipitation and temperature available from traditional weather stations. In order to validate the results, four indices applicable for both types of algorithms were applied. Experiments showed that better results were achieved when considering several variables than analysing each parameter alone. Also, it was observed that working with average values could mask maximum and minimum values that can influence the climatic variability. The validation provided as conclusion that one index by itself did not give enough information to determine which method is the best one. A combination of indices offers a better basis about the adequacy of the clustering method. Finally, it is also important to know the conditions at the surface, to find out if the regionalisation obtained is reliable. By comparing results from the cluster analysis with ancillary data, it is showed that the K-means algorithm was an effective method to achieve climatically homogeneous zones in the Sonora River Basin.
Year: 2007