Identification of Snowfall Areas in Iran using Cluster Analysis

Document Type : Research Article

Authors

1 Assistant Professor of Climatology, University of Isfahan, Iran

2 M.A Graduate of Climatology

Abstract

Snow is one of the main sources of water budget, spring currents, groundwater aquifers, rivers, springs and it's important in mountainous areas and high latitudes. Due to the importance of this topic, the present study was conducted with the aim of identifying and clustering of snow in Iran. In this regard, present weather codes of 304 synoptic stations were surveyed during the period of 1951-2010. Initially, 16 codes that belonged solely to the snow were separated and then spatial distribution maps of each code were mapped and spatially surveyed. The results indicate that snowfall occurs only in 251 stations and their density is mainly in the north-west and south-east axis and the mountain axis of Alborz and Zagros chain. The codes 70, 71, 73, 22 and 72 respectively have the highest frequency of recording at the stations studied. Finally, by applying a hierarchical cluster analysis on the percentage of snow frequency, the stations were divided into three main clusters. This analysis showed that synoptic stations in Iran are mainly classified into three main clusters, High Mountain, low mountains and foot of the mountain clusters. It should be noted that the third cluster is distinguishable due to its large dispersion to two submerged subsurface and low-altitude mountain and coastal areas. Studies have shown that snowfall in Iran is strongly dependent on elevation and latitude. There is a significant inverse relationship between the elevation and latitude of the stations. On the other hand, there is a significant direct relationship between height and percentage frequency of snow. However, the percentage frequency of snowfall with latitude is not significant. This indicates that elevation in snowfall is more important than latitude.

Keywords


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  • Receive Date: 23 August 2017
  • Revise Date: 27 October 2017
  • Accept Date: 09 December 2017
  • First Publish Date: 22 May 2018
  • Publish Date: 22 May 2018