Using linear regression method for predicting of UNESCO aridity index of Iran

The purpose of this paper is to discuss how prediction of UNESCO aridity index could be utilized to advance our understanding of climate change impact and of the potential for adaptation to climate change in the future of Iran. For this purpose, using linear regression method, we determined agricultural climatic indicators at 41 synoptic stations of the country under future climatic conditions under current conditions (2017) for 2025, 2050, 2075 and 2100. In linear regression method, the variables of precipitation, temperature (average, maximum and minimum), wind speed, relative humidity and solar radiation were evaluated. After investigating the slope trend of the mentioned variables for each station and then, classified using UNESCO aridity index. According to UNESCO aridity index, 85.36 percent of the stations were in the arid area in 2017 and 87.8 percent in the arid area since 2025 to 2100. According to these results, under future climatic conditions of the country, in terms of climatic indicators, the similarity between the stations will increase and the climatic diversity of the country will reduce compared to the current conditions.

Keywords:

Climate Changes , Drought , Meteorology , Zonation.