Zoning of minimum and maximum temperatures in Iran using multivariate regression

Temperature has a key role on human activities and natural processes. Due to Iran's vast and diverse climate and diverse spatial distribution and fluctuations of temperature, defining temperature zones is necessity in different sectors of industry, civil engineering and agriculture. Due to the country's climate variation and existence of different temperature gradients in different regions, in this study, the minimum and maximum temperature evaluation equations were investigated separately in six major agro-climate regions of the Iran. Minimum and maximum temperature data were obtained from 184 synoptic stations (from 2002 to 2016). The pattern used to extract the elevation of the areas (in the country) using the digital elevation data from SRTM satellite products with a precision of 90 meters was used. Maximum and minimum temperature data were used as dependent variable and elevation, latitude, longitude, data were used as independent variable in regression analysis and an independent relationship was established for estimating maximum and minimum temperatures and the calculations were done using SPSS software, multi-variable stepwise regression analysis. It was found that regression equations for maximum temperature modeling have a better performance than the minimum temperature. The equations obtained in each area are applied to the whole area and the peripheral buffer. Finally, with the addition of regions and averaging of buffer zones, maximum and minimum temperature maps were created for the whole country.