Modeling of Precipitation-Elevation Spatial Relationships in the Northeast of Iran by Using the GWR Model

Precipitation is one of the most variable climatic parameters. These changes occur both in terms of location and time in terms of the region's climate. This study was conducted to model the spatial relationships of seasonal rainfall in the northeast of the country with a joint monthly statistical period of 30 years (1980-2010).

In order to achieve spatial variation of rainfall, new methods of spatial statistics such as spatial autocorrelation, global Moran, spatial dispersion index and geographic weight regression model (GWR) were used in GIS software.

The results of this study showed that rainfall changes in northeastern Iran have a high cluster pattern or positive. The Global Moran Index for each of the four seasons and the annual sum is above 0.93, the highest Global Moran index with the value of 0032191 is for the summer season.

The results of the GWR model showed that rainfall in the northern parts of the study area had positive spatial auto-correlation and in the southern parts, which are mostly desert areas had negative spatial auto-correlation. Also, the results of dispersion data were the result of cluster pattern of precipitation in the northeast of the country. Based on the frequency index of clusters or the ICF, the winter season is the largest cluster with a numerical value of 2646.26 in Northeast of the country.