Spatial Modeling of Annual Precipitation in Iran

Due to deep, complex and everlasting interaction between precipitation and climatic elements-factors, there are changes and varieties in both time and space dimensions of precipitation. So that climate experts and related scientists take their attentions to this phenomenon. An approach to do this kind of investigations is to describe spatial variations based on spatial statistics. The major spatial non-stationary of Iran precipitation is due to variation in situation, elevation and topography characters (slope and its direction) in this country. Circumstances of every one of these characters could determine the precipitation spatial patterns. Accordingly understanding spatial distribution of precipitation and its mechanism are important aspect in climatological researches. One of the common statistical models in which it is possible to determine the relation between variables as well as reconstruct, estimating and forecasting data is multivariate regression model. These sorts of models are useful for time series analyses as well as spatial modeling. One of the regression models that could be used in spatial analyses is called Geographically Weighted Regression (GWR). In current study it will be attempted to introduce this approach and using General Regression (GR) to justify spatial variation of precipitation in Iran based on 1436 stations in Iran.Research Methodology In this research Esfezary data base have been used. This daily data based contain 15998 days and 7187 pixels (15*15 KM) of precipitation over Iran. Accordingly the data matrix is created in 15998* 7187 and S-mode dimension. This matrix data base is estimated by using 1436 stations and Kriging method. To achieve independent variables, digital elevation map by 15*15 KM resolution has been created. So that, spatial (including longitude and latitude) and topographic (including slope magnitude and aspect) characters have been derived. Accordingly a data base has been created that contain spatial characters, topographic features and precipitation amounts.