Modifying the soil moisture model of Palmer drought severity index for irrigated areas and its validation using satellite data

Palmer drought severity index is one of the most important agricultural drought indices which considers the relationship between climate change and agriculture conditions. This index is based on supply and demand soil moisture model. Hence, improving both the soil water model equations and its spatial resolution increases the efficiency, and provides more accurate estimates of the Palmer drought index and thus will improve drought management.  After correction and coding the soil moisture model equations based on Palmer’s drought index using MATLAB, the model was run for a period of 10 years in Isfahan province (1998 to 2007) with a spatial resolution of 1 km using station temperature and precipitation data and also soil (AWC) and satellite data (TRMM) as well .As a result, the amount of moisture in the soil to a depth of 1 meter was extracted in 120 layers; each layer contains the spatial distribution of monthly mean soil moisture. Also, in order to control the impact of changes in the model for irrigated areas, soil moisture from the Palmer index values ​​and soil wetness index (SWI) obtained by the satellite images were compared. The results of this comparison show that the solidarity of modeled soil moisture and SWI in irrigated areas dramatically increases after correcting the model (R² increases from about 0.14 to 0.30 in Dry period and from 0.19 to 0.50 in wet period).