Improving Rainfall Maps using Precipitation Products of IMERG V06 Final Satellite and Interpolation Methods

Aims: This study aims to improve precipitation maps and generalize precipitation to areas without stations. Materials Methods: In this study, to improve precipitation maps and increase the accuracy of precipitation maps, linear, multiple regressions and kriging subsets were used. The data from 14 meteorological stations and IMERG images in 20 years (2001 to 2020), a digital elevation model, and latitude and longitude maps of the Kermanshah watershed were used. At first, based on regression in Minitab software, the relationship between air and terrestrial variables was taken. Finally, with the interpolation methods and based on the error coefficients, the best equation for predicting precipitation was determined, and the spatial distribution of precipitation was obtained. Findings: According to the results, six out of 13 models were selected because of low RMSE and high R2, R, and NS. Forecast accuracy was reduced in regression models where only one climatic or edaphic variable was used. However, in the models used in the regression elevation, longitude, latitude, and IMERG variables in combination with interpolation methods, the extracted data matched the actual data with a slight difference. In this study, instead of the average of the input variables, the maps of each variable were used, increasing the forecast model’s accuracy to R2=0.8. Conclusion: The results showed that combining satellite precipitation products with interpolation methods led to a more accurate estimate of precipitation in the points without recording data will be precipitated and the multiple regression method will be more accurate than the linear gradient.