Evaluation of Different Spatial Interpolation Methods for IMERG Precipitation Zoning in Neyshabur Basin

Direct measurement of continuous spatial data is almost impossible due to the limitations in data collection. To address this issue, many methods have been proposed for data interpolation. Determining the most suitable interpolation method to describe the amount of precipitation at any point on a certain time scale and in a specific region has been the concern of many researchers. In this study, different spatial interpolation methods were evaluated for predicting the rainfall zoning of IMERG satellite products in Iran's Neyshabur basin for a statistical period of 17 years. For this purpose, 11 interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (OK), and Universal Kriging (UK) with different techniques were used. Regarding the results of statistical indices for the UK method with quadratic drift (with Bias = -2.85, CC = 0.98, KGE = 0.97, and RMSE = 8.1), this method was the best interpolation technique for zoning rainfall in the Neyshabur basin. Finally, the choice of interpolation method depends on the spatial scale, density of stations, and variability of the data.