Assessment of three spatial interpolation models to obtain the best one for cumulative rainfall estimation (case study: Ramsar District)

Abstract

There has been an increasing use of predictive spatial distribution of rainfall patterns for planning and regional management decisions. This study focused on three interpolation techniques including ordinary kriging module, linear regression method and inverse distance weighted (IDW) that were used to obtain the reliable spatial distribution of cumulative rainfall parameters in the study area. First, we selected twenty two meteorological stations in and around the study area. Second, rainfall data were analyzed in geographical information systems (GIS) to determine the accuracy of three models. Third, the distribution pattern was also validated by field investigations. Finally, the reliability map of rainfall was produced in GIS software. The results demonstrate that linear regression significantly performed better than inverse distance weighted (IDW) and ordinary kriging model.