How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed

Abstract

ABSTRACT Accurate spatial estimation of precipitation is central to hydroclimatic studies. In this study, deterministic and probabilistic inverse distance weighting (IDW) methods were utilized to provide spatial estimations of precipitation. To improve their accuracy, a modified version of IDW was developed involving a separation of IDW weights in the X-Y plane and Z direction. Moreover, four different weight functions were used to investigate how changing the pattern of the weights relative to the distance and elevation affects the accuracy of the interpolation. Finally, four IDW variants were implemented for spatial modelling of precipitation in the Central Plateau watershed located in Iran, using monthly data from 582 raingauge stations during 2005 to 2015. Based on the statistics, the modified IDW methods were about 8% more accurate than the standard IDW methods. Additionally, correlated triple collocation (CTC) analysis showed that the modified models resulted in 12% smaller root mean square errors in comparison with the standard models.