Evaluation of Different Cokriging Methods for Rainfall Estimation in Arid Regions Central Kavir Basin in Iran

Rainfall is considered a highly valuable climatologic resource, particularly in arid regions. as one of the primary inputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologic modeling. precipitation is known only at certain locations; interpolation procedures are needed to predict this variable in other regions. in this study, the ordinary cokriging ock and collocated cokriging cck methods of interpolation were applied for rainfall depths as the primary variate associated with elevation and surface elevation values as the secondary variate. the different techniques were applied to monthly and annual precipitation data measured at 37 meteorological stations in the central kavir basin. these sequential steps were repeated for the mean monthly rainfall of all twelve months and annual data to generate rainfall prediction maps over the study region. after carrying out cross-validation, the smallest prediction errors were obtained for the two multivariate geostatistical algorithms. the cross-validation error statistics of ock and cck presented in terms of root mean square error rmse and average error ae were within the acceptable limits for most months. then the two approaches were compared to select of the most accurate method ae close to zero and rmse from 0.53 to 1.46 for 37 rain gauge locations for all months . the exploratory data analysis, variogram model fitting, and generation precipitation prediction map were accomplished through use of arcgis software.