Evaluation of co-kriging different methods for rainfall estimation in arid region (Central Kavir basin in Iran)

Abstract

Rainfall is considered a highly valuable climatologic resource, particularly in arid regions. As one of the primaryinputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologicmodeling. Precipitation is known only at certain locations; interpolation procedures are needed to predict this variablein other regions. In this study, the ordinary cokriging (OCK) and collocated cokriging (CCK) methods ofinterpolation were applied for rainfall depths as the primary variate associated with elevation and surface elevationvalues as the secondary variate. The different techniques were applied to monthly and annual precipitation datameasured at 37 meteorological stations in the Central Kavir basin. These sequential steps were repeated for the meanmonthly rainfall of all twelve months and annual data to generate rainfall prediction maps over the study region. Aftercarrying out cross-validation, the smallest prediction errors were obtained for the two multivariate geostatisticalalgorithms. 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 werecompared to select of the most accurate method (AE close to zero and RMSE from 0.53 to 1.46 for 37 rain gaugelocations for all months). The exploratory data analysis, variogram model fitting, and generation precipitationprediction map were accomplished through use of ArcGIS software.