Kriging is one of the most developed methodologies in the regional variablemodeling. However, one of its drawbacks is that the influence radius can notbe determined by this method. In which distance and in what ratio that pivotstation is influenced from adjacent sites is rather often encountered problemin practical applications. Regional weighting functions obtained fromavailable data consist of several broken lines. Each line has differentslopes which represent the similarity and the contribution of adjacentstations as a weighting coefficient. The approach in this study is called asSlope Regional Dependency Function (SRDF). The main idea of this approach isto express the variability in value differences γ and distancestogether. Originally proposed SRDF and Trigonometric Point CumulativeSemi-Variogram (TPCSV) methods are used to predict streamflow. TPCSV andPoint Cumulative Semi-Variogram (PCSV) approaches are also compared with eachother. Prediction performance of all the three methods revealed a relativeerror less than 10% which is acceptable for most engineering applications.It is shown that SRDF outperforms PCSV and TPCSV with very high differences.It can be used for missing data completion, determination of measurementsites location, calculation of influence radius, and determination ofregional variable potential. The proposed method is applied for the 38 streamflow measurement sites located in the Mississippi River basin.
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