Mohamad Darand; Behrooz Ebrahimi
Abstract
To doing this research daily precipitation data from 162 synoptic, climatic and rain gauge stations in and out of province during 21/3/1961 to 31/12/2012 extracted from Kurdistan Regional Water Company and meteorology organizations. By geostatistic Kriging method daily precipitation interpolated on 6×6 ...
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To doing this research daily precipitation data from 162 synoptic, climatic and rain gauge stations in and out of province during 21/3/1961 to 31/12/2012 extracted from Kurdistan Regional Water Company and meteorology organizations. By geostatistic Kriging method daily precipitation interpolated on 6×6 kilometers and one digital map has been created for each days. Then data over province on the 811 pixels that covers whole of province extracted. A database was created in dimensions of 18914×811with time (day) on the rows and pixels (place) on the column. The average, high and low hresholds and standard deviation of waiting time duration calculated for each pixel during different months. To detection thresholds the t-student test has been applied. The thresholds calculated in 99% confidence level. The results showed that Mountains features have important effects on precipitation waiting time duration. The different precipitation waiting time duration observed over Kurdistan province during different months. The distribution of precipitation waiting time during the different seasons of the year shows route of Rain-bearing systems on Kurdistan province. In total, the cores of minimum precipitation waiting time are located on the North-West of province in spring, on the North and North-East of province in summer, and on the North-West and West of province in autumn and winter. The shortest and most prolonged precipitation waiting time is related to the months of February and September respectively. In February on the part of the western and northwestern parts of Kurdish province precipitation waiting time duration is about 3 days. While waiting period in September on the mentioned areas is more than 60 days.
Haji Karimi; Hasan Fathizade
Volume 19, Issue 53 , September 2015, , Pages 277-297
Abstract
Abstract
Selection of an optimal interpolation method for estimating the characteristics of the not-sampled points was the main aim of this study, due to the important role of data management. In this study, ordinary Kiriging interpolation models including linear, exponential, spherical, Gaussian were ...
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Abstract
Selection of an optimal interpolation method for estimating the characteristics of the not-sampled points was the main aim of this study, due to the important role of data management. In this study, ordinary Kiriging interpolation models including linear, exponential, spherical, Gaussian were used to estimate the mean annual rainfall of Ilam Province. For this purpose the normality of the data was checked using the Kolmogorov-Smirnov method and then the variogram of each model was calculated and plotted. In continuation, the best spatially fitted variogram between the data was used being compared to the other variograms. For this purpose, the relation between the piece effect and the roof of variogram was used (Co+C). According to the parameters obtained from the fitted variograms, the Gaussian variogram with the 0.33 best fitted the correlation between the data and was used for interpolation. In order to evaluate the efficiency of employed models, the root mean square error (RMSE) and the standard error of results were used. The results showed that the Gaussian Variogram having the lowest estimation error (6.12) and root mean square error (166) were the best model for the interpolation of the data in this investigation. Furthermore, comparison of RMSE with Standard Error (SE) for calculating the amount of expectations demonstrated that the four models gave overestimations.
