خلاصة:
Isohyetal maps of a region are a prerequisite for many hydrological and meteorological studies. The accuracy of isohyetal maps depends on the data interpolation method used for rainfall. Considering the complex topography of Khuzestan province and the lack of high-altitude meteorological stations with long-term statistics, determining a suitable method for interpolating monthly and annual rainfall data in this province seems necessary. For this purpose, seven interpolation methods including ordinary kriging, cokriging, kriging with external trend, regression kriging, inverse distance weighting, spline, and three-dimensional linear gradient were compared with each other. In variographic analysis of rainfall data, five semi-variogram models were fitted to the rainfall data. The evaluation of the methods was performed using the leave-one-out cross-validation method, and the selection of a suitable interpolation method was based on regression analysis, calculation of the root mean square error, and the mean bias error. The results of the variographic analysis showed that the spherical model is the best theoretical model of the semi-variogram. Also, the rainfall data in this region have a strong spatial structure in all months, except for the months with low rainfall. Analysis of the results showed that all methods except regression kriging suffer from underestimation errors in estimating high amounts of rainfall. By comparing the interpolation methods studied, regression kriging was identified as the most appropriate method for interpolating monthly and annual rainfall data. Also, with the selected method, the annual isohyetal map of the province was drawn, and from it, the average annual rainfall of the region was obtained as 391 mm, which is 41 mm more than the amount provided by the country's Meteorological Organization, which is due to the use of altitude as an auxiliary variable that was able to somewhat solve the problem of lack of high stations in the region. In addition, the results of the research showed that methods that use altitude as an auxiliary variable to estimate rainfall are more accurate than other methods.
ملخص الجهاز:
Considering the complex topography of Khuzestan province and the lack of high-altitude meteorological stations with long-term statistics in it, determining a suitable method for interpolating monthly and annual rainfall data in this province seems necessary.
(1383) by examining ordinary kriging, cokriging, weighted moving average, and spline methods, to estimate the spatial distribution of annual precipitation in arid and semi-arid regions of southeastern Iran, recommended the spline method with an auxiliary variable of elevation.
In this research, to determine the best method for spatializing rainfall data, seven interpolation methods including ordinary kriging, cokriging, kriging with external trend, regression kriging, inverse distance weighting, spline, and three-dimensional linear gradient were compared with each other.
Regarding the selection of the best method for interpolating annual rainfall data, the results of the regression analysis of this method (Figure 3) show that the linear equation obtained from regression kriging is closer to the 1:1 line compared to other methods, but the results in Table 3 indicate that the ordinary kriging method has the lowest estimation error among other methods.
(Refer to the page image) (Refer to the page image) Discussion and Conclusion In this research, to determine the best method for interpolating monthly and annual rainfall data in Khuzestan province, seven interpolation methods, including ordinary kriging, cokriging, kriging with external trend, regression kriging, inverse distance weighting, spline, and three-dimensional linear gradient, were compared with each other.
The selection of the regression kriging method as the best interpolation method for rainfall data in Khuzestan province indicates that using the auxiliary variable of elevation increases the accuracy of spatial estimates of rainfall data in this region.
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