SKLMState Key Laboratory of Magnetism (China)
SKLMSelective Kirchner's Liquid Medium (tuberculosis research)
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The residuals of rainfall in each weather station were calculated using this primary regression relationship, and the SKlm technique was applied.
In order to examine the applicability of the SKlm technique on 26 weather stations, this study applied and compared the Thiessen method and ordinary kriging (OK) method, which are general techniques in spatial interpolation.
The result of SKlm was expressed as spatial-interpolated values by DEM and the distances of fifteen stations.
45 m)) by applying Thiessen, OK, and SKlm techniques in sequence were compared with observed data and suggested in Figure 8.
According to the results of spatial interpolation from Figure 9 and Table 3, it was found that the SKlm technique reflects the geomorphologic characteristics (elevation) fairly well.
When these characteristics were considered, it was judged that the SKlm technique will have better applicability in the Korean peninsula, which has relatively smaller density in the number of weather stations in the area with high elevation.
In order to use the climate change scenario data in the scale of a basin, this study applied spatial downscaling techniques combining the ANN and SKlm techniques, which are believed to have better applicability, to the Namhan river basin.
As a methodology to address this issue, the ANN and SKlm techniques, both of which can use secondary data when performing spatial interpolation, were applied together and the results were compared to the Thiessen and ordinary kriging techniques, which are general downscaling techniques.
Based on the above results, the Thiessen, OK, and SKlm techniques were combined in the forecasting and comparison of future aerial average rainfalls of the Namhan river basin (Table 6).
Based on the results of Section 3.2, if the SKlm technique was the best technique in representing the rainfall spatial distribution characteristics of the Namhan river basin, it can be said that the existing spatial interpolation techniques for climate change impact evaluation were seriously underforecasting rainfall.