| dc.description.abstract | In storm-induced landslide study, rainfall capacity is an important triggering factor. A direct way to know rainfall value at any point in the study area is interpolation from the rainfall observation gauges. Because the density of gauge stations is commonly inadequate, many different interpolation methods were used for estimate the spatial distribution of rainfall. In this study, we test regression kriging (RK), and compare the effectiveness of this new method with other existing interpolation methods.
In this study, we collect rainfall data during the typhoon Matsa from the Water Resources Agency, Taiwan and from the Central Weather Bureau, Taiwan. These data were visually examined and errors were fixed. Good quality data were processed to extract total rainfall and maximum hourly rainfall values. Inverse square distance method and kriging method were used for comparison with two multivariate geostatistical algorithms: RK_1D and RK_trend. RK system uses the rainfall value as primary variable and the elevation as auxiliary variable. Because the rainfall values and the elevations have a correlation coefficient only about 0.26, the auxiliary variable cannot effectively improve the rainfall estimation in RK_1D. To solve this problem, we tested RK_trend. A hypersurface which incorporates locations (x, y) and elevation (z) was used to describe the drift of rainfall values. The results find that the RK_trend method shows better rainfall spatial distribution and smaller estimation errors than that of other methods.
The performances of the four interpolators were further examined by cross-validation method. Results confirm that the errors estimated from various geostatistical methods do have reliability, especially for the maximum hourly rainfall case. Although cross-validation result indicates kriging method provides the smallest mean absolute error, however when two rain gauge stations are close, and the rainfall values as well as the elevations are similar, RK_trend method provides the smallest mean absolute error and indicates less bias.
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