| Summary: | 碩士 === 國立中興大學 === 水土保持學系所 === 98 === In these recent years, because of global warming influence, the climate became more anomalous and hydrology events became more extreme, it caused rainfall distribution in Taiwan area to be more unbalanced, and brought about an extremely severe disaster. In search of the most suitable ways for rainfall spatial distribution in Shei-Pa National Park, this research will take Shei-Pa National Park as the boundary, and use Spline , IDW(Inverse Distance Weighted), Kriging method to estimate the distribution of rainfall space, also compare the merits and demerits of these three methods, and find out the most suitable analysis method that used in this area and Hydrological observation station where is located at severe shortage area.
This study will divide the rainfall into long time annual rainfall and short time daily cloudburst, and will be discussed separately, (1) for annual rainfall side, collect in the study area of annual rainfall in year 2000, and estimate its annual rainfall space distribution by using those three methods above, and also use geographical information system and traditional Thiessen Polygons method to calculate the average of annual rainfall volume, for comparing its results. (2) For cloudburst side, collect in the study area of the highest accumulated rainfall volume for 24, 48, 72 delayed rainfall volume, and also use the most extreme value of Gumbel type I distribution method and Log-Pearson Type III distribution method to analyze among 5, 10, 20, 50, 100 rainfall frequency for each year, and also take this rainfall volume as property value, and use those three methods above to estimate the distribution of cloudburst space. (3) Finally, using RMS to analyze annual rainfall and cloudburst space distribution result, and also find out the most suitable method that used in the distribution of rainfall space, and analyze DAD(Depth Area Duration) of this area and also K, n value of Hortan formula.
The conclusions of this study are :
(1). IDW(Inverse Distance Weighted) and Kriging method are better used in analyzing of annual rainfall space distribution, but the average of annual rainfall volume that analyzed by three methods are almost same, but it is more accurate than the results of analysis by Thiessen Polygons method.
(2). Kriging method is better used in analyzing of cloudburst space distribution, its average relative error is only 7%, while by using those two other methods it will be 14%.
(3). Although IDW(Inverse Distance Weighted) method is better than Spline method in annual rainfall space distribution, but the results of cloudburst space distribution are the worst, conform to Gotway and other people theory, only the stability of IDW(Inverse Distance Weighted) method is bad, and can be easily influenced by the factors.
(4). The annual rainfall space distribution uses 11 sample size, while cloudburst space distribution used 14 sample size, the both sample sizes are different, and cloudburst space distribution relative error is the lowest(only 10%), we can know that if sample sizes are more, the results will be more accurate.
(5). Kriging method result is the best after using RMS analysis method.
There are two suggestions :
(1). The average relative error is only 13.3% by using Kriging analysis method, while by using RMS analysis method, its residual is 76. Therefore, this study suggests using Kriging analysis method in annual and daily rainfall space distribution analysis. (2). When cloudburst is analyzed by using Kriging method, it can cause the boundary of estimate will be wider, thus cloudburst frequency analysis should use Log-Pearson Type III distribution method.
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