全球受氣候變遷之影響，極端水文事件發生之頻率加劇，無論是高強度之極端暴雨颱洪事件或低流量乾旱事件皆造成頻率分析上之困擾。以傳統之線性動差法（L-moments）進行高極端水文頻率分析時，容易對極端事件之重現期產生不合理之推估結果，因此發展高尾線性動差法（LH-moments）有其必要性，針對分布高尾端之部分加強其權重，使推估之參數更適用於極值部分。LH-moments方法結合三參數之通用極端值分布（Generalized Extreme Value, GEV）進行頻率分析，以提供洪水或暴雨等極端水文事件資料之應用。 本研究針對LH-moments方法結合GEV分布，應用於極端暴雨之頻率分析，加強權重 使高重現期之推測較為合理，並以資料之形狀參數作為判斷資料之指標，提出建議權重 以得到較為合理之重現期，並作為後續資料應用分析之參考。 Under global climate change, the frequency of extreme hydrological events such as floods and droughts has increased, complicating frequency analyses. Reasonable estimates of return periods for extreme events cannot be obtained through the use of traditional L-moments method in frequency analyses. Instead, LH-moments method which assigns higher weights to data at extreme ends of the distribution should be applied for parameter estimations. In this research, the LH-moments method is applied in the frequency analysis of extreme rainfall events, in which rainfall data are fitted to 3-parameter generalized extreme value (GEV) distribution model. The weighing factor (m) for data at extreme ends of the distribution is raised in order to obtain better estimation of the return periods. The value of the shape parameter κ is used to determine the weighing factor m, so the most reasonable return period can be obtained. The value of the shape parameter κ would also serve as a reference for follow-up data analysis.
