完整而可靠的水文紀錄，為水資源規劃及運用之重要憑藉，因設站時間晚、災害造成的損害、經費的限制及其他自然、人為的因素等，使得水文資料記錄年限太短或中間有脫誤，無法有效評估水資源開發方案之水源運用及缺水特性，提高替代方案之風險。為了彌補水文紀錄的不足，有必要利用合適的序率模式繁衍與歷史統計特性相符之水文過程以延長水文資料年限。
本研究先應用Thomas-Fiering (T-F)模式繁衍月雨量序列，再推導修正Lane分配模式(disaggregation model)，應用其將時間上相對較大尺度的合成月雨量分配至旬雨量序列，並與Lane分配模式比較兩者所繁衍旬雨量序列在統計性質上之差異。
針對台灣北部新店溪上游北勢溪流域44年歷史雨量序列之統計特性，本研究利用T-F模式繁衍月雨量序列，其平均值、標準差、相關係數及偏態係數均與歷史頗為吻合，再分別以Lane分配模式及修正Lane分配模式繁衍旬雨量序列，經與歷史旬雨量過程比較其統計特性，結果發現雖然兩者之平均值、標準差及偏態係數尚符合歷史趨勢，但修正Lane模式之各項統計特性皆較Lane模式與歷史資料接近，而Lane模式的相關係數則與歷史資料有顯著差異。若未來在氣候變遷下，降雨量之時間序列特性仍受到傳統大氣的相同物理機制影響，則本研究提出之修正分配模式可應用於降尺度分析，得到正確合理的小尺度時間降雨量過程。

A complete and reliable hydrographic data is an important basis for water planning. Therefore, The lack of hydrographic data from human and nature factors including fund shortage, the lack of rainfall observation institution in the past, and un-precise data during nature disaster may result in a wrong analysis in hydrographic research and increase the risk of water planning. In order to overcome the lack of hydrographic data, using an appropriate mode to generate the similar historical date is necessary in our research.

In theory, we can use Thomas-Fiering (T-F) mode to generate the date of monthly rainfall, and combined with Lane disaggregation model and modified disaggregation model to get the data of ten days rainfall. By using Lane disaggregation model and modified Lane disaggregation model, we can generate two data and analyze the difference between them to get the information we need.

To get the 44 years statistics date of Peishih watershed in the north of Taiwan, the research used T-F model to generate the Mean, Standard Deviation, Coefficient of Correlation, and Skewness, finding these data is similar to historical monthly rainfall. The second step is using Lane disaggregation model and modified disaggregation model to generate ten days rainfall. Although both the two data from Lane disaggregation model and modified disaggregation model are similar to historical ten days rainfall, we need find out which one is better. By comparing the two data, we found the data from modified Lane disaggregation model is more close to ten days historical rainfall than Lane disaggregation model. As a result, the data generated from modified Lane disaggregation model in the research can be used to appropriately forecast ten days rainfall in the future if the atmosphere environment is similar.

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