淡江大學機構典藏:Item 987654321/105861
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/105861


    Title: 應用雷達定量降水於水庫入庫流量推估模式之研究
    Other Titles: Studies on reservoir inflow estimating model by using the radar-based quantitative precipitation
    Authors: 郭秀筠;Kuo, Hsiu-yun
    Contributors: 淡江大學水資源及環境工程學系碩士班
    虞國興;蘇騰鋐;Yu, Gwo-hsing;Su, Teng-hung
    Keywords: 水庫入庫流量推估模式;逐步迴歸分析;雷達定量降水;Reservoir Inflow Estimating Model;Stepwise Regression Analysis;Radar-based Quantitative Precipitation Estimation
    Date: 2015
    Issue Date: 2016-01-22 15:07:07 (UTC+8)
    Abstract: 近年來，颱風及豪雨發生之頻率及強度日益增加，降雨季節分配不均情況更加嚴重，且防洪程度會因降雨強度增強而降低，使洪災發生機率提高。在颱風侵襲之過程中，利用颱洪事件之總累積雨量及不同平均降雨強度資料，以快速掌握水庫入庫流量，進而爭取水庫操作上之時效，提供操作人員最即時水庫調節之參考。
    本研究以石門水庫上游集水區為研究場址，針對水庫操作在不同調節放水之階段，利用颱風時期之總累積降雨量及不同平均降雨強度等降雨因子對水庫入庫流量之關係，分別建立洪峰前累積入庫流量、洪峰後累績入庫流量、總累積入庫流量及洪峰入庫流量推估模式。研究中利用逐步迴歸分析進行參數之篩選，以建立水庫入庫流量推估模式，以95%信賴區間檢定、交叉驗證確立模式之可信度，最後應用雷達定量降水資料進行驗證，確立推估模式之成效。
    結果顯示，經篩選後之場次作研究對象，以逐步迴歸分析所建立入庫流量推估模式，故挑選試驗場次可確立模式之成效。本研究發現4種入庫流量推估模式皆與總累積降雨量(P)有關，加入平均降雨強度(I)後，其判定係數R2值皆提升至0.85以上，故考慮平均降雨強度可提升模式之成效。本研究以交叉驗證結果顯示，僅3場颱風落於95%信賴區間外，其餘之30場皆落於95%信賴區間內，證明本研究所建立之水庫入庫流量推估模式可有效利用降雨資料進行推估。
    本研究應用雷達定量降水資料於4種水庫入庫流量推估模式中，其結果發現，洪峰入庫流量推估模式之驗證結果中，所有驗證場次皆落於95%信賴區間內，顯示洪峰入庫流量推估值為4種入庫流量驗證結果中最佳。
    In recent years, the typhoons and extreme rainfall days increases, the precipitation is unsteady in different places and during different seasons, and it usually concentrates in moist season. The typhoon causes results in severe flood inundation. During the typhoon hit, using the rainfall intensity data of typhoon flood events, to quickly grasp the reservoir inflow, it is expected that results of this study could be used for online reservoir operation in the future.
    In this study, we focus on the Shihmen reservoir upstream catchment area, using the relationship of rainfall factor and reservoir inflow to separately establish of cumulative inflow before peak flow, cumulative inflow after peak flow, total cumulative inflow and peak inflow estimating models. The study use the stepwise regression analysis to built model, with 95% confidence interval and cross-validation test the model, and finally using the radar-based quantitative precipitation estimation data verify the model.
    The results shows that using the stepwise regression analysis to establish reservoir inflow estimating model, the t-test are less than 0.05. The study found that four kinds of inflow are related to the total cumulative rainfall factor, and adding the average rainfall intensity factor, its coefficient of determination R2 value of 0.85 or more are up to. In this study, the cross-validation results showed that only three events are extreme value, the other 30 events of results are satisfactory.
    In this study, we use the radar-based quantitative precipitation estimation data in 4 reservoir inflow estimating models, the results shows that the peak inflow validation results is the four kinds of best estimate type.
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Thesis

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