應用氣候統計降尺度預報資料推估石門水庫入流量;Apply Statistical-Downscaling Climate Forecasts for Estimating Shihmen Reservoir Inflows

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Title:	應用氣候統計降尺度預報資料推估石門水庫入流量;Apply Statistical-Downscaling Climate Forecasts for Estimating Shihmen Reservoir Inflows
Authors:	韓宛容;Han,Wan-rong
Contributors:	水文與海洋科學研究所
Keywords:	流量預報;統計降尺度;氣候預報;石門水庫;Climate forecasts;Shihmen Reservoir;Statistical downscaling;Flow forecasts
Date:	2012-06-28
Issue Date:	2012-09-11 18:25:55 (UTC+8)
Publisher:	國立中央大學
Abstract:	水庫為台灣重要的水資源調配設施，也是幫助防範水文災害的重要緩衝器，近年來因為經濟蓬勃的發展導致民生與工業用水需求逐年遽增，石門水庫運用相當頻繁，造成相關管理單位之供水壓力尤其是乾旱時期大為增加，倘若能事先掌握未來入流量狀況，便能提供決策者進行供水操作決策與乾旱預警之參考。目前中央氣象局所提供氣候統計降尺度預報產品，為於每月月底預報未來五個月逐月雨量以及溫度狀態，本研究主要目的即為結合此預報系統，探討其可行之使用方法，並透過氣象資料產生器以繁衍未來五個月可能的日雨量及日溫度資料，投入水文模式進行集水區流量推估，進而提供機率預報和定量預報兩種流量預測資訊，且評估兩預報方法之經濟效益。本研究利用最大機率法則、機率加權法則、降尺度偏差修正值三種氣候統計降尺度預報產品取樣策略，以氣象資料合成模式(WGEN, Tung and Haith, 1995)繁衍水文模式所需之日溫度及日雨量資料，並以技術得分(RPSS、LEPS、MSE)評估流量機率預報能力，結果顯示三種取樣策略皆有大於氣候預報之能力，其中以最大機率法則下有最佳的流量預報能力。而機率和定量兩種流量預報方法亦皆有高於氣候預報的能力，其兩者於六月至十月評估結果中有相似的經濟效益，但在一月至五月評估結果則是以定量預報於實際入流量發生偏低區間時有較廣的經濟效益範圍；以機率預報於實際入流量發生偏高區間時中有較廣的經濟效益範圍。Resources in Taiwan not only are impotant for water resources management, but also paly as retention measures against flooding. In recent years, the need for domestic and industrial water have increased rapidly because of economic vigorous development, which result in rising stress of water supply especially in drought periods. Therefore, if reservoir inflows can be quantitatively forecasted beforehand, it will be helpful for issuing drought wqrning and making properly decision for water allocations. The Central Weather Bureau (CWB) issued short-term climate forecasts by statistical downscaling for precipitation and temperature with lead time of 5 months in a 1-month moving window. The objective of this study is to apply the short-term climate forecasts by integrating with a weather generator and a watershed hydrological model to predict inflows of the Shihmen Reservoir with the maximum lead time of 5 months. Both probabilistic flow forecasts and deterministic flow forecasts were produced in this approach, as well as the associated potential economic values of two flow forecasts. The sampling techniques, including maximum probability, weighted probability, and bias correction probability, were applied to retrieve monthoy mean values of precipitation and temperature from the climate forecast. Then a weather generator was applied to generate daily temperature and precipitation to drive a hydrological model for inflow predictions of the Shimen Reservoir. The skill scores (RPSS, LEPS and MSE) of three sampling results were all greater than climatology skill. The maximum probability approach has the highest predictive ability. Results of both probabilistic flow forecasts and deterministic flow forecasts are also greater than climatology skill, and show certain economic values from June to October. From January to May, the deterministic flow forecasts possess greater economic benefits than that of the probabilistic flow forecasts for cases of observed inflows at blow normal outlooks; while, the probabilistic flow forecasts possess greater economic benefits that than of the deterministic flow forecasts for cases of observed inflows at above normal outlooks.
Appears in Collections:	[Graduate Institute of Hydrological and Oceanic Sciences] Electronic Thesis & Dissertation

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