| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 106 === In Taiwan, utilization of water resources mainly relies on the reservoirs. Although the precipitation is abundant, lots of water is unavailable due to steep landform and spatial-temporally unequal distribution of rainfall. Besides, the quality the probabilistic forecasts often used, for example deterministic forecast, probabilistic forecast or ensemble forecast, would strongly affect decision-maker to make the operation policy. For instance, the influence of inflow forecast in reservoir operation. Many methods are proposed to adjust the accuracy of forecasts. However, forecasts are more inaccurate because of the impact of extreme events occurring. Although forecast verifications could follow reliability diagram or Bayes'' theorem - posterior probability, the limitation still exists due to severe climate change.
This study aims to give the uncertainty of probabilistic forecast in reservoir operation before events occurring. The method estimating the uncertainty based on two concepts – Order Statistics Method and Return Period. The concepts are the random sampling of Binomial Distribution and the relationship between hydraulic coefficients and return period calculated by statistics characteristics of hydraulic events. The Hypothesis test would be proposed, and the influence in the operation would be considered. With type I error and Type II error, the cost of the forecast would increase. However, the cost would get lower when forecast approaches demand target because considering uncertainty beforehand. Next, we apply the uncertainty to hedging rule. The operating results represent that under the existing of future uncertainty, operating cost tends to decrease under the low water level, especially in drought. Moreover, the uncertainty is applied to a practical operation for Shihmen reservoir during drought. The efficiency of considering the uncertainty of probabilistic forecast is evident when the water level is low. Besides, the cost would reduce apparently when during each water level and inflow with proper uncertainty estimate. With the increasing frequency of extreme events during drought, the accuracy of the probabilistic forecast is not high as before. The concept of the uncertainty of probabilistic forecast provides the decision maker a different aspect of treating the future uncertainty. As a result, the total loss would be diminished.
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