|dc.description.abstract||Climate abnormality still is an issue under defining until twentieth centenary. But natural environment continued to reveal some information about it recently. Experts in various fields began to admit the phenomenon due to heavy greenhouse gases emissions. So the Framework Convention on Climate Change (FCCC) signed in Rio, Brazil in 1992 and “Kyoto Protocol” signed in Kyoto, Japan in 1997, both are the agreements for controlling in principle the CO2. Then, the report of Intergovernmental Panel on Climate Change (IPCC, 2001) told that it is impossible to reduce CO2 emissions to double (=280ppm) of quantity before Industrial Revolution in twenty-first centenary. The effect of climate change will keep on going in this centenary.
Yearly precipitations can produce over 90 billions cubic meter of water in Taiwan. Because of high density of population, limits of the geography, different locality and season, everyone in Taiwan can use water within limits. However, the changes of industrial types make the unbalance for supply and demand of water resources. In addition to all of the above, the allocation of water resources becomes extreme important under climate change. The motivation of this study is developed from the above reasons. Generally analyzing time series data, Fourier analysis would be applied. Moreover, the consequences from climate change block to utilizing Fourier analysis. This study tried to apply wavelet theorem for making sure the information of data.
The mechanics of wavelet transfer was developed by Morlet in France. It not only can investigate the stable data, but also treat unstable and non-periodic data. From series, it can find the trend, localities, abnormal discontinuities…etc. First, this study applied wavelet theorem to analyze the data of the CWB 21 stations from 1971 to 2003. The climate parameters to examine are temperature, precipitation, relative humidity, and the numbers of rainy days. From results, we can get differences and trends of the data. Second, we made the comparisons between the northern of Taiwan and Shin-Men basin from analyzing data. We tried to look for the best rule curves under climate change, and confirmed that rule curve could operate in reality. The main fruits form this study include that:
1. This study made a long term analysis on climate data to establish the trends of series. From wavelet applications, we got some hidden information of the time series, and the change of the climate period in the future. All of the information is able to aid policy-making and the allocation of water resources.
2. With the comparisons of the differences, it is easy to be showed local results in various regions. The linear regression is not appropriate for analyzed data, because the R-square is bad. Hence, with grey generating, we can see the trends of the changes, and predict the next value in the series into real operations.
3. We received better result to observe the climate data by means of wavelet transfer and multi-resolution analysis. There is much useful information displayed in all of random and confused dada after data processed. Those outputs can be referred to the decision-makers for observe long term trends and period changes.
4. In this study, we discussed the functions of the rule curves individually. By different trials, we can understand the influences of the modified rule curves. The conclusions may be supplied to others to simulating reservoir operation or adjusting local rule curves.
5. The concept of “flexible rule curve” can be worked in real situations form conclusions. In times of climate change, particular operations must be needed. To combining precipitation and rule curves is an interesting topic that is worth of researching.
6. The period of a reservoir operation should change from “year” to “season” from our conclusions in Shin-Men reservoir. Other reservoirs can change theirs depending on real precipitations. Then, flood operation in reservoir can add recurrent neutral network into consideration and better ends will be expected with this framework.||en_US|