| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 99 === Daily precipitation data are frequently required for many hydrological applications. In this study, based on self-organizing map (SOM), a daily precipitation generator is proposed. The large-scale atmospheric variables simulated by General Circulation Models (GCMs) are used as predictors to estimate the local-scale precipitation under different scenarios. The proposed generator consists of two parts: the SOM-based weather type classification and the precipitation generation. Firstly, the atmospheric variables that have great influence on precipitation are clustered using SOM to indentify different weather types. The statistics of each weather type (the frequency, the spell duration, and the weather-type transformation probability) are calculated. Secondly, for each weather type, the statistics of corresponding daily precipitation data (the probability of precipitation and the average wet-day precipitation amount) are also calculated. Thirdly, according to the aforementioned statistics, the precipitation generator is constructed. Additionally, in order to apply the proposed generator to assess the impact of future climate change on precipitation, two different conditions are considered herein. For the first condition, the statistics of each weather type will change, but the statistics of precipitation in each weather type will remain the same in the future. For the second condition, both the statistics of each weather type and those of precipitation in each weather type will change in the future. For each condition, the proposed generator is used to generate future daily precipitation according to the future simulations of atmospheric variables from GCMs. An actual application to the Shi-Men Reservoir Watershed is conducted to evaluate the performance of the proposed generator and to assess the future variation of precipitation. Results show that the synthetic daily precipitation data generated by the proposed generator can preserve the statistical characteristics of the observed data, and hence the proposed generator can be used to generate the local-scale precipitation. As to the future precipitation in the study watershed, for both aforementioned conditions, the precipitation will increase in winter and summer and decrease in spring and fall.
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