| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 105 === Downscaling is used to translate the general circulation model outputs to a finer spatiotemporal resolution rainfall and temperature data, which is essential for assessing hydrological impacts of climate change. However, existing downscaling models for hourly temperature are unable to consider the inter-daily connection and the diurnal cycle and consider the hourly scale. Besides, existing rainfall-runoff models have less attention on hourly streamflow. To address this problem, a spatiotemporal downscaling model is proposed which is capable of reproducing the inter-daily connection, the diurnal cycle, and the statistics on daily and hourly scales. Using the results of rainfall and temperature as input to the rainfall-runoff model, the hydrological impacts of climate change on hourly streamflow are assessd.
The proposed downscaling model consists of two steps, the spatial downscaling and temporal downscaling. The spatial downscaling is applied first to obtain the relationship between large-scale weather factors and daily temperature at station scale using the k-nearest neighbor method. Then, the hourly downscaling of daily temperature is conducted in the second step using the k-nearest neighbor method with the genetic algorithm and consideration of the inter-daily connection and the diurnal cycle. The large-scale datasets, which are obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data and the Bergen Climate Model version 2 (BCM2.0) outputs, and the local temperature data are analyzed to assess the impacts of climate change on temperature. After rainfall and temperature projections, the HBV model are applied to predict the future streamflow of Shihmen reservoir basin (Taiwan) under the A2, A1B and B1 scenarios of the BCM2.0 for the periods 2046-2065 and 2081-2100.
The results show that the proposed model can accurately reproduce the local temperature and its statistics on daily and hourly scales. The change trend of the future streamflow under the impacts of climate change is presented and discussed. To conclude, the results demonstrate that the proposed model is a powerful tool for predicting streamflow. The future changes of temperature and streamflow statistics are obtained which will help decision makers in planning and management of water resources systems.
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