推求時雨量之空間–時間統計降尺度模式

A novel spatio-temporal statistical downscaling model for hourly rainfall

王千輔(Chian-Fu Wang)

指導教授 ： 林國峰

國立臺灣大學/工學院/土木工程學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201602826

摘要

空間及時間解析度精細的降雨資料，對氣候變遷在中、小流域的水文衝擊評估至關重要。然而，現存的模式較少關注跨日關係(inter-daily connection)及日夜循環(diurnal cycle)等，這兩個與水文循環緊密相關的統計數據。因此，本研究提出一個新的空間–時間統計降尺度模式，可以重現跨日關係、日夜循環，及日尺度和時尺度的統計特性，以此模式映射未來降雨，評估氣候變遷對降雨的衝擊。
本研究發展的模式包含空間降尺度階段及時間降尺度階段。空間降尺度階段使用K最近鄰居法(k-nearest neighbor method, KNN)，建立大尺度大氣因子及雨量關係，獲得測站尺度日雨量序列。接著，時間降尺度階段使用KNN結合基因演算法(genetic algorithm, GA)的GAKNN模式，並同時考慮跨日關係及日夜循環，由測站尺度日雨量序列，降尺度獲得測站尺度時雨量序列。模式建立使用的大尺度大氣因子為NCEP/NCAR再分析資料(NCEP/NCAR reanalysis data, NNR)，及測站尺度日與時雨量觀測資料。未來雨量映射使用CGCM3.1模式及BCM2.0模式，選用A2、A1B及B1三種情境之中期(2046-2065)和長期(2081-2100)資料，映射未來雨量變化情形。以實際案例評估本模式表現，顯示本模式降尺度結果可以保有觀測資料的統計特性。
總體而言，結果顯示本研究提出的模式，對於由大尺度大氣因子，降尺度產生測站尺度時雨量有著優異的表現。

關鍵字

氣候變遷 ； 降雨 ； 統計降尺度 ； K最近鄰居法 ； 基因演算法

並列摘要

Finer spatiotemporal resolution rainfall generation is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing models have less attention on the inter-daily connection and the diurnal cycle which can strongly influence the hydrological cycle. To address this problem, a spatiotemporal downscaling model is presented which is capable of reproducing the inter-daily connection, the diurnal cycle, and the statistics on daily and hourly scales. The large-scale datasets, which are obtained from the NCEP/NCAR reanalysis data and the GCMs outputs, and the local rainfall data are analyzed to assess the impacts of climate change on rainfall.
The proposed 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 rainfall at station scale using the k-nearest neighbor method. Then, the hourly downscaling of daily rainfall 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. After the downscaling processes, the changes of rainfall statistics are analyzed for the periods 2046-2065 and 2081-2100 under the A2, A1B and B1 scenarios of CGCM3.1 and BCM2.0.
An application to the Shihmen reservoir basin (Taiwan) has shown that the proposed model can accurately reproduce the local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal downscaling model is a powerful tool for generating hourly rainfall data from large-scale weather factors. The understanding of future changes of rainfall characteristics through this study are also expected to assist the planning and management of water resources systems.

並列關鍵字

Climate change ； Rainfall ； Statistical downscaling ； k-nearest neighbor method ； Genetic algorithm

參考文獻

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