雷達與雨量站降雨資料融合於都市水文之應用
Journal
土木水利
Journal Volume
50
Journal Issue
3
Start Page
22
End Page
33
ISSN
0253-3804
Date Issued
2023-06
Author(s)
Susana Ochoa Rodriguez
Abstract
都市水文傳統上多使用地面雨量站觀測資料作為主要降雨資訊來源，然而雨量站資料有其地理位置限制，無法提供足夠之空間降雨資訊。近年來，隨著資訊科技之進步，都市排水模型之解析度越來越高，為了得到最好之模擬結果，也需要高品質、高解析度之降雨資訊，僅使用雨量站數據可能無法滿足都市排水模擬之需求。隨著訊號處理技術之進步及硬體設備之升級，雷達降雨於大尺度水文模擬之應用逐漸普及，然而在都市水文之應用還是相對有限，主要原因還是因為雷達降雨之準確度不足。本研究透過二個位於英國之都市集水區案例，分享透過雷達、雨量站降雨資料融合技術，可以生成高解析度、高準確度之降雨數據，並透過都市排水模擬，展示融合降雨資訊可以產出較使用原始雷達降雨或是僅使用雨量站降雨資料更好品質之流量模擬結果。此外本研究也分析在不同降雨型態及模擬不同雨量站密度等情況下，對於融合結果品質產生之影響，此分析結果可以作為實務上使用資料融合技術之參考。
Rainfall estimates of high accuracy and resolution are required for urban hydrological applications, given the high imperviousness, small size and fast response of urban catchments. Despite significant progress in rainfall measurement in recent decades, the resolution and accuracy of the rainfall estimates typically available from national meteorological services are still insufficient for urban hydrological applications. The work focuses upon the techniques that can help improve radar rainfall accuracy, with the aim to provide guidance on the application of radar-rain gauge merging techniques at urban scales, so that high-accuracy rainfall estimates which meet urban requirements can be obtained. Three merging techniques, namely Mean Field Bias (MFB) correction, kriging with external (KED) and Bayesian (BAY) data merging, are selected for testing on grounds of performance and common use. Results suggest that all merging methods improve the applicability of radar estimates to urban hydrology. Overall, KED displays the best performance, with BAY a close second and MFB providing the smallest benefits.
Type
journal article