Lifting scheme for streamflow data in river networks. Issue 2 (17th February 2022)

• Record Type: Journal Article
• Authors: Park, Seoncheol; Oh, Hee‐Seok
• Is Part Of: Journal of the Royal Statistical Society. Volume 71:Issue 2(2022)
• Page Start: 467
• Page End: 490
• Publication Date: 2022-02-17
• Subjects: lifting scheme -- river network -- smoothing -- spatial adaptation -- spatial modelling -- streamflow data; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12542
• Languages: English
• ISSNs: 0035-9254

Abstract

Abstract: This paper presents a new multiscale method for analysing water pollutant data located in river networks. The main idea of the proposed method is to adapt the conventional lifting scheme, reflecting the characteristics of streamflow data in the river network domain. Due to the complexity of the data domain structure, it is difficult to apply the lifting scheme to the streamflow data directly. To solve this problem, we propose a new lifting scheme algorithm for streamflow data that incorporates flow‐adaptive neighbourhood selection, flow proportional weight generation and flow‐length adaptive removal point selection. A nondecimated version of the proposed lifting scheme is also provided. The simulation study demonstrates that the proposed method successfully performs a multiscale analysis of streamflow data. Furthermore, we provide a real data analysis of water pollutant data observed on the Geum‐River basin compared to the existing smoothing method.