Performance assessment of hydrologic uncertainty processor through integration of the principal components analysis

Abstract

Uncertainty analysis is important and should be always considered when using models for flood forecasting. In this paper, the ‘Principal Components Analysis-Hydrologic Uncertainty Processor’ (PCA-HUP) was developed for probabilistic flood forecasting (PFF) and further evaluated in the middle Yellow River, China. Due to the severe sediment erosion, small and medium floods drain in the main channel (normal floods) while large floods would spill over the bank and drain in river floodplains (overbank floods). Thus, the practical routing methods were used to provide the deterministic flood forecasting (DFF) input for PCA-HUP. PCA-HUP quantifies the forecast uncertainty and provides PFF results. The comparison of performance between the DFF and PFF outputs indicated that PFF could also provide a good accuracy of deterministic hydrograph. In order to explore the performance decay of DFF and PFF with lead time increasing, the lead times n = 1, 6 and 10 hours were chosen for comparison. Results suggested that, with the increasing lead time, the performances of both DFF and PFF decayed accordingly. As a consequence, this study proved the practicability of PCA-HUP in the operational forecasting for both normal and overbank floods in the middle reach of Yellow River.