Flood Wave Superposition Analysis Using Quantitative Matching Patterns of Peak Magnitude and Timing in Response to Climate Change

Abstract

Flood wave superposition (FWS) of upstream and tributary rivers, as a typical compound hydrological event, may lead to considerable downstream flood hazards. In spite of this, the quantitative identification of FWS classification so far remains elusive. In this study, we quantitatively examined the typical matching patterns of FWS based on flood peak magnitude and timing between the upstream and tributary discharge, to evaluate the flood severity for both present-day conditions and future climate projections. The future FWS projection was realized using hydrological modeling coupled with multiple outputs of global climate models (GCMs) under the Representative Concentration Pathway (RCP) 2.6 and 8.5 emission scenarios. A triple point of discharge stations, including upstream, downstream and tributary stations, located at a river confluence of the Poyang Lake Basin, China, was selected as the study area. The results revealed that the frequency of perfect temporal matching (0-day time lag) of projected peaks between upstream and tributary floods increased from 61% in the reference period to >68% and\u2009>\u200966% in the future under RCPs 2.6 and 8.5, respectively. Furthermore, both the future numbers and frequencies of the projected peaks between upstream and tributary floods with both perfect temporal and magnitude matching (the classification associated with the most damage in FWS) would substantially increase across all GCMs under RCPs 2.6 and 8.5. These findings indicate that future FWS is expected to experience increasing severity due to the changing climate under no matter RCP 2.6 or 8.5 emission scenarios. Overall, it is suggested that effective adaptation strategies be developed in order to stagger the timing of upstream and tributary floods in the future.