Incorporating seasonality into event-based joint probability methods for predicting flood frequency: A hybrid causative event approach

摘要

Flood extremes are driven by highly variable and complex climatic and hydrological processes. Observational evidence has identified that seasonality of climate variables has a major impact on flood peaks. However, event-based joint probability approaches for predicting the flood frequency distribution (FFD), which are commonly used in practice, do not commonly incorporate climate seasonality. This study presents an advance in event-based joint probability approaches by incorporating seasonality using the hybrid causative events (HCE) approach. The HCE was chosen because it uses the true causative events of the floods of interest and is able to combine the accuracy of continuous simulation with the computational efficiency of event-based approaches. The incorporation of seasonality is evaluated using a virtual catchment approach at eight sites over a wide range of Australian climate zones, including tropical, temperature, Mediterranean and desert climates (virtual catchment data for the eight sites is freely available via digital repository). The seasonal HCE provided accurate predictions of the FFD at all sites. In contrast, the non-seasonal HCE significantly over-predicted the FFD at some sites. The need to include seasonality was influenced by the magnitude of the seasonal variation in soil moisture and its coherence with the seasonal variation in extreme rainfall. For sites with a low seasonal variation in soil moisture the non-seasonal HCE provided reliable estimates of the FFD. For the remaining sites, it was found difficult to predict a priori whether ignoring seasonality provided a reliable estimate of the FFD, hence it is recommended that the seasonal HCE always be used. The practical implications of this study are that the HCE approach with seasonality is an accurate and efficient event-based joint probability approach to derive the flood frequency distribution across a wide range of climatologies. (C) 2016 Published by Elsevier B.V.