Climate change impacts on extreme floods I: combining imperfect deterministic simulations and non-stationary frequency analysis

Abstract

Flood quantiles are routinely used in hydrologic engineering to design hydraulic structures, optimize erosion control structure and map the extent of floodplains. As an increasing number of papers are pointing out cycles and trends in hydrologic time series, the use of stationary flood distributions leads to the overestimation or underestimation of the hydrologic risk at a given time. Several authors tried to address this problem by using probability distributions with time-varying parameters. The parameters of these distributions were assumed to follow a linear or quadratic trend in time, which may be valid for the short term but may lead to unrealistic long-term projections. On the other hand, deterministic rainfall-runoff models are able to successfully reproduce trends and cycles in stream flow data but can perform poorly in reproducing daily flows and flood peaks. Rainfall-runoff models typically have a better performance when simulation results are aggregated at a larger time scale (e.g. at a monthly time scale vs. at a daily time scale). The strengths of these two approaches are combined in this paper where the annual maximum of the time-averaged outputs of a hydrologic model are used to modulate the parameters of a non-stationary GEV model of the daily maximum flow. The method was applied to the Kemptville Creek located in Ontario, Canada, using the SWAT (Soil and Water Assessment Tool) model as rainfall-runoff model. The parameters of the non-stationary GEV model are then estimated using Monte Carlo Markov Chain, and the optimal span of the time windows over which the SWAT outputs were averaged was selected using Bayes factors. Results show that using the non-stationary GEV distribution with a location parameter linked to the maximum 9-day average flow provides a much better estimation of flood quantiles than applying a stationary frequency analysis to the simulated peak flows.