Regional flood-duration–frequency modeling in the changing environment

Flood-duration–frequency (QdF) analysis is becoming a popular tool for estimating the severity of flood events as an integrated function of return period and flood duration. QdF models are often applied in regional flood studies, leading to regional QdF relations derived for hydrologically homogeneous regions. Regional QdF models can be used to estimate flood quantiles for a given return period and flood duration at any ungaged site of a homogeneous region. By combining the information on sampling variability from hydrologically similar neighboring sites, the regional approach to QdF modeling also decreases the estimation uncertainty at gaged sites of the region. Regional QdF models have been developed for stable environmental conditions. Nevertheless, recent studies on stationarity of hydrologic records conducted in different parts of the world have identified significant changes in the statistical parameters of the analyzed records. Changing environmental conditions call for new flood estimation methods that can take into account the nonstationary character of hydrologic records and that can deal with time-dependent parameters of flood frequency distributions. This study defines the key concepts of a nonstationary approach to regional QdF modeling. The proposed approach uses regional trend analysis to identify time-dependent parameters of the model, and to estimate and predict flood quantiles for the present and near future time horizons. The model can be flexibly applied to various scenarios of nonstationarity at both local and regional scales. The approach is illustrated on a set of data from a hydrologically homogeneous region in Québec, Canada. A split-sample experiment is used to compare the performance of the proposed model with the traditional stationary regional QdF model. The case study results demonstrate that ignoring statistically significant nonstationarity of hydrologic records can seriously bias flood quantiles estimated for the near future.