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Non-stationary methods of flood frequency analysis are widespread in research but rarely implemented by practitioners. One reason may be that research papers on non-stationary statistical models tend to focus on model fitting rather than extracting the sort of results needed by designers and decision makers. It can be difficult to extract useful results from non-stationary models that include stochastic covariates for which the value in any future year is unknown. We explore the motivation for including such covariates, whether on their own or in addition to a covariate based on time. We set out a method for expressing the results of non-stationary models as an integrated flow estimate, which removes the dependence on the covariates. This can be defined either for a particular year or over a longer period of time. The methods are illustrated by application to a set of 375 river gauges across England and Wales. We find annual rainfall to be a useful covariate at many gauges, sometimes in conjunction with a time-based covariate. For estimating flood frequency in future conditions, we advocate exploring hybrid approaches that combine the best attributes of non-stationary statistical models and simulation models that can represent changes in climate and river catchments. [ABSTRACT FROM AUTHOR]

Summary: Although no single weather-related event can be directly attributed to climate change, new techniques make it possible to estimate how much the chance of an event has been altered by anthropogenic emissions. This paper looks at the floods that occurred in England in Autumn/Winter 2000, by using large ensembles of 1-year climate model simulations representing April 2000–March 2001. These represent an industrial climate and four estimates of an hypothetical non-industrial climate (without historical greenhouse gas emissions), and are used to drive hydrological models for eight catchments in England. The simulated flows are used to assess the impact of historical emissions on the chance of occurrence of extreme floods in each catchment, through calculation of the fraction of attributable risk (FAR). Combining results for the four non-industrial climates, positive median values of FAR indicate that, for all but one catchment, emissions are likely to have led to an increased chance of flooding in the October–December period. Definitive conclusions are difficult however, as there are wide bands of uncertainty in FAR, with distributions generally spanning no attributable difference in risk (FAR =0). One catchment shows a decreased flood chance (negative median FAR), due to its high permeability, but an analysis of the effect of antecedent conditions shows that a longer period of climate data than 1year is probably required to obtain more representative values of FAR for such catchments. The inclusion of snowfall/snowmelt is also shown to be important for floods over the October–March period, as the reduced likelihood of snowmelt-induced floods in the warmer temperatures of the industrial climate moderates the increased flood chance due to other sources of flooding. [ABSTRACT FROM AUTHOR]