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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]

Summary: This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. [Copyright &y& Elsevier]