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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]

Abstract: Climate change is a global problem and across the world the transport sector is finding it difficult to break projected increases in carbon dioxide (CO2) emissions; there are very few contexts where deep reductions in transport CO2 emissions are being made. A number of research studies are now examining the potential for future lower CO2 emissions in the transport sector. This paper develops this work to consider some of the wider sustainability impacts (economic, social and local environmental) as well as the lower CO2 transport impacts of different policy trajectories. Hence the central argument made is for an integrated approach to transport policy making over the longer term – incorporating scenario analysis and multi-criteria assessment (MCA) – to help assess likely progress against a range of objectives. The analysis is based on work carried out in Oxfordshire, UK. Different packages of measures are selected and two scenarios developed which satisfy lower CO2 aspirations, one of which also provides wider positive sustainability impacts. A simulation model has been produced to help explore the strategic policy choices and tensions evident for decision-makers involved in local transport planning. The paper argues for a ‘strategic conversation’ () at the sub-regional and city level, based upon future scenario analysis and MCA, discussing the priorities for intervention. Such an approach will help us examine the scale of change and trade-offs required in moving towards sustainable transport futures. [Copyright &y& Elsevier]

Abstract: A computer based building simulation model was developed to examine the energy load and environmental management in The National Archives Q1 repository building in Kew, UK to optimise environmental management and examine the impacts of climate change. The need to accurately simulate the hygrothermal environment inside the archive building which houses mainly paper-based records led to the choice of EnergyPlus as the modelling software. The study presents the simulation results of five environmental strategies which predict energy saving potential as high as 43% without significantly affecting the quality of the preservation environment. The effect of climate change is predicted to have little impact on the archive environment due to the filtering effect of the air conditioning system. On the other hand, an increase in total energy load by 15% and 24% is predicted under the worst case climate change scenario in 2050 and 2080, respectively, if the current environmental management practice is continued into the future. However, the identified energy saving strategies could represent possible mitigative solutions in reducing future energy load against the impact of climate change. [Copyright &y& Elsevier]

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]