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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: 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]