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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: London is expected to experience more frequent periods of intense rainfall and tidal surges, leading to an increase in the risk of flooding. Damp and flooded dwellings can support microbial growth, including mould, bacteria, and protozoa, as well as persistence of flood-borne microorganisms. The amount of time flooded dwellings remain damp will depend on the duration and height of the flood, the contents of the flood water, the drying conditions, and the building construction, leading to particular properties and property types being prone to lingering damp and human pathogen growth or persistence. The impact of flooding on buildings can be simulated using Heat Air and Moisture (HAM) models of varying complexity in order to understand how water can be absorbed and dry out of the building structure. This paper describes the simulation of the drying of building archetypes representative of the English building stock using the EnergyPlus based tool ‘UCL-HAMT’ in order to determine the drying rates of different abandoned structures flooded to different heights and during different seasons. The results are mapped out using GIS in order to estimate the spatial risk across London in terms of comparative flood vulnerability, as well as for specific flood events. Areas of South and East London were found to be particularly vulnerable to long-term microbial exposure following major flood events. [Copyright &y& Elsevier]

This paper discusses computer simulation modelling in the context of environmental risk management. Approaching computer simulation as practice, performed in networks of heterogeneous elements, we examine the modelling undertaken by engineering consultants commissioned to provide knowledge about local flood risk to the Environment Agency of England and Wales (EA), the public body responsible for flood risk management. We propose that this simulation modelling is best understood as a form of engineering, work geared to solving the problems of clients. It is also a 'virtual' activity, articulating risks and possibilities in the digital space of the computer. We find that this 'virtual engineering' is shaped by the demands and protocols of the EA, first, by the establishment of long-term contractual agreements for delivering knowledge and second, by an EA requirement to use particular software packages. Fashioned between long-term contracts and black-boxed software virtual engineering becomes stabilised as 'the' way in which knowledge about flood risk in actual localities is generated and, consequently, becomes 'hard-wired' into flood risk management in England. [ABSTRACT FROM AUTHOR]