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  • No preview · Article · Aug 2014 · Quaternary Science Reviews
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    ABSTRACT: Climate impact studies focused on the projection of changing flood risk are increasingly utilized to inform future flood risk policy. These studies typically use the output from global (GCMs) and regional climate models (RCMs). However the direct application of GCM/RCM output is controversial as often significant biases exist in predicted rainfall; instead a number of alternative ‘correction’ approaches have emerged. In this study an ensemble of RCMs from the ENSEMBLES and UKCP09 projects are applied, via a number of application techniques, to explore the possible impacts of climate change on flooding in the Avon catchment, in the UK. The analysis is conducted under a continuous simulation methodology, using a stochastic rainfall generator to drive the HBV-light rainfall run-off model under a parameter uncertainty framework. This permitted a comparison between the projections produced by differing application approaches, whilst also considering the uncertainty associated with flood risk projections under observed conditions. The results from each of the application approaches project an increase in annual maximum flows under the future (2061–2099) climate scenario. However the magnitude and spread of the projected changes varied significantly. These findings highlight the need to incorporate multiple approaches in climate impact studies focusing on flood risk. Additionally these results outline the significant uncertainties associated with return period estimates under current climate conditions, suggesting that uncertainty over this observed record already poses a challenge to develop robust risk management plans.
    Full-text · Article · Apr 2014 · Journal of Hydrology
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    ABSTRACT: Reither et al. (2009) use a Hierarchical Age-Period-Cohort model (HAPC - Yang & Land, 2006) to assess changes in obesity in the USA population. Their results suggest that there is only a minimal effect of cohorts, and that it is periods which have driven the increase in obesity over time. We use simulations to show that this result may be incorrect. Using simulated data in which it is cohorts, rather than periods, that are responsible for the rise in obesity, we are able to replicate the period-trending results of Reither et al. In this instance, the HAPC model misses the true cohort trend entirely, erroneously finds a period trend, and underestimates the age trend. Reither et al.’s results may be correct, but because age, period and cohort are confounded there is no way to tell. This is typical of age-period-cohort models, and shows the importance of caution when any APC model is used. We finish with a discussion of ways forward for researchers wishing to model age, period and cohort in a robust and non-arbitrary manner.
    Full-text · Article · Jan 2014 · Social Science [?] Medicine
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