|Item type||Location||Call number||Copy||Status||Date due|
|REPORT||Foothills Lab||03703 (Browse shelf)||1||Available|
Detection of changes in climate responses is essential to facilitate appropriate adaptation actions to be taken by decision makers, particularly where changes in the arrival rate and intensity of extreme daily precipitation event may lead to high impact flooding. Long established hydrological practice may no longer be appropriate if these changes are significant over the engineering design life. While extreme daily precipitation events are known to be seasonally over-dispersed, the dependent relationship between events is often ignored. Many statistical representations also explicitly ignore the complex relationships which exist between seasonality, atmospheric variables and extreme event frequency; thus a robust statistical tool is required to test the significance of any changes.
This paper presents the construction of a Vector Generalized Additive Model (VGAM) to characterize the inter-annual variability of extreme daily precipitation event frequency, and their associated magnitude, and so test the significance of changes in the temporal pattern of frequency and intensity. The modeling technique is one which could be applied in many regions of the world, but was specifically focused on an application to UK extreme daily precipitation.
The seasonal behavior of daily extreme precipitation and its dependence on sea surface temperatures (SST), air temperature range and the North Atlantic Oscillation (NAO) were represented in flexible Generalized Extreme Value and Poisson distribution parameter estimates using VGAMs. The model was validated for the period 1961-2000, replicating well seasonal peaks in event frequency, and regional differences across the UK in event magnitude and timing. There is a strong negative correlation with monthly maximum daily air temperature range, reflecting heightened event intensity and probability when the diurnal temperature range is at its lowest. Event frequency is positively correlated with SST for all regions; while event magnitude is dependent on either SST in the south of the UK or the NAO in the northwest of the UK.