Climate Change Impacts on the Extreme Rainfall for Selected Sites in North Western England

Mawada Abdellatif,William Atherton,Rafid Alkhaddar

doi:10.4236/ojmh.2012.23007

Mawada Abdellatif, William Atherton + Show 1 more

Open Access

https://doi.org/10.4236/ojmh.2012.23007

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Abstract

Impact and adaptation assessments of climate change often require more detailed information of future extreme rainfall events at higher resolution in space and/or time, which is usually, projected using the Global Climate Model (GCM) for different emissions of greenhouse concentration. In this paper, future rainfall in the North West region of England has been generated from the outputs of the HadCM3 Global Climate Model through downscaling , employing a hybrid Generalised Linear Model (GLM) together with an Artificial Neural Network (ANN). Using two emission scenarios (A1FI and B1), the hybrid downscaling model was proven to have the capability to successfully simulate future rainfall. A combined peaks-over-threshold (POT)-Generalised Pareto Distribution approach was then used to model the extreme rainfall and then assess changes to seasonal trends over the region at a daily scale until the end of the 21st century. In general, extreme rainfall is predicted to be more frequent in winter seasons for both high (A1FI) and low (B1) scenarios, however for summer seasons, the region is predicted to experience some increase in extreme rainfall under the high scenario and a drop under the low scenario. The variation in intensity of extreme rainfall was found to be based on location,season, future period, return period as well as the emission scenario used.

Highlights

Extreme events have greater impacts on the lives of communities and investigation of extreme events is crucial for impact assessment and adaptation studies
Future rainfall in the North West region of England has been generated from the outputs of the HadCM3 Global Climate Model through downscaling, employing a hybrid Generalised Linear Model (GLM) together with an Artificial Neural Network (ANN)
Extreme rainfall is predicted to be more frequent in winter seasons for both high (A1FI) and low (B1) scenarios, for summer seasons, the region is predicted to experience some increase in extreme rainfall under the high scenario and a drop under the low scenario

Summary

Introduction

Extreme events have greater impacts on the lives of communities and investigation of extreme events is crucial for impact assessment and adaptation studies. Most existing systems of water management and other infrastructure have been designed under the assumption that climate is stationary This basic concept on which engineers work, assumes that climate is variable, but with properties of this variability being constant with time and occur around an unchanging mean state. This assumption of stationary conditions is still common practice for design criteria for (the safety of) new infrastructure, even though the notion that climate change may alter the mean, variability and extremes of relevant weather variables is widely accepted [1]. Information derived directly from global climate model outputs may not be of sufficient spatial or temporal resolution to represent changes within a specific region, downscaling is required

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