The Quantifying Flood Risk of Extreme Events using Density Forecasts Based on a New Digital Archive and Weather Ensemble Predictions Project is a Natural Environment Research Council (NERC) Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002013/1 - Duration January 2007 - December 2008) led by Dr Patrick McSharry, University of Oxford. The dataset contains a collection of rainfall depth maxima data, dating back to 1860, plus associated description documents and rainfall maps of extreme events across the UK, have been used. All of these products have been digitised from the paper version of the British Rainfall publication, and are now archived at the BADC to enable easy access for future use and the wider community.
Floods in the UK are often caused by heavy rainfall lasting from minutes to weeks. Efficient management and mitigation of flood risk, especially surface water flooding in urban areas, requires accurate and reliable precipitation forecasts as inputs to flood risk models. Houses in flat areas are particularly at risk and meeting the shortage of houses in the south-east requires building on these areas. To estimate the flood hazard risk in order to try to protect these buildings, accurate rainfall predictions are needed. However, the connection between record rainfall and flooding is highly nonlinear, so that rainfall predictions must also say how likely rainfall is at any time - calculating the probability of rainfall.
Extreme rainfalls caused devastating floods in Boscastle in 2004 and Lynmouth in 1952, but the causes and pattern of rainfall was different. Therefore, scientists also need to know what pattern of rainfall caused the flooding.
This research aims to get good quality predictions of the probability of rainfall by combining advanced methods from statistics, the output from a new supercomputer model of the weather, and a new computer archive of exteme rainfalls going back to 1866 (and up to 1968), provided by a specialist company Hydro-GIS Ltd. It also aims to produce an automatic system for discovering the most likely pattern in the predicted rainfalls. The new prediction system and data will be freely available over the internet for use by the government and universities.
