Reconstructing extreme flows using an airflow index-based stochastic weather generator and a hydrological simulation model

This paper offers an alternative to the use of geomorphological and sedimentological evidence for the reconstruction of flood and low flow frequencies. It is based on a technique developed to estimate the hydrological impact of future climate change and it uses either observed or calculated meteorological parameters. It is possible to use this method directly without modification to ‘hindcast’ events within the period of regional meteorological records, which in areas like the UK extend back over the last 150 years. It should also be possible to extend the approach to earlier periods using reconstructed meteorological parameters based on surrogate data, such as ships' logs. The resulting extreme flow sequences may be used to study past hydrological regimes or to improve estimates of present-day risks by extending the flow records. The technique uses an airflow index-based stochastic weather generator to create hydrometeorological parameters to input into a physically based hydrological simulation model. The method is illustrated here in a reconstruction of daily flow series for the River Wye catchment above Rhayader, mid-Wales, for the period 1889–1998. The method makes use of observed climatic variables for the entire period, with the aim of capturing actual climate variability occurring over the 110-year period. Changes in high flow characteristics are assessed using the mean annual flood (MAF), Q5 flow and peaks-over-threshold (POT) calculated from the 110-year simulated daily flow series. This particular application shows evidence of a possible trend towards increasing magnitude and frequency of high flow events, which, if continued, would have implications for flood and water resource management. Looking at evidence from the last 110 years helps to place possible future trends within the context of past variations in high flow extremes due to both natural and anthropogenically influenced fluctuations in climate.