The University of Sheffield
Catchment Science Centre

A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the River Thames, United King

Jacqueline Diaz-Nieto(1) and Prof. Rob L. Wilby(2)

Climatic Change (2005) 69: 245-268

1. Catchment Science Centre, University of Sheffield, North Campus, Kroto Research Institute, Broad Lane, S3 7HQ, United Kingdom

2. Environment Agency, Trentside Office, Scarrington Road, West Bridgford, Nottingham, NG2 5FA


Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.

Change in Mean Monthly Discharge

Change in monthly mean using discharge using statistical downscaling for the A2 Medium High climate change scenarios. The vertical lines associated with SD plots indicate 5th and 95th percentile confidence intervals derived from the ensemble of downscaled precipitation and PE series. Natural variability was estimated using the extreme ensemble members of downscaled daily precipitation and PE for the 1961–1990 baseline. This provides an indication of whether future changes lie outside the bounds of simulated, natural river flow variability. The most notable difference between the projected flow regimes is the magnitude of the changes associated with the SD and CF scenarios.