Changes in hydrological extremes and climate variability in the Severn Uplands

Hydrological extremes within the UK have increased in intensity, frequency andpersistence over recent years and are predicted to increase in variability throughout the 21stcentury. Past and future changes in hydrological extremes relative to climate change wereinvestigated within Severn Uplands, a climate sensitive catchment. Using the Mann-Kendall trend detection test, time-series analysis over a 30-year period revealed asignificant increase in winter and autumn precipitation and a decrease in summerprecipitation. The analysis of flow time-series indicated an increase in winter and Julyflows and a decrease in spring flows. Changes in climate variability over the same periodshowed increases in air temperature and SST, and a reduction in snow cover. Climatevariables were found to largely correlate with hydrological extremes which werecharacteristic of certain weather types and largely influenced by the NAO.To model future flows within the Severn Uplands a hydrological model (HEC-HMS) wasused to simulate hydrological processes. The extreme hydrological event of November-December 2006 was used to calibrate the model. The difference between using radar andgauge precipitation data to drive the model was quantified. Radar data resulted in thesmallest prediction accuracy followed by gauge-corrected radar data (corrected using themean-field bias where gauge rainfall was interpolated using cokriging) and then gaugeprecipitation which had the largest prediction accuracy. Model accuracy was sufficientusing the gauge corrected radar and gauge precipitation data as inputs, so both were alteredfor future predictions to investigate the propagation of uncertainty. Predicted changes intemperature and precipitation by the UKCIP02 scenarios were used to alter the baselineextreme event to predict changes in peak flow and outflow volume. Both radar- and gaugedrivenhydrological modelling predicted large flow increases for the 21st century withincreases up to 8% by the 2020s, 18% by the 2050s and 30% by the 2080s. Discrepanciesbetween predictions were observed when using the different data inputs