The 2013/14 Thames Basin Floods: Do Improved Meteorological Forecasts Lead to More Skillful Hydrological Forecasts at Seasonal Time Scales?

Abstract

AbstractThe Thames basin experienced 12 major Atlantic depressions in winter 2013/14, leading to extensive and prolonged fluvial and groundwater flooding. This exceptional weather coincided with highly anomalous meteorological conditions across the globe. Atmospheric relaxation experiments, whereby conditions within specified regions are relaxed toward a reanalysis, have been used to investigate teleconnection patterns. However, no studies have examined whether improvements to seasonal meteorological forecasts translate into more skillful seasonal hydrological forecasts. This study applied relaxation experiments to reforecast the 2013/14 floods for three Thames basin catchments with different hydrogeological characteristics. The tropics played an important role in the development of extreme conditions over the Thames basin. The greatest hydrological forecasting skill was associated with the tropical Atlantic and less with the tropical Pacific, although both captured seasonal meteorological flow anomalies. Relaxation applied over the northeastern Atlantic produced confident ensemble forecasts, but hydrological extremes were underpredicted; this was unexpected with relaxation applied so close to the United Kingdom. Streamflow was most skillfully forecast for the catchment representing a large drainage area with high peak flow. Permeable lithology and antecedent conditions were important for skillfully forecasting groundwater levels. Atmospheric relaxation experiments can improve our understanding of extratropical anomalies and the potential predictability of extreme events such as the Thames 2013/14 floods. Seasonal hydrological forecasts differed from what was expected from the meteorology alone, and thus knowledge is gained by considering both components. In the densely populated Thames basin, considering the local hydrogeological context can provide an effective early alert of potential high-impact events, allowing for better preparedness.