Enhancing Seasonal Flood Forecasts through Spectral Transformation of Hydroclimatic Covariates

Seasonal forecasting of extreme streamflow is essential for effective reservoir management, including optimizing flood retention capacity and preparing for disaster response supplies. This study investigates whether the probabilistic forecasts of seasonal floods can be improved by integrating spectrally transformed hydroclimatic variables from the preceding season. Building on previous research, we proposed the spectral transformation technique to conditional covariates within a Generalized Extreme Value (GEV) modeling framework. Using streamflow observations from European catchments provided by the Global Runoff Data Centre (GRDC), we evaluated the role of transformed hydroclimatic covariates using Wavelet System Prediction (WASP) in enhancing seasonal flood forecasting skills. Results reveal that incorporating spectrally transformed covariates leads to improved forecasting skills measured by Ranked Probability Skill Score (RPSS) for a significant proportion of stations across Europe. Northern European catchments exhibit a stronger influence of climate covariates compared to Central and Western Europe. However, when transformed covariates are employed, teleconnections are enhanced across the continent, with notable improvements in the UK, Germany, and France. The hybrid WASP-GEV forecasting framework, integrating spectral transformation, significantly enhanced forecast skills with up to three months lead time. These findings underscore the importance of advanced data transformation and modeling techniques in improving the prediction of hydroclimatic extremes, offering practical implications for water resource management in a changing climate.