Seasonal streamflow forecasts for Europe - I. Hindcast verification with pseudo- and real observations.

Seasonal predictions can be exploited among others to optimize hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a model-based system built to produce seasonal hydrological forecasts (WUSHP: Wageningen University Seamless Hydrological Prediction system), applied here to Europe. The present paper presents the development and the skill evaluation of the system. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4. The system is probabilistic. For the assessment of skill, we performed hindcast simulations (1981-2010) and a reference simulation, in which VIC was forced by gridded meteorological observations, to generate initial hydrological conditions for the hindcasts and discharge output for skill assessment (pseudo-observations). Skill is analysed with monthly temporal resolution for the entire annual cycle. Using the pseudo-observations and taking the correlation coefficient as metric, hot spots of significant skill in runoff were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, North Germany, Romania and Bulgaria (mainly from November to January) and West France (from December to May). The spatial pattern of skill is fading with increasing lead time but some skill is left at the end of the hindcasts (7 months). On average across the domain, skill in discharge is slightly higher than skill in runoff. This can be explained by the delay between runoff and discharge and the general tendency of decreasing skill with lead time. Theoretical skill as determined with the pseudo-observations was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is mostly and often substantially less than theoretical skill, which is consistent with a conceptual analysis of the two types of verification. Qualitatively, results are hardly sensitive to the different skill metrics considered in this study (correlation coefficient, ROC area and Ranked Probability Skill Score) but ROC areas tend to be slightly larger for the Below Normal than for the Above Normal tercile. [ABSTRACT FROM AUTHOR]