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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 second 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. Whereas the first paper presents the development and the skill evaluation of the system, this paper provides explanations for the skill. In WUSHP hydrology is simulated by running the Variable Infiltration Capacity (VIC) hydrological model with meteorological forcing from bias-corrected output of ECMWF's Seasonal Forecasting System 4 (S4). WUSHP is probabilistic. For the assessment of skill, hindcast simulations (1981-2010) were carried out. To explain skill, we first looked at the forcing and found considerable skill in the precipitation forecasts of the first lead month but hardly any significant skill for later lead months. Seasonal forecasts for temperature have more skill. Skill in summer temperature is related to climate change and more or less independent of lead time. Skill in February and March is unrelated to climate change. Sources of skill in runoff were isolated with Ensemble Streamflow Prediction (ESP) experiments. These revealed that beyond the second lead month simulations with forcing that is identical for all years (ESPall) produce more skill in runoff than the simulations forced with S4 output (Full Hindcasts). This occurs because interannual variability of the S4 forcing has insufficient skill while it adds noise. Other ESP-experiments show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, at the end of the melt season, initial conditions of snow contribute significantly to the skill, also when forecasts start much earlier. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Finally, predictability of evapotranspiration was analysed in some detail, leading among others to the conclusion that it is due to all potential sources of skill but mostly to forcing. [ABSTRACT FROM AUTHOR]

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]

This contribution deals with the skill of a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. The system basically consists of the Variable Infiltration Capacity (VIC) hydrological model forced with bias-corrected output of ECMWF's Seasonal Forecasting System 4. Our analyses focus on skill in runoff hindcasts determined with so-called pseudo-observations, i.e. with runoff data generated with VIC forced with historical meteorological observations (1981-2010; skill in discharge against true observations are available but beyond the scope of the present analyses). For specific parts of the year, hot spots of significant discrimination skill were identified in Fennoscandia, the southern part of the Mediterranean, Poland and northern Germany, Romania and Bulgaria, western France and the eastern side of Great Britain. Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. To explain the skill in runoff, we first analysed the meteorological forcing. Most importantly for runoff, the precipitation forecasts contain hardly any significant skill beyond the first lead month. Seasonal forecasts of temperature have more skill, which is partly related to climate warming. To explain the skill in runoff, we also performed a suite of specific hydrological hindcasts akin to Ensemble Streamflow Predictions (ESP), which each isolate a different source of skill. These hindcasts show that in Europe initial conditions of soil moisture form the dominant source of skill in runoff. From April to July, initial conditions of snow contribute significantly to the skill. The combination of specific hindcasts and pseudo-observations revealed several interesting features. The most noteworthy is that beyond the second lead month, full hindcasts with S4-forcing are less skilful than hindcasts with forcing without interannual variation (like ESPs) because the S4-forcing consists mainly of noise which enhances degradation of the skill. [ABSTRACT FROM AUTHOR]