Abstract
<p>This contribution deals with the skill of a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to South America and earlier to Europe (see  Greuell et al., hess-23-371-2019). The system basically consists of the Variable Infiltration Capacity (VIC) hydrological model forced with output from ECMWF’s Seasonal Forecasting System 5 (SEAS5). We analyse skill in runoff and discharge hindcasts both with real observations and with so-called pseudo-observations, i.e. with discharge data generated with VIC forced with historical meteorological observations (1981-2015). At the continental scale discrimination skill in runoff shows characteristics that are similar to Europe. Especially, even at the longest lead time (7 months) significant skill remains in 20-30% of both continents. However, in the first lead month there is less significant skill in South America, due to absence of skill in its very dry and very wet regions, than in Europe, where similar extremes do not exist. To explain the skill in runoff, we performed two suites of specific hydrological hindcasts akin to Ensemble Streamflow Predictions (ESP), which each isolate a different source of skill (meteorological forcing and initial conditions). We find that in South America the contribution to skill by forcing is larger than in Europe, which can be ascribed to differences in the skill in the precipitation forcing. Even at a lead time of 7 months, the precipitation hindcasts have significant skill in 15-30% of South America while in Europe skill is almost confined to the first lead month. Discharge hindcasts for grid cells with a sufficient amount of observations were post-processed with ensemble model output statistics (EMOS). This procedure successfully increased reliability but resulted in a small decrease of discrimination skill. Nevertheless, for the location of the Itaipu dam, used to produce 18% of Brazil’s electricity, discrimination skill is highly significant for the post-processed discharge, e.g. at all lead times in the last two months of the year.</p>
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