Seasonal forecasts of hydrological parameters, including the inflow, to hydropower systems have recently been explored in a number of projects under the Copernicus Climate Change Service (C3S) relating to the energy and water sector. Based on these demonstrators, the C3S Energy operational service will be developed and provide such information to the European energy sector. Meanwhile, several meteorological institutes and commercial vendors already offer related services aimed at the hydropower sector. For this aim, several methodologies have been developed (e.g. Olsson et al. 2016). These include the use of advanced seasonal forecasts of 6+ months duration based on a dynamical modelling chain that links seasonal atmospheric forecasts to hydrological models through an ensemble approach, e.g. as pursued in several of the abovementioned C3S initiatives.
When measuring the added value of model-based seasonal forecasts, results are often compared to the result one would get from using simple climatological means. In this study, we argue that it is more reasonable to benchmark the performance of seasonal hydrological forecasts against more advanced statistical models instead. For this aim, we develop variations of a detailed statistical forecast model based on observed time series inflow to 82 representative stations from Norwegian hydropower system in the period 1958-2016 (Holmqvist and Engen, 2008). The impact of climate change in terms of increased runoff is clearly observed in the data, and hence we further investigate whether this affects the skill of our statistical model. In general, our model is found to outperform forecasts based on the climatological means, and thus to provide a more qualified measure to benchmark seasonal forecast systems based on dynamical modelling.