Enhancement of seasonal hydrological forecasting with analogue selection of historical years

Hydropower accounts for nearly half of Sweden’s electrical energy production, however the natural availability of water in the rivers is asymmetrically distributed throughout the year. As much as 70% of the annual streamflow is generated during the relatively short spring flood period (i.e., May – July) and the situation is further complicated by the fact that this occurs just when the energy demand begins to fall as the summer approaches. To offset these issues, operators must store as much of this excess streamflow in reservoirs for later use while still maintaining adequate storage capacity for flood control. Seasonal forecasts (up to 7 months ahead) of reservoir inflows are vital for the operational effectiveness of such endeavours.

To avoid the complexity and known biases associated with dynamic GCM-based seasonal forecasting approaches while improving the forecast skill, an analogue-weighted Ensemble Streamflow Prediction approach (A-ESP) was implemented in 84 sub-catchments across seven of the largest hydropower producing river systems in Sweden. The approach uses hydrological weather regimes (HWR) to select analogues from the of historical ensemble of meteorological data to force the hydrological models. Here, HWRs are classified large-scale atmospheric regimes used to describe “average” variability of local climate that results in precipitation events of similar frequency and magnitude. The selected analogues are pooled with the traditional ESP ensemble to make up the A-ESP seasonal forecast. We assess the forecast skill with respect to volume error and frequency of improvement using the ESP approach as the benchmark.

The results show that HWRs are a relatively simple yet useful tool for improving the forecast skill by offering objective criteria for analogue selection. Compared to the traditional ESP approach, the HWR-based A-ESP approach is able to reduce the inflow volume forecast errors by between 2-15% for all months and outperforms the ESP between 53-70% of the time. These results suggest that the analogue hypothesis to seasonal forecasting is still relevant and that using HWR for selection is effective. The comparative simplicity of this approach would offer operators improved forecasts that are both relatively computationally cheap and easy to implement, especially for operators using ESP-based forecast systems.