Abstract
Hydropower is the most important source of renewable energy globally. The effective operation of hydropower plants and their associated reservoirs is crucial to ensure safety and improved system performance in terms of optimization of the economic value of water. Sustainable hydropower planning and management depend highly on estimates of water availability in rivers or reservoirs for the coming months or seasons, generated by model-based hydro-meteorological forecasting chains. Such chains are based on probabilistic ensemble forecasts that account for the uncertainty in future projections of hydrological variables. The major sources of uncertainty in seasonal hydrological forecasts involve the climate forcing (CF) during the forecast period and the initial hydrological conditions (IC) at the start date of the forecast. The importance of the latter is defined by their persistence, which in turn is dictated by catchment geology. In this work, the sources of uncertainty in seasonal hydro-meteorological forecasting chains were analyzed, with a special focus on the effect of geology on the contribution of the hydrological predictability sources (IC, CF) to the seasonal streamflow forecast skill. To this end, the Ensemble Streamflow Prediction (ESP)/reverse ESP (revESP) framework was applied over two case studies located in the Upper Adige River Basin in northern Italy, that represent the end members of a set of catchments of contrasting geology, hence contrasting hydrological response: a highly-permeable, hence slow-responding catchment and a fast-responding catchment of low permeability. The results obtained suggest that IC are more important for seasonal streamflow predictability in the slow-responding catchment due to the higher IC persistence that leads to up to 95% higher ESP skill compared to the respective skill in the fast-responding catchment. The critical lead time (CLT), i.e., the time that the skill of the revESP surpasses the skill of the ESP, is up to 0.5 months higher in the slow-responding catchment. As a further step, a sensitivity analysis of the effect of different levels of uncertainty in the predictability sources on the streamflow forecast skill was performed by employing the End Point Blending (EPB) methodology. The findings show that the contribution of IC in the slow-responding catchment is higher by up to 44%, with IC being important for up to 4 months of lead in the slow-responding catchment and 2 months of lead in the flashier catchment. The latter analysis highlights the added value of the EPB in comparison to the traditional ESP/revESP approach for identifying the sources of seasonal hydrological predictability, on the basis of catchment geology. To account for the impact of diverse topographical and hydro-climatic characteristics on streamflow predictability, the application of the ESP/revESP framework was extended over 307 catchments in Switzerland. The conclusions drawn highlight the importance of diverse hydrological regimes and geology-induced subsurface storage capacity for IC-related seasonal streamflow predictability. This work is concluded with the investigation of the value of ESP streamflow forecasts within a simplified optimization framework designed to maximize the revenue of a reservoir-based hydropower plant. The preliminary results obtained corroborate the benefit of utilizing skillful ESP forecasts to improve the plant’s operational scheme in comparison to the common practice that relies on the climatology (CLIM). The present work provides the basis for effective hydropower management based on skillful seasonal streamflow predictions. Future developments involve improving the quality of the developed forecasting tool by incorporating seasonal climate forecast products as well as by employing multiple hydrological models.