A real time reservoir inflow forecast evaluation framework

Cameron Bracken , Youngjun Son, Vince Tidwell, Nathalie Voisin

Recent advances in data driven hydrologic forecasts have led to a multitude of new and rapidly improving streamflow forecast products and technologies. These often lead hydropower producers to question whether such advances translate into increases in revenue and whether implementing these new forecasts justify the additional associated costs. To address this, we present a general framework that can be used in real time (or near real time) for reservoir inflow forecast evaluation. The framework calls for an iterative approach which involves direct operator feedback in the evaluation process. The evaluation can include forecast verification and validation, but also includes other qualitative or specialized metrics of forecast quality. We also present a successful application of this evaluation framework to three reservoirs in the Great River Hydro system on the Connecticut River in the Northeast U.S. While there is no single evaluation approach that can apply to all hydrologic forecasts, we also present a range of options of both traditional and tailored metrics that will be suitable for a diverse set of situations and systems. The goal of this framework is to provide hydropower operators with information necessary for evaluating how much a particular forecast product will improve their operations and ultimately inform ongoing investment decisions, such as whether to purchase external forecast products or improve existing internal forecasting capabilities.