Synthetic Ensemble Forecasts: Operations‐Based Evaluation and Inter‐Model Comparison for Reservoir Systems Across California

Abstract

Abstract Synthetic ensemble forecasts are an important tool for testing the robustness of forecast‐informed reservoir operations (FIRO). These forecasts are statistically generated to mimic the skill of hindcasts derived from operational ensemble forecasting systems, but they can be created for time periods when hindcast data are unavailable, allowing for a more comprehensive evaluation of FIRO policies. Nevertheless, it remains unclear how to determine whether a candidate synthetic ensemble forecasting approach is sufficiently representative of its real‐world counterpart to support FIRO policy evaluation. This highlights a need for formal fit‐for‐purpose validation frameworks to advance synthetic forecasting as a generalizable risk analysis strategy. We address this research gap by first introducing a novel operations‐based validation framework, where reservoir storage and release simulations under a FIRO policy are compared when forced with a single ensemble hindcast and many different synthetic ensembles. We evaluate the suitability of synthetic forecasts based on formal probabilistic verification of the operational outcomes. Second, we develop a new synthetic ensemble forecasting algorithm and compare it to a previous algorithm using this validation framework across a set of stylized, hydrologically diverse reservoir systems in California. Results reveal clear differences in operational suitability, with the new method consistently outperforming the previous one. These findings demonstrate the promise of the newer synthetic forecasting approach as a generalizable tool for FIRO policy evaluation and robustness testing. They also underscore the value of the proposed validation framework in benchmarking and guiding future improvements in synthetic forecast development.

Keywords

• forecast informed reservoir operations
• stochastic hydrology
• hydrologic forecasting
• probabilistic verification
• simulation and optimization
• uncertainty