Operational Evaluation of Synthetic Ensemble Forecasts for Forecast Informed Reservoir Operations (FIRO)
Abstract
Forecast Informed Reservoir Operations (FIRO) with state-of-the science hydrologic ensemble forecasts present a fundamental advancement in water management. However, the design and evaluation of FRIO-based policies is hindered by the limited record length of ensemble hindcasts. To address this limitation, this work presents a multivariate synthetic ensemble forecast procedure to simulate complex, multi-site ensemble forecasts in any period where observational inflow data are available. We demonstrate the approach in a case study of the FIRO-based Ensemble Forecast Operations (EFO) control policy for the Lake Mendocino - Russian River basin, which conditions release decisions on ensemble forecasts from the Hydrologic Ensemble Forecast System (HEFS). We evaluate the synthetic forecasts using ensemble verification techniques and event-based validation, finding good agreement with the actual ensemble forecasts. We then evaluate EFO policy performance using synthetic and actual forecasts over the hindcast period (1985-2010) and synthetic forecasts over the pre-hindcast period (1948-1984). We find that the synthetic ensemble forecasts produce operational performance that closely matched operations using the HEFS hindcast, although some discrepancies remain during the largest flood events. We also show that the synthetic forecasts highlight important failure modes of the EFO policy under plausible forecast ensembles. Finally, we discuss the implications of a full-scale synthetic forecast framework in developing key components of FIRO, including the policy structure (through enhanced calibration-validation-testing) and key operational constraints (e.g., safety margin for sizing of the flood pool).
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H32D..05B