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Abstract Forecast informed reservoir operations (FIRO) is an important advance in water management, but the design and testing of FIRO policies is limited by relatively short (10–35 year) hydro‐meteorological hindcasts. We present a novel, multisite model for synthetic forecast ensembles to overcome this limitation. This model utilizes parametric and non‐parametric procedures to capture complex forecast errors and maintain correlation between variables, lead times, locations, and ensemble members. After being fit to data from the hindcast period, this model can generate synthetic forecast ensembles in any period with observations. 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 explore two generation strategies: (a) simulation of synthetic forecasts of meteorology to force HEFS; and (b) simulation of synthetic HEFS streamflow forecasts directly. 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 only over the pre‐hindcast period (1948–1984). Results show that the synthetic forecasts highlight important failure modes of the EFO policy under plausible forecast ensembles, but improvements are still needed to fully capture FIRO policy behavior under the actual forecast ensembles. Overall, the methodology advances a novel way to test FIRO policy robustness, which is key to building institutional support for FIRO.
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A Multivariate Approach to Generate Synthetic Short-To-Medium Range Hydro-Meteorological Forecasts Across Locations, Variables, and Lead Times
The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10–40 years) that is insufficient for assessing risk related to forecast-informed decision-making during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables, forecast lead times, and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for (1) streamflow and (2) temperature and precipitation, which are based on hindcasts from the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.
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- Award ID(s):
- 1803563
- PAR ID:
- 10276722
- Date Published:
- Journal Name:
- Water resources research
- Volume:
- 57
- Issue:
- 6
- ISSN:
- 1944-7973
- Page Range / eLocation ID:
- e2020WR029453
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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