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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 machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States
Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
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- Award ID(s):
- 1652293
- PAR ID:
- 10368456
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Hydrological Processes
- Volume:
- 36
- Issue:
- 6
- ISSN:
- 0885-6087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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