Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Ensemble Forecast Operations (EFO) is a risk-based approach of reservoir flood control operations that incorporates ensemble streamflow predictions (ESPs) made by the California-Nevada River Forecast Center. Reservoir operations for each member of an ESP are individually modeled to forecast system conditions and calculate risk of reaching critical operational thresholds. Reservoir release decisions are simulated to manage forecasted risk with respect to established risk tolerance levels. EFO was developed for Lake Mendocino, a 111,000 acre-foot reservoir near Ukiah, California, to evaluate its viability to improve reservoir storage reliability without increasing downstream flood risk. Lake Mendocino is a dual use reservoir, owned and operated for flood control by the United States Army Corps of Engineers and operated for water supply by Sonoma Water. EFO was simulated using a 26-year (1985-2010) ESP hindcast generated by the California-Nevada River Forecast Center, which provides 61-member ensembles of 15-day flow forecasts. EFO simulations yield generally higher storage levels during the flood management season while maintaining needed flood storage capacity by strategically prereleasing water in advance of forecasted storms. Model results demonstrate a 33% increase in median storage at the end of the flood management season (10 May) over existing operations without marked changes in flood frequency for locations downstream from Lake Mendocino. EFO may be a viable alternative for managing flood control operations at Lake Mendocino that provides multiple benefits (water supply, flood mitigation, and ecosystems) and provides a management framework that could be adapted and applied to other flood control reservoirs.

Section: Discussionmentioning

confidence: 99%

Forecast Informed Reservoir Operations Using Ensemble Streamflow Predictions for a Multipurpose Reservoir in Northern California

Delaney¹,

Hartman²,

Mendoza³

et al. 2020

“…To simulate reservoir releases with a data‐driven scheme, we substitute generic operations with a constrained linear piecewise function parameterized for each of the 36 reservoirs in the CRB with sufficient observational records (see Turner et al, 2020). This includes eight very large reservoirs with storage capacity greater than 1,000 MCM, or 1 km 3 (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…A different function is trained for each week of the water year (thus 52 functions). The functions are interpretable and parsimonious, while being constrained to realistic reservoir operator behavior—a strategy we use to avoid overfit in a data‐scarce environment inhospitable to split‐sample calibration validation (see Turner et al, 2020) (a k ‐fold cross‐validation has been performed to ensure the efficacy of this strategy—see the supporting information). The scheme can be simulated at daily resolution by determining the week‐ahead release on each day, implementing a day's worth of that release, recalculating a new week‐ahead release the following day, and so on.…”

Section: Methodsmentioning

confidence: 99%

“…The differences in model performances between Experiment 3 (online) and Experiment 2 (off‐line) indicate the extent to which inflow biases inherent in the hydrological model affect the performance of the reservoirs under different operating configurations. This is important because the candidacy of data‐driven release schemes has thus far been based on assessments of their performances in off‐line mode (Coerver et al, 2018; Turner et al, 2020; Yassin et al, 2019) or against a benchmark of no reservoir representation (Yassin et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Each experiment tests three distinct reservoir release schemes: generic (Scheme A), as described in section 2.1; data‐driven based on time of year, storage and 1‐week (current period) inflow (Scheme B); and data‐driven based on time of year, storage, and inflow forecasts of varying predefined lead times (dynamic horizon—Scheme C), using the horizon curves reported in Turner et al (2020). Inflow forecasts have been shown to strongly influence seasonal reservoir operations, particularly for high elevation dams in Western United States fed by snowmelt (Turner et al, 2020). We adopt the forecast‐based model in this study to understand whether the benefits of representing forecasts are maintained when reservoirs are simulated within the LHM.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model

Turner

Doering

Voisin

2020

Self Cite

Large-scale hydrological and water resource models (LHMs) are used increasingly to study the vulnerability of human systems to water scarcity. These models rely on generic reservoir release schemes that often fail to capture the nuances of operations at individual dams. Here we assess whether empirically derived release-availability functions tailored to individual dams could improve the simulation performance of an LHM. Seasonally varying, linear piecewise relations that specify water release as a function of prevailing storage levels and forecasted future inflow are compared to a common generic scheme for 36 key reservoirs of the Columbia River Basin. When forced with observed inflows, the empirical approach captures observed release decisions better than the generic scheme-including under conditions of drought. The inclusion of seasonally varying inflow forecasts used by reservoir operators adds further improvement. When exposed to biases and errors inherent in the LHM, data-driven policies fail to offer a robust improvement; inclusion of forecasts deteriorates LHM reservoir simulation performance in some cases. We perform sensitivity analysis to explain this result, finding that the bias inherent in LHM streamflow is amplified by a reservoir model that relies on forecasts. To harness the potential of interpretable, data-driven reservoir operating schemes, research must address LHM flow biases arising from inaccuracies in climate input, runoff generation, flow routing, and water withdrawal and consumption data.

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

Herman

Quinn

Steinschneider

et al. 2020

152

111

Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions—in other words, framing climate adaptation as an optimal control problem. This paper uses the control paradigm to review and classify recent dynamic planning studies according to their approaches to uncertainty characterization, policy structure, and solution methods. We propose a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the unambiguous application of control methods to this problem. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human‐environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, several opportunities exist to improve the use of control methods for climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out‐of‐sample validation and robustness of optimized adaptation policies, and monitoring and data assimilation, including trend detection, Bayesian inference, and indicator variable selection. We conclude with a summary of recommendations for dynamic water resources planning under climate change through the lens of optimal control.