2018 – A Robust and Efficient Stochastic Simulation Framework for Estimating Reservoir Stage-Frequency Curves with Uncertainty Bounds

C. Haden Smith

The U.S. Army Corps of Engineers (USACE) Risk Management Center (RMC) developed the Reservoir Frequency Analysis software (RMC-RFA) to facilitate, enhance, and expedite flood hazard assessments within the USACE Dam Safety Program. RMC-RFA is a stochastic flood modeling software that employs advanced statistical and computing techniques, allowing a user to perform a screening-level stage-frequency analysis on a desktop PC with runtimes on the order of seconds to a few minutes. RMC-RFA utilizes an inflow volume-based stochastic simulation framework that treats the seasonal occurrence of the flood event, the antecedent reservoir stage, inflow volume, and the inflow flood hydrograph shape as uncertain variables rather than fixed values. In order to construct uncertainty bounds for reservoir stage-frequency estimates, RMC-RFA employs a two looped, nested Monte Carlo methodology. The natural variability of the reservoir stage is simulated in the inner loop defined as a realization, which comprises many thousands of events, while the knowledge uncertainty in the inflow volume-frequency distribution is simulated in the outer loop, which comprises many realizations.

Stage-frequency curves derived with RMC-RFA are compared to those derived with more complex, precipitation-based simulation frameworks, such as the Monte Carlo Reservoir Analysis Model (MCRAM), the Stochastic Event Flood Model (SEFM), and the Watershed Analysis Tool (HEC-WAT). The inflow volume-based framework employed by RMC-RFA produces stage-frequency curves that strongly agree with the more complex, precipitation-based methods. Furthermore, the results from the alternative methods fall within the RMC-RFA uncertainty bounds, demonstrating its robustness. In this sense, the RMC-RFA simulation framework lends itself to a value of information approach to risk management, where knowledge uncertainty can be efficiently quantified at a screening-level assessment, and then the value of performing more complex and sophisticated studies to reduce uncertainty can be considered.


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