Reservoir Flood Storage Capacity as an Indicator of Potential Forecast-Informed Prerelease Effectiveness
Abstract
Abstract The operation of multipurpose reservoirs for which flood risk management (FRM) is a primary or key purpose may be improved by the use of Forecast-Informed Reservoir Operations (FIRO). The ability to screen a portfolio of dams to assess FIRO viability allows for more-efficient use of time and funding than individual detailed analyses for several dams. In this study, screening criteria were used to evaluate the potential effectiveness of FIRO prestore and prerelease (PS-PR) and basic operation prerelease (BO-PR) methods using a large data set (58 dams) and along three lines of inquiry. A regression model was used to assess system characteristics and identify the reservoirs for which reservoir filling is most sensitive to changes in flood storage capacity. Historic flood events provided insight into the feasibility of FIRO based on seasonality and storm characteristics. Physical and policy constraints were studied to assess the feasibility of FIRO at or near top of conservation pool and to provide an initial input to the screening framework. A four-parameter equation to predict reservoir-filling frequency had an R 2 value of 0.67, and flood storage capacity was significant at a 99% confidence level. Review of historic reservoir-filling events showed a median 3-day flood storage of 23% and a 7-day flood storage of 38%. Policy and physical release constraints yielded maximum daily drawdown estimates for each reservoir and indicated a median of 2% of average annual inflow evacuation per day. A case study of five dams was used to assess viability of using the storage-based regression as a criterion for prerelease screening. The presented screening approach utilizes an estimate of maximum prerelease drawdown and the regression equations to provide high estimates for potential FIRO effectiveness (with uncertainty); actual applicability depends on forecast skill and available lead time, which are not evaluated in this study.
// Source
Authors: Trevor W. Timberlake, John Jairo Ramirez-Avila, Neil S. Grigg, David C. Curtis
Institutions: Colorado State University, Mississippi State University, United States Army Corps of Engineers