Establishing a Numerical Modeling Framework for Hydrologic Engineering Analyses of Extreme Storm Events
A modeling framework is necessary for deriving engineering parameters such as probable maximum precipitation that are the cornerstone of large water management infrastructure. Using three heavy storms associated with atmospheric rivers as test cases, researchers including a scientist at Pacific Northwest National Laboratory established an optimal modeling framework for hydrologic engineering design and analysis based on a systematic analysis of forecast skill from simulations using different model configurations.
This study explores a modeling framework that takes into consideration major uncertainty factors that contribute to the final model performance. This more physically-based method provides an important contribution to the engineering design and analyses community who are engaged in large water management infrastructure issues of today and the future.
This study aims at establishing a numerical modeling framework for simulating extreme storm events using the Weather Research and Forecasting (WRF) model. Such a framework is necessary for the derivation of engineering parameters such as probable maximum precipitation that are the cornerstone of large water management infrastructure design. The framework was built based on a heavy storm event that occurred in Nashville, TN in 2010, and verified using two other extreme storms in northern California and the Pacific Northwest, respectively. All storms were associated with atmospheric rivers making landfall in parts of the U.S. To achieve the optimal setup, the team evaluated several combinations of model resolutions, initial/boundary conditions (IC/BC), cloud microphysics, and cumulus parameterization schemes using multiple metrics of precipitation characteristics. Their evaluation suggests that WRF is most sensitive to the IC/BC option. Simulation generally benefits from finer resolutions up to 5 km, and higher-resolution simulations benefit from the use of higher-resolution data to provide the IC/BC. Based on systematic analysis, they established and validated an optimal framework as a good starting choice for modeling extreme events similar to the test cases. They propose this optimal framework in response to emerging engineering demands of extreme storm events forecasting and analyses for design, operations, and risk assessment of large water infrastructures.