A two-dimensional numerical model (RiverFLO-2D) has been enhanced to simulate flooding of urban areas by developing an innovative wet and dry surface algorithm, accounting for variable rainfall, and recoding the model computer program for parallel computing. The model formulation is based on the shallow water equations solved with an explicit time-stepping element-by-element finite element method. The dry-wet surface algorithm is based on a local approximation of the continuity and momentum equations for elements that are completely dry. This algorithm achieves global volume conservation in the finite element, even for flows over complex topographic surfaces. A new module was implemented to account for variable rainfall in space and time using NEXRAD precipitation estimates. The resulting computer code was parallelized using OpenMP Application Program Interface, which allows the model to run up to 5 times faster on multiple core computers. The model was verified with analytical solutions and validated with laboratory and field data. Model application to the Malpasset dam break and Sumacarcel flooding event show that the model accurately predicts flood wave travel times and water depths for these numerically demanding real cases. To illustrate the predictive capability of the enhanced model, an application was made of the city of Sweetwater flooding in Miami-Dade County, FL caused by the Hurricane Irene. The simulation starts with dry bed and rainfall is provided by NEXRAD estimates. Integrating NEXRAD rainfall estimates, developing a novel dry-wet area algorithm and parallelizing RiverFLO-2D code, this dissertation presents a proof of concept to accurately and efficiently predict floods in urban areas, identifying future improvements along this line of research.
| Identifer | oai:union.ndltd.org:UMIAMI/oai:scholarlyrepository.miami.edu:oa_dissertations-1652 |
| Date | 12 May 2010 |
| Creators | Gonzalez-Ramirez, Noemi |
| Publisher | Scholarly Repository |
| Source Sets | University of Miami |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Open Access Dissertations |
Page generated in 0.0017 seconds