Street Level Hydrology: An Urban Application of the WRF-Hydro Framework in Denver, Colorado

Friday, 18 December 2015
Poster Hall (Moscone South)
Terri S Hogue, Colorado School of Mines, Civil and Environmental Engineering, Golden, CO, United States, Laura Read, Tufts University, Department of Civil and Environmental Engineering, Medford, MA, United States, Fernando R Salas, University of Texas at Austin, Austin, TX, United States and David Gochis, National Center for Atmospheric Research, Boulder, CO, United States
Urban flood modeling at the watershed scale carries unique challenges in routing complexity, data resolution, social and political issues, and land surface – infrastructure interactions. The ability to accurately trace and predict the flow of water through the urban landscape enables better emergency response management, floodplain mapping, and data for future urban infrastructure planning and development. These services are of growing importance as urban population is expected to continue increasing by 1.84% per year for the next 25 years, increasing the vulnerability of urban regions to damages and loss of life from floods. Although a range of watershed-scale models have been applied in specific urban areas to examine these issues, there is a trend towards national scale hydrologic modeling enabled by supercomputing resources to understand larger system-wide hydrologic impacts and feedbacks. As such it is important to address how urban landscapes can be represented in large scale modeling processes. The current project investigates how coupling terrain and infrastructure routing can improve flow prediction and flooding events over the urban landscape. We utilize the WRF-Hydro modeling framework and a high-resolution terrain routing grid with the goal of compiling standard data needs necessary for fine scale urban modeling and dynamic flood forecasting in the urban setting. The city of Denver is selected as a case study, as it has experienced several large flooding events in the last five years and has an urban annual population growth rate of 1.5%, one of the highest in the U.S. Our work highlights the hydro-informatic challenges associated with linking channel networks and drainage infrastructure in an urban area using the WRF-Hydro modeling framework and high resolution urban models for short-term flood prediction.