UC Irvine

Floods are among the most devastating natural disasters which affect millions of people worldwide. Forecasting floods to provide warnings to the public in a timely manner is crucially important, however, this is a very challenging task. HiResFlood-UCI was developed by coupling the National Weather Service's (NWS) distributed hydrologic model (HL-RDHM) with the hydraulic model BreZo (developed by Sanders and Begnudelli) in order to estimate localized flood depths and velocities. A semi-automated technique of efficient unstructured mesh generation for BreZo was developed. HiResFlood-UCI was implemented for the ELDO2 catchment in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10m versus 30m), and computation mesh resolution (10m+ versus 30m+). Simulations show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution, and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using six measured precipitation events as model input for validation of the streamflow at the outlet and an interior point. Validation builds confidence in model predictions of river discharge, flood extent and localized velocities, which are fundamental to reliable flood warning.

HiResFlood-UCI was implemented for flood forecasting in the Cedar River Basin using real-time remote sensing precipitation PERSIANN-CCS data. The model was evaluated for the historical 2008 Iowa flood. The results show HiResFlood-UCI with real-time PERSIANN-CCS was able to capture the observed hydrographs and reasonably match the USDA's AWiFS 56m resolution flood imagery over the most impacted area in the extended Cedar Rapids region. This is promising for a global high resolution flood warning system pairing HiResFlood-UCI with PERSIANN-CCS in the near future.