Using reduced complexity models to estimate debris flow timing in post-wildfire watersheds

Francis Rengers, Research Geologist, U.S. Geological Survey

Post-wildfire natural hazards such as flooding and debris flows threaten infrastructure and can lead to loss of life. The risk from these natural hazards could be reduced if floods and debris flows could be predicted from modeling.  Our ability to test predictive models is primarily constrained by a lack of observational data that can be used for comparison with model predictions.  Following the 2009 Station Fire in the San Gabriel Mountains, CA, USA we were able to collect unique observational data of debris flow stage, rainfall, and basin topography.  We leveraged this natural experiment to conduct a study to test the effectiveness of two different hydrologic routing models to predict flood and debris flow timing.   Our research focuses on comparing the performance of two hydrologic models with differing levels of complexity and efficiency using high-resolution, lidar-derived digital elevation models.  The simpler model uses the kinematic wave approximation to route flows, while the more complex model uses the full shallow water equations.  In both models precipitation is spatially uniform and infiltration is simulated using the Green-Ampt infiltration equation.  The effective models were extended to ultimately test nine basins with debris flow stage data, and we found the model simulations and observational data typically matched to within a few minutes. This work shows that using rainfall-runoff models can capture much of the real debris flow timing observed in steep basins.