Flash floods are an important component of the semiarid hydrological cycle, and provide the potential for groundwater recharge as well as posing a dangerous natural hazard. A number of catchment models have been applied to flash flood prediction; however, in general they perform poorly. This study has investigated whether the incorporation of light detection and ranging (lidar) derived data into the structure of a 1-D flow routing model can improve the prediction of flash floods in ephemeral channels. Two versions of this model, one based on an existing trapezoidal representation of cross-section morphology (K-Tr), and one that uses lidar data (K-Li) were applied to 5 discrete runoff events measured at two locations on the main channel of The Walnut Gulch Experimental Watershed, United States. In general, K-Li showed improved performance in comparison to K-Tr, both when each model was calibrated to individual events and during an evaluation phase when the models (and parameter sets) were applied across events. Sensitivity analysis identified that the K-Li model also had more consistency in behavioral parameter sets across runoff events. In contrast, parameter interaction within K-Tr resulted in poorly constrained behavioral parameter sets across the multidimensional parameter space. These results, revealed with a modeling focus on the structure of a particular element of a distributed catchment model, suggest that lidar derived cross-section morphology can lead to improved, and more robust flash flood prediction.