Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/2/2012
Publication Date: 4/10/2012
Citation: Hutton, C., Brazier, R., Nicholas, A., Nearing, M.A. 2012. On the effects of improved cross-section representation in one dimensional flow routing models applied to ephemeral rivers. Water Resources Research. 48: 1-11. Interpretive Summary: Accurate flood prediction in Semi-Arid Environments is difficult due to uncertainties in rainfall and in model calibration, yet the hazard and cost of flash flooding still remains. The paper has demonstrated that incorporating high resolution topographic data into the structure of a flow routing model can improve on existing approaches for representing river morphology in such models, and lead to more robust flash flood prediction in ephemeral semi-arid rivers. Incorporating such datasets into existing models applied for flash flood prediction is increasingly feasible given the now wide availability of topographic products from Light Detection and Ranging (LiDAR) platforms. Such datasets can help contribute to improved flash flood prediction, and therefore more robust flood warnings
Technical Abstract: Flash floods are an important component of the semi-arid 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 1D 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, U.S. 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 behavioural parameter sets across runoff events. In contrast, parameter interaction within K-Tr resulted in poorly constrained behavioural parameter sets across the multi-dimensional parameter space. These results, revealed with a modelling 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.