Is HEC-RAS 2D accurate enough for storm-event hazard assessment? Lessons learnt from a benchmarking study based on rain-on-grid modelling

Abstract

Rain-on-Grid (RoG) modelling is an increasingly popular approach within the storm risk management, in which the hydrological and hydrodynamic flood processes are modelled entirely within the 2D hydrodynamic model. Contrarily to what was done in the context of flood propagation in rivers, at the present time no systematic benchmarking study of the software packages implementing this approach is available in the literature. The need of benchmarking studies related to the RoG approach is becoming ever more important for HEC-RAS 2D (HR2D), the use of which has been rapidly increasing in the last few years, as further testified by very recent papers explicitly focussed on this research issue. However, there is still a lack of studies that clearly show the suitability of this software package as a reliable fully integrated hydrologic-hydrodynamic model. Therefore, the novelty introduced by this paper is the benchmarking of HR2D for RoG simulations, analysing several situations representing most of the applications related to “storm-event” hazard assessment. Specifically, after the analysis of ideal cases, which gave the opportunity to carefully assess the accuracy of the numerical schemes for RoG simulations, practical applications were considered to face some challenging issues related to the analysis of rainfall/runoff in urbanized areas and actual basins, including the numerical treatment of buildings and the computation of infiltration losses within hydrodynamic computations. The HR2D results were compared to analytical solutions, when available, experimental data and numerical solutions of a numerical code developed by the authors. The results shown in the paper highlight the potential but also the limitations of this software package, providing evidence to scientists and technicians about those situations for which reliable results are expected but also indications on specific aspects that need improvements so as to increase the model accuracy.