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In this paper, the results of a benchmark test launched within the framework of the NSF–PIRE project “Modelling of Flood Hazards and Geomorphic\ud Impacts of Levee Breach and Dam Failure” are presented. Experiments of two-dimensional dam-break flows over a sand bed were conducted at\ud Université catholique de Louvain, Belgium. The water level evolution at eight gauging points was measured as well as the final bed topography.\ud Intense scour occurred close to the failed dam, while significant deposition was observed further downstream. From these experiments, a benchmark\ud was proposed to the scientific community, consisting of blind test simulations, that is, without any prior knowledge of the measurements. Twelve\ud different teams of modellers from eight countries participated in the study. Here, the numerical models used in this test are briefly presented. The results\ud are commented upon, in view of evaluating the modelling capabilities and identifying the challenges that may open pathways for further research

This paper presents a 1D-2D dual drainage model to compute the rainfall-runoff transformation in urban environments. Overland flow in major drainage systems is modelled with the 2D shallow water equations, whereas the flow in a sewer network is computed with the 1D Saint-Venant equations using the two-component pressure approach to model pressureflow conditions. The surface and sewer network models are linked through manholes, which allow water interchange in both directions. A new series of rainfall -runoff experiments in a real-scale physical model of a street section is used to validate the model under unsteady part-full and pressure flow conditions. The experimental measurements of water depth and discharge at several locations in a drainage network show a very satisfactory performance of the numerical model.

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km2 located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.

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