International audienceUrban catchments are typically characterised by high spatial variability and fast runoff processes resulting in short response times. Hydrological analysis of such catchments requires high resolution precipitation and catchment information to properly represent catchment response. This study investigated the impact of rainfall input resolution on the outputs of detailed hydrodynamic models of seven urban catchments in North-West Europe. The aim was to identify critical rainfall resolutions for urban catchments to properly characterise catchment response. Nine storm events measured by a dual-polarimetric X-band weather radar, located in the Cabauw Experimental Site for Atmospheric Research (CESAR) of the Netherlands, were selected for analysis. Based on the original radar estimates, at 100m and 1min resolutions, 15 different combinations of coarser spatial and temporal resolutions, up to 3000m and 10min, were generated. These estimates were then applied to the operational semi-distributed hydrodynamic models of the urban catchments, all of which have similar size (between 3 and 8km2), but different morphological, hydrological and hydraulic characteristics. When doing so, methodologies for standardising model outputs and making results comparable were implemented. Results were analysed in the light of storm and catchment characteristics. Three main features were observed in the results: (1) the impact of rainfall input resolution decreases rapidly as catchment drainage area increases; (2) in general, variations in temporal resolution of rainfall inputs affect hydrodynamic modelling results more strongly than variations in spatial resolution; (3) there is a strong interaction between the spatial and temporal resolution of rainfall input estimates. Based upon these results, methods to quantify the impact of rainfall input resolution as a function of catchment size and spatial-temporal characteristics of storms are proposed and discussed. © 2015 The Authors

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Urban stormwater models can be semi-distributed (SD) or fully distributed (FD). SD models are based on subcatchment units with various land use types, where rainfall is applied and runoff volumes are estimated and routed. FD models are based on the two dimensional (2D) discretization of the overland surface, which has a finer resolution with each grid-cell representing one land use type, where runoff volumes are estimated and directly routed by the 2D overland flow module. While SD models have been commonly applied in urban stormwater modeling, FD models are generally more detailed and theoretically more realistic. This paper presents a comparison between SD and FD models using two case studies in Coimbra (Portugal) and London (UK). To enable direct comparison between SD and FD setups, a model-building process is proposed and a novel sewer inlet representation is applied. SD and FD modeling results are compared against observed records in sewers and photographic records of flood events. The results suggest that FD models are more sensitive to surface storage parameters and require higher detail of the sewer network representation.

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially-and temporally-variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km 2 ) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard OPEN ACCESSWater 2015, 7 3397 maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions.