Radar‐rain gauge merging techniques have been widely used to improve the applicability of radar and rain gauge rainfall estimates by combining their advantages, while partially overcoming their individual weaknesses. Despite significant research in this area, guidance on the suitability of and factors affecting merging techniques at the fine spatial‐temporal resolutions required for urban hydrological applications is still insufficient. In this paper, an in‐depth review of radar‐rain gauge merging techniques is conducted, with a focus on their potential for urban hydrological applications. An overview is first given of existing merging techniques and an application‐oriented categorization is proposed: (1) radar bias adjustment methods, (2) rain gauge interpolation methods using radar spatial association as additional information, and (3) radar‐rain gauge integration methods. A detailed review is given of studies focusing on the evaluation and intercomparison of merging methods, based upon which the most widely used and best performing techniques from each category are identified. These are mean field bias adjustment, kriging with external drift, and Bayesian merging. Climatological, operational, and methodological factors affecting merging performance are then reviewed and their relevance for urban applications discussed. Based on this review, conclusions on merging potential for urban applications are drawn and research gaps are identified, which should be addressed to provide further guidance on the use of merging techniques for urban hydrological applications.

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 km²) 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 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.

Following extensive surface water flooding (SWF) in England in summer 2007, progress has been made in improving the management and prediction of this type of flooding. A rainfall threshold‐based extreme rainfall alert (ERA) service was launched in 2009 and superseded in 2011 by the surface water flood risk assessment (SWFRA). Through survey responses from local authorities (LAs) and the outcome of workshops with a range of flood professionals, this paper examines the understanding, benefits, limitations and ways to improve the current SWF warning service. The current SWFRA alerts are perceived as useful by district and county LAs, although their understanding of them is limited. The majority of LAs take action upon receipt of SWFRA alerts, and their reactiveness to alerts appears to have increased over the years and as SWFRA superseded ERA. This is a positive development towards increased resilience to SWF. The main drawback of the current service is its broad spatial resolution. Alternatives for providing localised SWF forecast and warnings were analysed, and a two‐tier national‐local approach, with pre‐simulated scenario‐based local SWF forecasting and warning systems, was deemed most appropriate by flood professionals given current monetary, human and technological resources.

Fractal analysis relies on scale invariance and the concept of fractal dimension enables one to characterize and quantify the space filled by a geometrical set exhibiting complex and tortuous patterns. Fractal tools have been widely used in hydrology but seldom in the specific context of urban hydrology. In this paper, fractal tools are used to analyse surface and sewer data from 10 urban or peri-urban catchments located in five European countries. The aim was to characterize urban catchment properties accounting for the complexity and inhomogeneity typical of urban water systems. Sewer system density and imperviousness (roads or buildings), represented in rasterized maps of 2 m × 2 m pixels, were analysed to quantify their fractal dimension, characteristic of scaling invariance. The results showed that both sewer density and imperviousness exhibit scale-invariant features and can be characterized with the help of fractal dimensions ranging from 1.6 to 2, depending on the catchment. In a given area consistent results were found for the two geometrical features, yielding a robust and innovative way of quantifying the level of urbanization. The representation of imperviousness in operational semi-distributed hydrological models for these catchments was also investigated by computing fractal dimensions of the geometrical sets made up of the sub-catchments with coefficients of imperviousness greater than a range of thresholds. It enables one to quantify how well spatial structures of imperviousness were represented in the urban hydrological models.