Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh⁻¹. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

An open source subcatchment generator program was developed for the Stormwater Management Model (SWMM) to automate tedious stages in the model construction process. The generator divides the investigated area into subcatchments using a uniform computation grid and connects the grid cells together and to the underlying stormwater network. The system was tested by applying it to two small urban catchments with different fractions of impervious surfaces in Helsinki, Finland, using mostly openly available data. The simulated discharge results were compared to measured data and to results obtained from manually built models. The proposed system significantly accelerated the setup of a SWMM modelling project, as the routing between the subcatchments as well as the subcatchment slopes and flow widths were directly derived from the computation grid. Automatically generated and manually constructed SWMM models produced discharge results that differed only slightly from each other.

Urbanization modifies the natural water cycle particularly by reducing the water storage capacity. We analysed the storage capacity of three stormwater management designs in south-western Finland to demonstrate how an urban catchment releases stormwater and how storage contributes to flood resilience. The analysis relies on EPA SWMM5.1 simulations of water balance for a seven-month period including two extreme rain events during the summer and autumn. The enhanced storage capacity provided by the designs increased resilience against flooding and released stormwater with slower rates leading to reduced peak flows. Even the design with the least storage (10% LID coverage) was efficient at regulating floods due to controlled flow in a vegetated swale, whereas the design with the highest storage capacity (60% LID coverage) demonstrated the possibility of restoring nearly natural water cycle in urban catchments. The study suggests storage capacity can act as a flood resilience indicator directly linked with the physical catchment characteristics.

Rainfall accumulation depths over a given area are strongly dependent on the shape of the storm together with its direction of advection. A method to produce design storms exhibiting anisotropic spatial scaling is presented by combining a state‐of‐the‐art stochastic rainfall generator STEPS with the linear generalized scale invariance (GSI) notation. The enhanced model is used to create ensembles of design storms based on an extreme storm with a distinct rainband shape observed in Melbourne, Australia. Design storms are generated both with and without accounting for anisotropy. Effect of anisotropy on precipitation characteristics is studied using the entire region covered by the radar (radar scale) and at a significantly smaller catchment scale. A rainfall‐runoff model is applied to route the rainfall through the catchment into streamflow. Accounting for anisotropy allows for a more realistic description of precipitation features at the radar scale. At the catchment scale, anisotropy increases the probability of high rainfall accumulations, which translates into greater flood volumes. No discernible difference was observed in streamflow characteristics after controlling for the accumulation over the catchment. This could be explained by a lower importance of anisotropy relative to other factors affecting streamflow generation, and by the difficulties in creating representative rainfall temporal properties at the catchment scale when the radar scale is used for model calibration. The proposed method provides a tool to create ensembles of design storms when the anisotropic shape of the fields is of importance.