If the resolution is insufficient compared with what is needed for the runoff simulations, the accuracy of flood predictions is likely to be compromised ( Andréassian et al., 2001 Aronica et al., 2005 Bruni et al., 2015 Rafieeinasab et al., 2015).Īnother important issue besides resolution is the accuracy of the rainfall data themselves. Previous research has shown that in order to obtain reliable results in small urban catchments, the rainfall data should have a resolution of at least 10 min and 1 km ( Schilling, 1991 Ogden and Julien, 1994 Berne et al., 2004). Indeed, several studies have shown that the resolution of the rainfall data directly impacts the shape, timing and peak flow of hydrographs ( Aronica et al., 2005 Löwe et al., 2014 Ochoa-Rodriguez et al., 2015 Rico-Ramirez et al., 2015 Cristiano et al., 2017). The ability to measure short-duration, high-intensity rainfall rates is of paramount importance in predicting hydrological response. 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 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. 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. 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. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). 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. 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. However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. 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). 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. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. 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. 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. However, since there is no common benchmark, improvements are hard to quantify objectively. Each country has developed its own strategy for addressing this issue. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. The most important of them is that radar tends to underestimate rainfall compared to gauges. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response.
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