Referee comment on nhess-2022-111

Abstract

The disastrous July 2021 flooding events made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts. This is an urgent concern since extreme events are increasing due to global warming. For the July 2021 events, we simulated the hourly streamflows of seven catchments in western Germany, by combining five, partly polarimetric, radar-based quantitative precipitation estimates (QPE) with two hydrological models: a conceptual lumped model (GR4H) and a physically-based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows: (1) All radar-based QPE products underestimated the total precipitation depth relatively to rain gauges due to intense collision-coalescence processes near the surface, i.e. below the height levels monitored by the radars. (2) The use of polarimetric radar variables led to clear improvements compared to reflectivity-based QPE products. (3) The probability of exceeding the highest measured peakflow before July 2021 was highly impacted by the QPE product, and depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peakflows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPE for extreme floods. It also underlines the large uncertainty in peakflow estimates due to model parameter estimation.