International collaboration between research institutes and universities is a promising way to reach consensus on hydrological model development. Although model comparison studies are very valuable for international cooperation, they do often not lead to very clear new insights regarding the relevance of the modelled processes. We hypothesise that this is partly caused by model complexity and the comparison methods used, which focus too much on a good overall performance instead of focusing on a variety of specific events. In this study, we use an approach that focuses on the evaluation of specific events and characteristics. Eight international research groups calibrated their hourly model on the Ourthe catchment in Belgium and carried out a validation in time for the Ourthe catchment and a validation in space for nested and neighbouring catchments. The same protocol was followed for each model and an ensemble of best-performing parameter sets was selected. Although the models showed similar performances based on general metrics (i.e. the Nash–Sutcliffe efficiency), clear differences could be observed for specific events. We analysed the hydrographs of these specific events and conducted three types of statistical analyses on the entire time series: cumulative discharges, empirical extreme value distribution of the peak flows and flow duration curves for low flows. The results illustrate the relevance of including a very quick flow reservoir preceding the root zone storage to model peaks during low flows and including a slow reservoir in parallel with the fast reservoir to model the recession for the studied catchments. This intercomparison enhanced the understanding of the hydrological functioning of the catchment, in particular for low flows, and enabled to identify present knowledge gaps for other parts of the hydrograph. Above all, it helped to evaluate each model against a set of alternative models.@en

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very short range rainfall forecasting (less than 6 hr ahead). We performed a large-sample analysis of 1,533 events, systematically selected for 4 event durations and 12 lowland catchments (6.5–957 km2), to determine the predictive skill of nowcasting. Four algorithms are tested and compared with Eulerian Persistence: Rainymotion Sparse, Rainymotion DenseRotation, Pysteps deterministic, and Pysteps probabilistic with 20 ensemble members. We focus on the dependency of nowcast skill on event duration, season, catchment size, and location. Maximum skillful lead times increase for longer event durations, due to the more persistent character of these events. For all four event durations, Pysteps deterministic attains the longest average decorrelation times, with 25 min for 1-hr durations, 40 min for 3 hr, 56 min for 6 hr, and 116 min for 24 hr. During winter, with more persistent stratiform precipitation, we find three times lower mean absolute errors than for convective summer precipitation. Higher skill is also found after spatially upscaling the forecast. Catchment location matters too: Given the prevailing storm movement, two times higher skillful lead times are found downwind than upwind toward the edge of the domain. In most cases, Pysteps algorithms outperform the Rainymotion benchmark algorithms. We speculate that most errors originate from growth and dissipation processes which are not or only partially (stochastically) accounted for.@en

Accurate and timely precipitation forecasts are crucial for early warning. Rainfall nowcasting, the process of statistically extrapolating recent rainfall observations, is increasingly used for short-term forecasting. Nowcasts are generally constructed with high-resolution radar observations. As a proof of concept, we construct nowcasts with country-wide rainfall maps estimated from signal level data of commercial microwave links (CMLs) for 12 summer days in the Netherlands. CML nowcasts compare well to radar rainfall nowcasts. Provided well-calibrated CML rainfall estimates are employed, CML nowcasts can outperform unadjusted real-time radar nowcasts for high rainfall rates, which are underestimated as compared to a reference. Care should be taken with the sensitivity of the advection field derivation to areas with low CML coverage and the inherent measurement scale of CML data, which can be larger than the application scale. We see potential for rainfall nowcasting with CML data, for example, in regions where weather radars are absent.@en