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Flood projections are still highly uncertain, partly resulting from the limited accuracy of simulated precipitation by climate models. To overcome this limitation, recent studies suggest to use direct linkages between atmospheric processes leading precipitation, often better simulated than precipitation, and the flood occurrence. Such an approach implies, however, that historical flood events mainly result from direct contribution of precipitation only. Consequently, this paper has a twofold objective: (i) To explore to what extent the generation of medium-magnitude flood events in a large mountainous catchment can be explained by the precipitation only, and (ii) to identify what are the best features of flood-inducing precipitation episodes (i.e., duration and accumulation). Taking advantage of centennial-long discharge (gauge stations) and precipitation (ERA-20C reanalysis) data series, this study is based on three-year return period flood events of the upper Rhône River (NW European Alps). Our results suggest that half of the studied floods are triggered by precipitation only, but precipitation indices are mainly good only for high-magnitude events with return period of at least 20 years. Hence, modelling flood occurrence directly from atmospheric processes leading precipitation seems to be possible for events with the highest magnitude (i.e., the ones with the highest potential to impact societies). [ABSTRACT FROM AUTHOR]

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data. [ABSTRACT FROM AUTHOR]

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. [ABSTRACT FROM AUTHOR]