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Abstract. Precipitation is the key factor controlling the highfrequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in a hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasting and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole country of France at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semidistributed mode using three unit sizes of subcatchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of French catchments (181) representing a variety of sizes and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semidistributed model in western France, where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. By contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.

Le comportement du signal radar TerraSAR-X en fonction des paramètres du sol (rugosité, humidité, structure) a été analysé sur des données 2009 et 2010. Les résultats montrent que la sensibilité du signal radar à l'humidité est plus importante pour des faibles incidences (25° en comparaison à 50°). Pour des fortes valeurs d'humidité, le signal TerraSAR-X est plus sensible à la rugosité du sol à forte incidence (50°). La forte résolution spatiale des données TerraSAR-X (1 m) permet de détecter la croûte de battance à l'échelle intra parcellaire. / Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5 GHz) data acquired in 2009 and 2010. We analyzed the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (N25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°). The high spatial resolution of the TerraSAR-X data (1 m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust

Abstract. As all hydrological models are intrinsically limited hypotheses on the behaviour of catchments, models -which attempt to represent real-world behaviour -will always remain imperfect. To make progress on the long road towards improved models, we need demanding tests, i.e. true crash tests. Efficient testing requires large and varied data sets to develop and assess hydrological models, to ensure their generality, to diagnose their failures, and ultimately, help improving them.

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