International audienceThis paper reports a radiofrequency identification (RFID) tracing experiment implemented in a high-sedimentload mountain stream typical of alpine gravel-bed torrents. The study site is the Bouinenc Torrent, a tributary to the Bléone River in southeast France that drains a 38.9-km² degraded catchment. In spring 2008, we deployed 451 tracers with b-axis ranging from 23 to 520 mm. Tracers were seeded along eight cross-sections located in the upstream part of the lowest 2.3 km of the stream. Three tracer inventories were implemented in July 2008, 2009 and 2010. Recovery rates calculated for mobile tracers declined from 78% in 2008 to 45% in 2009 and 25% in 2010. Observations of tracer displacement revealed very high sediment dispersion, with frontrunners having travelled more than 2 km only three months after their deployment. The declining recovery rate over time was interpreted as resulting from rapid dispersion rather than deep burial. We evaluated that 64% of the tracers deployed in the active channel were exported from the 2.3-km study reach three years after the onset of the tracing experiment. Travel distances were characterized by right-skewed and heavy-tailed distributions, correctly fitted by a power-law function. This supports the idea that in gravel-bed rivers with abundant sediment supply relative to transport capacity, bedload transport can be viewed as a superdiffusive sediment dispersion process. It is also shown that tracers initially deployed in the low-flow channel were characterized by a 15- to 30-fold increase of mobility compared to tracers deployed in gravel bars

The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique-Application et Recherche or Critical Zone Observatories-Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between "the rock and the sky," the Earth's skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict ("earthcast") the fluxes of water and matter of the Earth's near surface and how they will change in response to the "new climatic regime." The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ , and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project.

Abstract. Using the original setup described in Gallée et al. (2013), the MAR regional climate model including a coupled snowpack/aeolian snow transport parameterization, was run at a fine spatial (5 km horizontal and 2 m vertical) resolution over 1 summer month in coastal Adélie Land. Different types of feedback were taken into account in MAR including drag partitioning caused by surface roughness elements. Model outputs are compared with observations made at two coastal locations, D17 and D47, situated respectively 10 and 100 km inland. Wind speed was correctly simulated with positive values of the Nash test (0.60 for D17 and 0.37 for D47) but wind velocities above 10 m s −1 were underestimated at both D17 and D47; at D47, the model consistently underestimated wind velocity by 2 m s −1 . Aeolian snow transport events were correctly reproduced with the right timing and a good temporal resolution at both locations except when the maximum particle height was less than 1 m. The threshold friction velocity, evaluated only at D17 for a 7-day period without snowfall, was overestimated. The simulated aeolian snow mass fluxes between 0 and 2 m at D47 displayed the same variations but were underestimated compared to the second-generation FlowCapt ™ values, as was the simulated relative humidity at 2 m above the surface. As a result, MAR underestimated the total aeolian horizontal snow transport for the first 2 m above the ground by a factor of 10 compared to estimations by the second-generation FlowCapt ™ . The simulation was significantly improved at D47 if a 1-order decrease in the magnitude of z 0 was accounted for, but agreement with observations was reduced at D17. Our results suggest that z 0 may vary regionally depending on snowpack properties, which are involved in different types of feedback between aeolian transport of snow and z 0 .

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Knowledge of snow particle speeds is necessary for deepening our understanding of the internal structures of drifting snow. In this study, we utilized a snow particle counter (SPC) developed to observe snow particle size distributions and snow mass flux. Using high-frequency signals from the SPC transducer, we obtained the sizes of individual particles and their durations in the sampling area. Measurements were first conducted in the field, with more precise measurements being obtained in a boundary layer established in a cold wind tunnel. The obtained results were compared with the results of a numerical analysis. Data on snow particle speeds, vertical velocity profiles, and their dependence on wind speed obtained in the field and in the wind tunnel experiments were in good agreement: both snow particle speed and wind speed increased with height, and the former was always 1 to 2 m s À1 less than the latter below a height of 1 m. Thus, we succeeded in obtaining snow particle speeds in drifting snow, as well as revealing the dependence of particle speed on both grain size and wind speed. The results were verified by similar trends observed using random flight simulations. However, the difference between the particle speed and the wind speed in the simulations was much greater than that observed under real conditions. Snow transport by wind is an aeolian process. Thus, the findings presented here should be also applicable to other geophysical processes relating to the aeolian transport of particles, such as blown sand and soil.