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The fine-scale precipitation of NbC in ferrite has been quantitatively characterized in the temperature range 873–1073 K for two alloy compositions, containing respectively 800 p.p.m. Nb and 400 p.p.m. Nb (by weight). Transmission electron microscopy (TEM) has revealed that the precipitates are located on dislocations, and have a plate-like morphology with an average aspect ratio between 2 and 3. Small-angle neutron scattering (SANS) has been systematically used to determine the precipitation kinetics. The validity of the quantitative SANS measurements of size and volume fraction has been assessed by TEM image analysis and chemical dissolution experiments. The precipitation kinetics is observed to depend strongly on temperature but to be similar for the two alloy compositions. From the measurements, it is inferred that precipitate nucleation is extremely rapid, in relation to the nature of the nucleation sites. A time–temperature transformation diagram is built from the kinetic data, showing a maximum reaction rate between 973 and 1073 K.

Abstract. Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing lowflow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storagetype models -Gardenia, GR6J, Mordor and Presages -and one distributed physically oriented model -SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average.

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