The heating of tungsten monoblocks at the ITER divertor vertical targets is calculated using the heat flux predicted by three-dimensional ion orbit modelling. The monoblocks are beveled to a depth of 0.5 mm in the toroidal direction to provide magnetic shadowing of the poloidal leading edges within the range of specified assembly tolerances, but this increases the magnetic field incidence angle resulting in a reduction of toroidal wetted fraction and concentration of the local heat flux to the unshadowed surfaces. This shaping solution successfully protects the leading edges from inter-ELM heat loads, but at the expense of (1) temperatures on the main loaded surface that could exceed the tungsten recrystallization temperature in the nominal partially detached regime, and (2) melting and loss of margin against critical heat flux during transient loss of detachment control. During ELMs, the risk of monoblock edge melting is found to be greater than the risk of full surface melting on the plasma-wetted zone. Full surface and edge melting will be triggered by uncontrolled ELMs in the burning plasma phase of ITER operation if current models of the likely ELM ion impact energies at the divertor targets are correct. During uncontrolled ELMs in pre-nuclear deuterium or helium plasmas at half the nominal plasma current and magnetic field, full surface melting should be avoided, but edge melting is predicted.

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The geometry of river channels is a key descriptive element for hydromorphology, hydraulics and hydroecology. Gravel bed rivers usually have a mean water depth of ~0·5 m. For such shallow waters, the accuracy of bathymetric LiDAR data has to be precisely assessed. Alongside this accuracy investigation, methodological questions arise: How to assess the data quality of elevation LiDAR when comparing reference topographic points on river beds to laser beam footprints of several square metres at different locations? What are the consequences of uncertainties and scaling in accuracy estimations? In this study, we designed a methodology to assess the quality of LiDAR topographical data within rivers using a specifi c geostatistical method that conducts upscaling as well as interpolation of reference data that takes into account uncertainties. This method uses an anisotropic block kriging from DGPS points on LiDAR footprint areas within a channel-fi tted coordinate system. This assessment focused on a 1·5 km long reach of the Gardon gravel bed river, in the south of France. DGPS points pseudo-regularly located along the river were acquired at the same time as the LiDAR survey with the HawkEyeII system. LiDAR accuracy results for river bottom elevation show a negative bias for high depth. Added to that bias, a random error with 0·32 m standard deviation was found by considering upscaling and uncertainties in reference data, and a 0·20 m standard deviation was found if they were not considered. Consequently, if LiDAR bias can be corrected, measuring a water depth less than 32 cm, i.e. for 28% of the river area, is unrealistic.However, this experiment shows that LiDAR provides an accurate representation of the riverbed forms. It also provided a useful, continuous, topographic surface from the underwater river bed up to riparian areas.