Influence of Collision Cascade Statistics on Pattern Formation of Ion-Sputtered Surfaces

Theoretical continuum models that describe the formation of patterns on surfaces of targets undergoing ion-beam sputtering, are based on Sigmund's formula, which describes the spatial distribution of the energy deposited by the ion. For small angles of incidence and amorphous or polycrystalline materials, this description seems to be suitable, and leads to the classic BH morphological theory [R.M. Bradley and J.M.E. Harper, J. Vac. Sci. Technol. A 6, 2390 (1988)]. Here we study the sputtering of Cu crystals by means of numerical simulations under the binary-collision approximation. We observe significant deviations from Sigmund's energy distribution. In particular, the distribution that best fits our simulations has a minimum near the position where the ion penetrates the surface, and the decay of energy deposition with distance to ion trajectory is exponential rather than Gaussian. We provide a modified continuum theory which takes these effects into account and explores the implications of the modified energy distribution for the surface morphology. In marked contrast with BH's theory, the dependence of the sputtering yield with the angle of incidence is non-monotonous, with a maximum for non-grazing incidence angles.Comment: 12 pages, 13 figures, RevTe

A catchment based assessment of the 3-arc second SRTM digital elevation data set

Digital elevation models (DEMs) are three-dimensional representations of the earth's surface, providing a valuable source of data within geomorphological and hydrological studies. In recent years new methods for creating DEMs have become available, with many new data sets available for public use. The recently released Shuttle Radar Topography Mission (SRTM) 3-arc second DEM is an example of such a data set, providing an almost complete global coverage of the earth's land surface at a resolution of approximately 90m horizontal grid size. Concerns over the quality of these new data sets has seen an abundance of studies examining the effect of different DEM grid scales on their ability to accurately and reliably represent catchment form and function (Zhang and Montgomery, 1994). In this paper we examine the SRTM data for two Australian catchments of different climates, geology and resultant geomorphology. The SRTM data is compared with high resolution DEMs using basic hydrological and geomorphological statistics and descriptors including the area-slope relationship, hypsometric curve, width function, Strahler (1964) and stream networking statistics. The SRTM data was also assessed for catchment hydrology and runoff properties using the Hydrogeomorphic Steady State (HGSS) model of Willgoose and Perera (2001). Results demonstrate that the SRTM data provides a poor catchment representation (Figure 1). Hillslopes appear as a linked set of facets, displaying little of the complex curvature observed in high resolution data. While catchment area-slope and area-elevation (hypsometry) properties are largely correct, catchment area, relief and shape (as measured by the width function) are poorly captured by the SRTM data. The large grid size of the SRTM data also results in incorrect drainage network patterns and runoff properties. Consequently, for quantitative assessment of catchment hydrology and geomorphology care must be used, as in all cases SRTM derived catchment area is incorrect and smaller DEM grid sizes are required for accurate catchment wide assessment. (Graph Presented)

A comparison of SRTM and high-resolution digital elevation models and their use in catchment geomorphology and hydrology: Australian examples

The recently released Shuttle Radar Topography Mission (SRTM) 3-arc second digital elevation data set provides a complete global coverage of the Earth's land surface. In this paper we examine the SRTM data for three catchments in Australia over a range of climates, geology and resultant geomorphology. To test this new data set the SRTM data are compared with high resolution digital elevation models. We use basic hydrological and geomorphological statistics and descriptors such as the area-slope relationship, cumulative area distribution and hypsometric curve, along with Strahler and networking statistics. The above measures describe the surface morphology of a catchment, therefore integrating catchment geology, climate and vegetation. The SRTM data were also assessed as input into the SIBERIA landscape evolution and soil erosion model as were runoff properties, using a wetness index. The results demonstrate that the 90 m SRTM data provide a poor catchment representation. Hillslopes appear as a linked set of facets and display little of the complex curvature that is observed in high resolution data. While catchment area-slope and area-elevation (hypsometry) properties are largely correct, catchment area, relief and shape (as measured by the width function) are poorly captured by the SRTM data. Catchment networking statistics are also variable. The large grid size of the SRTM data also results in incorrect drainage network patterns and different runoff properties. Consequently, care must be used for quantitative assessment of catchment hydrology and geomorphology, as in all cases SRTM-derived catchment area is incorrect and smaller digital elevation grid sizes are required for accurate catchment-wide assessment. While only a limited number of catchments have been examined, we believe our findings are applicable to other areas. © Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO

Bedload transport, hydrology and river hydraulics in the Ngarradj Creek catchment, Jabiluka, Northern Territory, Australia

Rainfall, discharge and bedload were measured at three gauging stations in the Ngarradj Creek catchment at Jabiluka, Northern Territory. These gauging stations were East Tributary, Upper Swift Creek and Swift Creek, and all had catchment areas less than 45km². Hand-held pressure-difference Helley-Smith bedload samplers were used to measure bedload fluxes for the 1998/1999, 1999/2000, 2000/2001 and 2001/2002 wet seasons. The bedload sampling procedure involved the completion of two traverses of the channel with at least four measurement points on each traverse at East Tributary, five at Upper Swift Creek and six at Swift Creek. Minimum sample collection time was 120 seconds and the maximum was 660 seconds. These variations were determined by bedload flux so that no more than 40% of the sample bag was filled at a time