Digital elevation models (DEMs) play a key role in supporting fine scale representations of surface drainage structure. In fact, representing surface drainage structure has become a primary application of DEMs. It has directly driven the development of DEM interpolation techniques, such as the ANUDEM locally adaptive elevation gridding procedure (Hutchinson 1989; Hutchinson et al. 2009). High resolution remotely sensed elevation data sources, such as LIDAR and the Shuttle Radar Topography Mission (SRTM) DEM, have brought new challenges to DEM interpolation and applications. The inherent noise in remotely sensed elevation data needs to be appropriately smoothed to help remove drainage artifacts, especially in low relief landscapes. This smoothing also needs to be applied in conjunction with the enforcement of drainage structure using streamline data. The multi-grid structure of the ANUDEM algorithm is well suited to this task and has been upgraded to prevent corruption of stream heights by noisy elevation values. It has also been upgraded to accommodate the spatial complexity associated with high resolution streamline data. When incorporated onto a regular grid, these data can generate many spurious stream junctions and disjunctions that can significantly distort subsequent analysis of catchment structure. New procedures have been developed to make small shifts in the locations of gridded streamlines and streamline junctions to reduce the number of spurious coincident gridded streamline junctions while simultaneously maximising the fidelity of the shifted gridded streamlines to the original data streamline network. New analytical methods for calculating specific catchment area from drainage enforced DEMs, an important DEM derivative for many hydro-ecological applications, have also been obtained (Gallant and Hutchinson 2011). Such methods are best understood by recognising the vector field interpretation of specific catchment area.