A high-fidelity multiresolution DEM model for Earth systems

Abstract

Abstract. The topographic impacts on modifying Earth systems variability have been well recognised. As numerical simulations evolved to incorporate broader scales and finer processes, accurately embedding the underlying topography to simulate land – atmosphere – ocean interactions, or performing commensurate scale transformation to topography while considering high-fidelity retention have proven to be quite difficult. Numerical schemes from Earth systems either use empirical parameterization as sub-grid scale and downscaling skills to express topographic endogenous processes, or rely on insecure point interpolation to induce topographic forcing, which create bias and input uncertainties. DEM generalisation provides systematic topographic transformation by considering loyal fidelity, but existing heuristic approaches are not performed optimally because of point clustering, or are difficult to incorporate into numerical systems because of sliver triangles. This article proposes a novel high-fidelity multiresolution DEM model with high-quality grids to meet the challenges of scale transformation. The generalized DEM model is initially approximated as a triangulated irregular network (TIN) via selected terrain feature points, control points, and possible embedded terrain features. The TIN surface is then optimized through an energy-minimized centroidal Voronoi tessellation (CVT). By devising a robust discrete curvature as a density function and exact geometry clipping as an energy reference, the developed curvature CVT (cCVT) converges, the generalized model evolves to a further approximation to the original DEM surface, and the points and their dual cells become equalized with the curvature distribution, exhibiting a quasi-uniform high-quality grid. The cCVT model is then evaluated on real LiDAR-derived DEM datasets compared to the classical heuristic method. The experimental results show that the cCVT multiresolution model outperforms classical heuristic DEM generalisations in terms of both surface approximation precision and surface morphology retention.