Comparison of distribution strategies in uncertainty-aware catchment delineation

Abstract

Delineation of drainage basins from a digital elevation model (DEM) has become a standard operation in a number of terrain analysis software packages, but limitations of the conventionally used techniques have become apparent. Firstly, the delineation methods make assumption of error-free data, which is an unreachable utopia even with modern sensor technology. Secondly, even though the computing capacity has increased dramatically during the last decades, sizes of geospatial data sets have increased simultaneously. Thus far, the typical problems arising when using uncertainty-aware geospatial analysis are 1) the computational complexity of the analysis and 2) memory allocation problems when large datasets are used. In this paper, we raise the question about the general need for developing scalable and uncertainty-aware algorithms for terrain analysis and propose improvements to the existing drainage basin calculation methods. The distributed uncertainty-aware catchment delineation methods with and without spatial partitioning of the DEM are introduced and the performance of the methods in different cases are compared.