Hand in hand with the increasing availability of high-resolution digital elevation models ({DEM}), an efficient computation of land-surface parameters ({LSPs}) for large-scale digital elevation models becomes more and more important, in particular for web-based applications. Parallel processing using multi-threads on multi-core processors is a standard approach to decrease computing time for the calculation of local {LSPs} based on moving window operations (e.g. slope, curvature). {LSPs} which require non-localities for their calculation (e.g. hydrological connectivities of grid cells) make parallelization quite challenging due to data dependencies. On the example of the calculation of the {LSP} 'flow accumulation', we test the two parallelization strategies 'spatial decomposition' and 'two phase approach' for their suitability to manage non-localities. Three datasets of digital elevation models with high spatial resolutions are used in our evaluation. These models are representative types of landscape of Central Europe with highly diverse geomorphic characteristics: a high mountains area, a low mountain range, and a floodplain area in the lowlands. Both parallelization strategies are evaluated with regard to their usability on these diversely structured areas. Besides the correctness analysis of calculated relief parameters (i.e. catchment areas), priority is given to the analysis of speed-ups achieved through the deployed strategies. As presumed, local surface parameters allow an almost ideal speed-up. The situation is different for the calculation of non-local parameters which requires specific strategies depending on the type of landscape. Nevertheless, still a significant decrease of computation time has been achieved. While the speed-ups of the computation of the high mountain data set are higher by running the 'spatial decomposition approach' (3:2 by using four processors and 4:2 by using eight processors), the speed-ups of the 'two phase approach' have proved to be more efficient for the calculation of the low mountain and the floodplain data set (2:6 by using four processors and 2:9 by using eight processors). There, more non-localities in flat areas (e.g. filled sinks and floodplains) occur.

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