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<p>Digital elevation models (DEMs) depression processing is a critical preprocessing step in hydrological modelling. In this study, the evolution of depression processing algorithms across DEMs resolutions was systematically examined, tracing the paradigm shift from traditional data modification methods for coarse-resolution DEMs to modern data preservation strategies for high-precision DEMs that maintain original terrain fidelity. Quantitative analysis of the acceleration ratio indicator revealed that algorithmic engineering efficiency can differ by as much as 189% under the same time complexity, which highlights the disparity between theoretical complexity and actual performance. Current research faces three principal challenges in processing ultra-large-scale data: the influence of geographical characteristics on data structures, performance bottlenecks in traditional algorithms, and the efficiency limitations of serial computation. Future research should concentrate on three key areas: (1) developing novel data structures based on enhanced binary tree encoding to mitigate the effects of geographical characteristics; (2) integrating artificial intelligence technologies to address the limitations of traditional algorithms; and (3) constructing parallel computing frameworks. These advances are expected to significantly increase the accuracy of surface water flow simulations and facilitate the comprehensive application of DEMs in hydrology.</p>

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From data modification to preservation: evolution of DEM depression processing algorithms in hydrological modelling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

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  • Lejun Ma,
  • Huan Wang,
  • Xingnan Zhang,
  • Yue Yuan,
  • Xiaodong Yuan

摘要

Digital elevation models (DEMs) depression processing is a critical preprocessing step in hydrological modelling. In this study, the evolution of depression processing algorithms across DEMs resolutions was systematically examined, tracing the paradigm shift from traditional data modification methods for coarse-resolution DEMs to modern data preservation strategies for high-precision DEMs that maintain original terrain fidelity. Quantitative analysis of the acceleration ratio indicator revealed that algorithmic engineering efficiency can differ by as much as 189% under the same time complexity, which highlights the disparity between theoretical complexity and actual performance. Current research faces three principal challenges in processing ultra-large-scale data: the influence of geographical characteristics on data structures, performance bottlenecks in traditional algorithms, and the efficiency limitations of serial computation. Future research should concentrate on three key areas: (1) developing novel data structures based on enhanced binary tree encoding to mitigate the effects of geographical characteristics; (2) integrating artificial intelligence technologies to address the limitations of traditional algorithms; and (3) constructing parallel computing frameworks. These advances are expected to significantly increase the accuracy of surface water flow simulations and facilitate the comprehensive application of DEMs in hydrology.