Proposal of a facet-to-facet multiple flow direction algorithm based on geometrical and mathematical analysis of physical dispersion over triangle facet

IF 3.1 2区 地球科学 Q2 GEOGRAPHY, PHYSICAL Geomorphology Pub Date : 2024-10-02 DOI:10.1016/j.geomorph.2024.109440
Zhenya Li , Xijun Lai , Pengfei Shi , Tao Yang
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Abstract

Flow direction algorithms have been widely used to extract crucial terrain attributes of great hydrological and geomorphological significance. However, essential distinctions between the empirically-designed strategies of typical algorithms and the natural rules of physical dispersions bring various problems (e.g. parallel channel, artificial dispersion), leading to the low size and extent precisions of estimated results. In this work, geometrical and mathematical analysis is conducted on the inherent characteristics of physical dispersions along slope lines on local terrains. On each 3 × 3 window of digital elevation model (DEM), center pixel is divided into eight non-overlapping sub-facets. Necessary and sufficient (NS) conditions of size relationships between the elevations of adjacent pixels are summarized to directly identify the receiving facets of a sub-facet. Then, strict mathematical relations are derived between slope direction of a sub-facet and flow proportions allocated to receiving facets. A strategy is designed to re-adjust receiving facets and flow proportions for the boundary flow of adjacent facets. Lastly, a multiple-flow-direction algorithm called TFGA is proposed with the NS condition of size relationships, mathematical relation of slope direction with flow proportion, and adjustment strategy of boundary flow. Case studies are conducted for investigating the total contributing areas (TCA) and specific contributing areas (SCA) estimated by TFGA. Results reveal all-side superiorities of TFGA to typical algorithms in spatial patterns, error indicators and statistic characteristics of estimated TCAs and SCAs. Particularly, TFGA improves size precision of estimated results by approximately one order of magnitude. In a conclusion, we highly recommend TFGA for digital elevation analysis.
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基于三角形面上物理分散的几何和数学分析,提出面与面之间多流向算法的建议
流向算法已被广泛用于提取具有重要水文和地貌意义的关键地形属性。然而,典型算法的经验设计策略与物理散布的自然规律之间的本质区别带来了各种问题(如平行河道、人工散布),导致估计结果的尺寸和范围精度较低。在这项工作中,对当地地形上沿斜坡线物理分散的固有特征进行了几何和数学分析。在数字高程模型(DEM)的每个 3 × 3 窗口中,中心像素被划分为八个不重叠的子面。通过总结相邻像素高程大小关系的必要条件和充分条件(NS),可直接确定子面的接收面。然后,推导出子面的坡度方向与分配给接收面的流量比例之间的严格数学关系。针对相邻面的边界流,设计了一种重新调整接收面和流量比例的策略。最后,结合尺寸关系的 NS 条件、坡向与流量比例的数学关系以及边界流的调整策略,提出了一种名为 TFGA 的多流向算法。对 TFGA 估算的总贡献面积(TCA)和具体贡献面积(SCA)进行了案例研究。结果表明,TFGA 在估算的 TCA 和 SCA 的空间模式、误差指标和统计特征方面均优于典型算法。特别是,TFGA 将估计结果的尺寸精度提高了约一个数量级。总之,我们强烈推荐将 TFGA 用于数字高程分析。
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来源期刊
Geomorphology
Geomorphology 地学-地球科学综合
CiteScore
8.00
自引率
10.30%
发文量
309
审稿时长
3.4 months
期刊介绍: Our journal''s scope includes geomorphic themes of: tectonics and regional structure; glacial processes and landforms; fluvial sequences, Quaternary environmental change and dating; fluvial processes and landforms; mass movement, slopes and periglacial processes; hillslopes and soil erosion; weathering, karst and soils; aeolian processes and landforms, coastal dunes and arid environments; coastal and marine processes, estuaries and lakes; modelling, theoretical and quantitative geomorphology; DEM, GIS and remote sensing methods and applications; hazards, applied and planetary geomorphology; and volcanics.
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