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A GIS numerical framework to study the process basis of scaling statistics in river networks

机译：研究河网规模统计过程基础的GIS数值框架

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摘要

A new geographic information system (GIS) numerical framework (NF), called CUENCAS, for flows in river networks is presented. The networks are extracted from digital elevation models (DEMs). The program automatically partitions a basin into hillslopes and channel links that are required to correspond to these features in an actual terrain. To investigate the appropriate DEM resolution for this correspondence, we take a high-resolution DEM at 10-m pixel size, and create DEMs at eight different resolutions in increments of 10 m by averaging. The extracted networks from 10-30 m remain about the same, even though there is a tenfold reduction in the number of pixels. By contrast, the extracted networks show increasing distortions of the original network from 40-90 m DEMs. We show the presence of statistical self-similarity (scaling) in the probability distributions of drainage areas in a Horton-Strahler framework using CUENCAS. The NF for flows takes advantage of the hillslope-link decomposition of an actual terrain and specifies mass and momentum balance equations and physical parameterizations at this scale. These equations are numerically solved. An application of NF is given to test different physical assumptions that produce statistical self-similarity in spatial peak flow statistics in a Horton-Strahler framework.
机译：提出了一种新的地理信息系统（GIS）数值框架（NF），称为CUENCAS，用于河流网络中的流量。网络是从数字高程模型（DEM）中提取的。该程序会自动将盆地分为山坡和与实际地形中的这些要素相对应的通道链接。为了调查适合此对应关系的DEM分辨率，我们采用10 m像素大小的高分辨率DEM，并通过平均以10 m为增量创建八种不同分辨率的DEM。即使像素数量减少了十倍，从10-30 m提取的网络仍保持不变。相比之下，提取的网络显示原始网络从40-90 m DEM的失真增加。我们在使用CUENCAS的Horton-Strahler框架中的流域概率分布中显示了统计自相似性（缩放）。流动的NF利用了实际地形的坡度链接分解，并在此比例下指定了质量和动量平衡方程式以及物理参数化。这些方程是数值求解的。给出了NF的应用，以测试在Horton-Strahler框架中在空间峰值流量统计中产生统计自相似性的不同物理假设。

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