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流域中排水区域的幂律尾概率。

Power-law tail probabilities of drainage areas in river basins.

作者信息

Veitzer Seth A, Troutman Brent M, Gupta Vijay K

机构信息

National Research Council, U.S. Geological Survey, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Jul;68(1 Pt 2):016123. doi: 10.1103/PhysRevE.68.016123. Epub 2003 Jul 25.

Abstract

We examine the appearance of power-law behavior in rooted tree graphs in the context of river networks. It has long been observed that the tails of statistical distributions of upstream areas in river networks, measured above every link, obey a power-law relationship over a range of scales. We examine this behavior by considering a subset of all links, defined as those links which drain complete Strahler basins, where the Strahler order defines a discrete measure of scale, for self-similar networks with both deterministic and random topologies. We find an excellent power-law structure in the tail probabilities for complete Strahler basin areas, over many ranges of scale. We show analytically that the tail probabilities converge to a power law under the assumptions of (1) simple scaling of the distributions of complete Strahler basin areas and (2) application of Horton's law of stream numbers. The convergence to a power law does not occur for all underlying distributions, but for a large class of statistical distributions which have specific limiting properties. For example, underlying distributions which are exponential and gamma distributed, while not power-law scaling, produce power laws in the tail probabilities when rescaled and sampled according to Horton's law of stream numbers. The power-law exponent is given by the expression phi=ln(R(b))/ln(R(A)), where R(b) is the bifurcation ratio and R(A) is the Horton area ratio. It is commonly observed that R(b) approximately equal R(A) in many river basins, implying that the tail probability exponent for complete Strahler basins is close to 1.0.

摘要

我们在河网背景下研究有根树形图中幂律行为的表现。长期以来人们观察到,在河网中,在每个河段上方测量的上游区域统计分布的尾部,在一系列尺度范围内遵循幂律关系。我们通过考虑所有河段的一个子集来研究这种行为,该子集被定义为那些排水完整斯特拉勒流域的河段,其中斯特拉勒序定义了尺度的离散度量,适用于具有确定性和随机拓扑的自相似网络。我们发现在许多尺度范围内,完整斯特拉勒流域面积的尾部概率具有出色的幂律结构。我们通过分析表明,在以下假设下尾部概率收敛到幂律:(1)完整斯特拉勒流域面积分布的简单缩放,以及(2)应用霍顿河流数量定律。并非所有基础分布都会收敛到幂律,但对于一大类具有特定极限性质的统计分布会发生这种情况。例如,指数分布和伽马分布的基础分布,虽然不是幂律缩放,但根据霍顿河流数量定律重新缩放和采样时,会在尾部概率中产生幂律。幂律指数由表达式phi = ln(R(b)) / ln(R(A))给出,其中R(b)是分叉比,R(A)是霍顿面积比。人们普遍观察到,在许多流域中R(b)近似等于R(A),这意味着完整斯特拉勒流域的尾部概率指数接近1.0。

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