Modeling Landforms as Self‐Organized, Hierarchical Dynamical Systems
Modeling Landforms as Self‐Organized, Hierarchical Dynamical Systems
复制标题
将地形建模为自组织的分层动力系统
DOI：
10.1029/135gm10
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
B. Werner
中科院分区：
文献类型：
--
作者：
B. Werner
地貌是自组织的结果，通过这种自组织，系统中快尺度和小尺度成分之间的局部非线性、耗散性相互作用产生了一种更大尺度、更慢演化的形式。景观是有序的时间层次结构中的一系列水平的特点是离散的，分离的时间尺度连接的自组织，但不动态地相互作用。这两个假设之间的关系和他们的影响，在这里探索使用自组织系统的三个属性：出现的顺序，其中变量的数量减少;时间尺度分离，其中的时间尺度特征的反应扰动增加;和动态不对称性，其中的自组织形式的动态变得抽象和奴隶的组成动态。自组织的这三个属性形成了一种新的建模方法--分层建模的基础。模型是在一个层次结构中的层次上构建的，对应于景观的涌现的、自组织的形式、模式或行为。分层建模理论上比较两个更传统的，终端成员的方法在地貌学：还原论，它使用详细的动态在基本尺度，和普遍性，它对待缓慢变化的动态或稳定状态之间的不同系统。分层建模将物理洞察力的获取纳入到一系列时间尺度的模型构建中，而洞察力只需要在还原论的基本成分尺度和普遍性的最长尺度上。分层模型可能提供更好的可预测性，因为最终模型既不包含太多的变量和过程（如还原论），这可能导致数值和概念错误，也不包含太少的变量和过程（如普遍性），这只允许部分表示景观的动态。这些一般性的论点说明了考虑底形，山坡，河流，图案地面和近岸。
Landforms result from self-organization, by which local nonlinear, dissipative interactions between the fast- and small-scale constituents of a system give rise to emergence of a larger-scale, slower evolving form. Landscapes are ordered in a temporal hierarchy in which a range of levels characterized by discrete, separated time scales are connected by self-organization but do not dynamically interact. The relationship between these two hypotheses and their implications for modeling are explored here using three properties of self-organized systems: emergence of order, in which the number of variables decreases; time-scale separation, in which the time scale characterizing the reaction to perturbations increases; and dynamical asymmetry, in which dynamics of the self-organized form becomes abstracted and slaves the constituent dynamics. These three properties of self-organization form the basis of a new modeling methodology, hierarchical modeling. Models are constructed at levels in a hierarchy corresponding to emergent, self-organized forms, patterns or behaviors of the landscape. Hierarchical modeling is compared theoretically to two more traditional, end-member methodologies employed in geomorphology: reductionism, which uses detailed dynamics at the fundamental scale, and universality, which treats the slowly varying dynamics or steady state common amongst diverse systems. Hierarchical modeling incorporates the acquisition of physical insight into model construction across a range of temporal scales, whereas insight is required only at the scale of fundamental constituents for reductionism and at the longest scales for universality. Hierarchical modeling potentially provides improved predictability because the resulting models contain neither too many variables and processes (as in reductionism), which can lead to numerical and conceptual errors, nor too few variables and processes (as in universality), which permits only a partial representation of the dynamics of a landscape. These general arguments are illustrated by consideration of bedforms, hillslopes, rivers, patterned ground and the nearshore.