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具有复杂形态的冠层上流动阻力的通用标度律。

Universal scaling-law for flow resistance over canopies with complex morphology.

作者信息

Rubol Simonetta, Ling Bowen, Battiato Ilenia

机构信息

Dept. of Energy Resources Engineering, Stanford University, Stanford, CA, 94305, USA.

出版信息

Sci Rep. 2018 Mar 13;8(1):4430. doi: 10.1038/s41598-018-22346-1.

Abstract

Flow resistance caused by vegetation is a key parameter to properly assess flood management and river restoration. However, quantifying the friction factor or any of its alternative metrics, e.g. the drag coefficient, in canopies with complex geometry has proven elusive. We explore the effect of canopy morphology on vegetated channels flow structure and resistance by treating the canopy as a porous medium characterized by an effective permeability, a property that describes the ease with which water can flow through the canopy layer. We employ a two-domain model for flow over and within the canopy, which couples the log-law in the free layer to the Darcy-Brinkman equation in the vegetated layer. We validate the model analytical solutions for the average velocity profile within and above the canopy, the volumetric discharge and the friction factor against data collected across a wide range of canopy morphologies encountered in riverine systems. Results indicate agreement between model predictions and data for both simple and complex plant morphologies. For low submergence canopies, we find a universal scaling law that relates friction factor with canopy permeability and a rescaled bulk Reynolds number. This provides a valuable tool to assess habitats sustainability associated with hydro-dynamical conditions.

摘要

植被引起的水流阻力是正确评估洪水管理和河流修复的关键参数。然而,事实证明,在几何形状复杂的冠层中量化摩擦系数或其任何替代指标(例如阻力系数)是很困难的。我们通过将冠层视为具有有效渗透率的多孔介质来探讨冠层形态对植被渠道水流结构和阻力的影响,有效渗透率是描述水通过冠层层流动难易程度的一种属性。我们采用双域模型来模拟冠层上方和内部的水流,该模型将自由层中的对数定律与植被层中的达西 - 布林克曼方程耦合起来。我们针对在河流系统中遇到的广泛冠层形态收集的数据,验证了模型关于冠层内部和上方平均速度剖面、体积流量和摩擦系数的解析解。结果表明,对于简单和复杂植物形态,模型预测与数据之间都具有一致性。对于低淹没冠层,我们发现了一个通用的标度律,该标度律将摩擦系数与冠层渗透率和重新标度的整体雷诺数联系起来。这为评估与水动力条件相关的栖息地可持续性提供了一个有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c24/5849746/aee34ea51054/41598_2018_22346_Fig1_HTML.jpg

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