Collaborative Research: The Effects of Hydrodynamic and Granular Controls on Bed Load Flux Intermittency: Application to Steep Mountain Streams

了解河流中流体流动如何驱动沉积物运输是预测侵蚀和景观演变、管理河流基础设施以及保护和促进鱼类栖息地的基础。尽管进行了近一个世纪的定量研究，但河床负荷运输——泥沙与河底频繁接触的运动——仍然是出了名的难以预测。在陡峭的山间溪流中，这个问题尤其严重，因为在那里，输运发生在接近运动阈值的地方，而由此产生的大量级的床载脉冲无法用现有的数学模型来预测。我们将在这里研究山涧的两个独特方面：(1)产生大规模流体湍流结构的大型、很少移动的巨石的存在；(2)引起许多粒子协调运动的高能颗粒-颗粒碰撞。我们将进行两组平行的实验室实验来检验这两个因素对颗粒床结构形成和破碎的影响。将使用颗粒图像测速法绘制流量图，而声学传感器将量化床上载荷传输，压力传感器将测量流体应力，激光诱导荧光将用于对颗粒床进行成像。这些测量将使我们能够确定流体和颗粒应力对床质输运的相对贡献，并开发一个更适合于山间溪流预测的精细床质输运方程。我们将测试最近在软物质物理学中开发的一个框架，该框架将广泛的无序系统中的输运描述结合起来，是否有助于理解河流中沉积物输运的统计力学。洪水侵蚀河岸，冲垮桥梁，使水库积满泥沙。在陡峭的山间溪流中，巨大的巨石形成了天然水坝，在暴雨期间可能突然坍塌，向下游输送灾难性的沉积物脉冲，危及财产和生命。这些事件很少发生，因此很难获得理解物理过程和建立预测模型所需的测量数据。在这项研究中，我们将使用一组缩放的实验室实验，这使我们能够通过缩小尺寸来有效地加快时间，以了解驱动山涧中巨石运动的物理过程。我们将在精心控制的条件下测量移动的水对巨石施加的力，以及巨石之间相互碰撞的力。然后，我们将建立一个数学模型，利用这种物理理解来预测何时，以及在不同震级的洪水中，巨石会移动多少。这个模型将帮助地球科学家研究侵蚀地貌的长时间演化。研究结果对负责保护和维护山区基础设施以及恢复自然河流功能的工程师和管理人员也很有用。此外，这项资助将为未来的科学家提供重要的培训，因为实验将由一名博士生和博士后研究员领导，并涉及几名本科生。

Understanding how fluid flow drives sediment transport in a river is fundamental for predicting erosion and landscape evolution, managing river infrastructure, and protecting and promoting fish habitat. Bed load transport - the movement of sediment in frequent contact with the river bottom - remains notoriously unpredictable, despite almost a century of quantitative research. The problem is particularly acute in steep mountain streams where transport occurs close to the threshold of motion, and the resulting large-magnitude bed load pulses cannot be predicted using existing mathematical models. There are two unique aspects of mountain streams that we will examine here: (1) the presence of large, rarely-mobile boulders that generate large-scale fluid turbulence structures; and (2) highly energetic grain-grain collisions that cause coordinated motion of many particles. We will perform two parallel sets of laboratory experiments to examine the influence of these two factors on the formation and breakup of granular bed structures. Flow will be mapped using particle image velocimetry, while acoustic sensors will quantify bed load transport, pressure sensors will measure fluid stresses, and laser-induced fluorescence will be used to image the granular bed. These measurements will allow us to determine the relative contributions of fluid and granular stresses to bed load transport, and to develop a refined bed load transport equation that is better suited for prediction in mountain streams. We will test whether a recent framework developed in soft-matter physics, which unites the description of transport in a wide class of disordered systems, can be useful for understanding the statistical mechanics of sediment transport in rivers.Floods erode streambanks, take out bridges and fill reservoirs with sediment. In steep mountain streams, large boulders form natural dams that can suddenly give way during a rain storm, sending catastrophic pulses of sediment downstream that endanger property and lives. These events occur infrequently, making it difficult to obtain the measurements required to understand the physical processes and to build predictive models. In this study we will use a set of scaled laboratory experiments, which allow us to effectively speed up time by scaling down size, to understand the physical processes driving the movement of boulders in mountain streams. We will measure the forces exerted on boulders by the moving water, and among boulders as they collide with each other, under carefully controlled conditions. We will then build a mathematical model that uses this physical understanding to predict when, and how much, boulders will move in floods of different magnitudes. This model will help Earth scientists studying the long-timescale evolution of eroding landscapes. Results will also be useful for engineers and managers charged with protecting and maintaining infrastructure, and restoring natural river function, in mountain settings. In addition, this grant will provide important training for future scientists, as the experiments will be led by a doctoral student and post-doctoral researcher, and also involve several undergraduate students.