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

了解水流如何驱动河流中的泥沙输送，对于预测侵蚀和地貌演变、管理河流基础设施以及保护和促进鱼类栖息地是基本的。泥沙运输？泥沙运动与河底频繁接触？尽管进行了近一个世纪的定量研究，但仍然是出了名的不可预测。这个问题在陡峭的山间溪流中尤为严重，那里的泥沙输送发生在接近运动阈值的地方，由此产生的大规模推移质脉动无法使用现有的数学模型进行预测。我们将在这里研究山脉溪流的两个独特方面：(1)大的、很少移动的巨石的存在，这些巨石产生大规模的流体湍流结构；(2)高能量的颗粒-颗粒碰撞，导致许多颗粒的协调运动。我们将进行两组平行的实验室实验，以考察这两个因素对颗粒床结构的形成和破碎的影响。流动将使用粒子图像测速仪绘制，而声波传感器将量化推移质，压力传感器将测量流体应力，激光诱导荧光将用于成像颗粒床。这些测量将使我们能够确定流体和颗粒应力对推移质输运的相对贡献，并开发出更适合于山区溪流预报的改进的推移质输运方程。我们将测试最近在软物质物理学中开发的一个框架，该框架统一了对一大类无序系统中传输的描述，是否有助于理解河流中泥沙传输的统计机制。洪水侵蚀河岸，拆除桥梁，用泥沙填满水库。在陡峭的山间溪流中，巨大的巨石形成天然大坝，在暴雨期间突然坍塌，向下游输送灾难性的泥沙脉冲，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性模型所需的测量结果。在这项研究中，我们将使用一组规模化的实验室实验，这些实验使我们能够通过缩小规模来有效地加快时间，了解推动山间溪流中巨石运动的物理过程。我们将在严格控制的条件下，测量水流对巨石施加的力，以及在巨石相互碰撞时施加在巨石之间的力。然后，我们将建立一个数学模型，利用这种物理理解来预测巨石在不同强度的洪水中何时移动以及移动多少。这一模型将帮助地球科学家研究侵蚀地貌的长期演化。研究结果也将对负责保护和维护基础设施以及恢复山区自然河流功能的工程师和管理人员有用。此外，这笔资金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，还将涉及几名本科生。

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.