What controls the flow resistance of rough-bed rivers? Major new advances from high-resolution topography

能够预测河道中的水深和流速对于管理河道工程、预测泥沙输运和洪水风险、规划河流恢复、规定最小流量以保护河流栖息地以及预测二氧化碳排放量非常重要。为了进行这些预测，我们需要了解河道河床和河岸的粗糙度如何减缓水流，即河道的流动阻力。我们通常认为泥沙颗粒是阻碍河道水流的最重要的物体，我们使用相对于水流深度的较大颗粒的尺寸来表示它们的影响。这种假设合理地预测了较大河流中的流动阻力，其中颗粒与流动深度相比较小。但在占所有河流网络77%的源头河流中，水流通常不会比最大的河床颗粒深得多。即使是现有的最好的方法来预测河流的速度和流量从深度，或深度和速度的流量，在这些条件下是非常不可靠的，预测通常是错误的两倍。相比之下，预测在气候变化情景下水流深度的变化具有一半的不确定性。对于水流较浅的河流，很难预测其水流阻力，因为它们的河道包含许多不同形状和大小的障碍物，包括沉积物、巨石、裸露基岩和不规则的河岸。较大颗粒的尺寸本身并不能可靠地代表阻碍流动的许多不同物体的综合效应，因此它对流动阻力的预测不佳也就不足为奇了。然而，社区继续使用粒度，因为仍然没有好的替代品。尽管研究的历史很长，但还没有开发出更好的替代方案，因为我们仍然不了解不同大小的障碍物减缓水流的过程。特别是，我们不知道什么尺寸的障碍物具有最大的影响（例如，一个大的巨石与几个较小的巨石相比），也不知道不同类型和尺寸的多个障碍物的组合影响是什么。测量河道地形和水流特性的困难严重限制了进展。但是，最近在现场测量、水槽技术和数值模拟方面的进展意味着我们第一次可以获得数据集，这些数据集对于预测水流阻力以及随之而来的所有变化至关重要。在本项目中，我们将使用最先进的技术在现场以高分辨率测量河道地形（陆地激光扫描、浅水多波束声纳）制作第一个粗糙河床地形的综合数据集，并将使用统计方法描述河床和河岸的粗糙程度。我们将从这个数据集中选择有代表性的渠道，并在实验室水槽中复制它们，以缩小的规模进行三维铣削。在水槽中，我们将依次添加巨石，沉积物和粗糙的银行，并测量每个组件如何影响流深和流阻。我们还将使用新的数值模拟方法来模拟我们所操纵的通道中的流动特性，以便它们只包含某些地形尺度，从而使我们能够识别最重要的障碍物尺寸。结合水槽和数值模拟实验将使我们能够确定如何在一个渠道联合收割机的不同大小和类型的障碍物相结合，以设置总的流动阻力的物理基础。从这一理解中，我们将产生新的方法来最好地预测流速，深度或体积。总的来说，该项目将为理解和预测河流流量提供一个根本性的变化。

Being able to predict the depth and speed of water in a river channel is important for managing in-channel engineering, predicting sediment transport and flood risk, planning river restoration, prescribing minimum flows to preserve river habitats, and predicting carbon dioxide efflux. To make these predictions, we need to understand how the roughness of a river channel's bed and banks slows down the flow within it, i.e. the flow resistance of the channel. We commonly assume that sediment particles are the most important objects obstructing flow in river channels, and we represent their effect using the size of the larger particles relative to the flow depth . This assumption predicts flow resistance reasonably well in larger rivers where particles are small compared to the flow depth. But in headwater streams, which make up 77% of all river networks, flow is usually not much deeper than the largest bed particles. Even the best existing methods for predicting river speed and flow volume from depth, or depth and speed from flow volume, are very unreliable in these conditions, with predictions commonly being wrong by a factor of two. For comparison, predictions of how flow depths will change under climate change scenarios have about half this degree of uncertainty.It is difficult to predict the flow resistance of rivers where flow is shallow compared to the largest bed particles because their channels contain lots of different obstacles of different shapes and sizes, including sediment, boulders, patches of exposed bedrock, and irregular banks. The size of the larger particles alone does not reliably represent the combined effect of the many different objects obstructing the flow, and so it is not surprising that it produces poor predictions of flow resistance. However, the community continues to use particle size because there are still no good alternatives. Despite a long history of research, better alternatives have not been developed because we still do not understand the processes by which different sizes of obstacles slow down the flow. In particular, we do not know what sizes of obstacles have most impact (e.g. one large boulder compared to several smaller ones), nor what the combined impact is of multiple obstacles of different types and sizes. Progress has been severely limited by the difficulty of measuring both channel topography and flow properties. But, recent advances in field measurements, flume techniques and numerical modelling mean that for the first time we can acquire the datasets that are essential to make a step change in predicting flow resistance and all that follows from it. In this project we will use state of the art technologies for measuring river channel topography at high resolution in the field (terrestrial laser scanning, shallow-water multi-beam sonar) to produce the first comprehensive dataset of rough-bed river topographies, and will use statistical methods to describe the roughness of their beds and banks. We will select representative channels from this dataset, and replicate them in a laboratory flume by 3-D milling them at a reduced scale. In the flume we will sequentially add boulders, sediment and rough banks, and measure how each component affects flow depth and flow resistance. We will also use new numerical modelling methods to simulate flow properties in channels that we have manipulated so that they only contain certain topographic scales, thus allowing us to identify the most important sizes of obstacle. The combined flume and numerical modelling experiments will allow us to determine the physical basis for how different sizes and types of obstacles in a channel combine to set the total flow resistance. From this understanding we will produce new approaches for how best to predict flow speed, depth or volume. Overall, this project will provide a fundamental step change in understanding and prediction of flow in rivers.