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

能够预测河道中水的深度和速度，对于管理河道内工程、预测泥沙输送和洪水风险、规划河流恢复、制定保护河流栖息地的最小流量以及预测二氧化碳排放非常重要。为了做出这些预测，我们需要了解河道河床和河岸的粗糙度如何减缓河道内的水流，即河道的水流阻力。我们通常认为泥沙颗粒是阻碍河道水流的最重要物体，我们用较大颗粒相对于水流深度的大小来表示它们的效果。这一假设很好地预测了较大河流中的水流阻力，这些河流中的颗粒与水流深度相比较小。但在占所有河流网络77%的源头溪流中，水流通常不会比最大的床面颗粒深多少。即使是现有最好的根据深度预测河流速度和流量的方法，或者根据流量预测深度和速度的方法，在这些情况下也是非常不可靠的，预测通常误差两倍。相比之下，对气候变化情景下水流深度将如何变化的预测大约有一半的不确定性。与最大的床面颗粒相比，很难预测水流较浅的河流的水流阻力，因为它们的河道包含许多不同形状和大小的不同障碍物，包括泥沙、巨石、成片裸露的基岩和不规则的堤岸。较大颗粒的大小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很差也就不足为奇了。然而，社区继续使用颗粒大小，因为仍然没有好的替代方案。尽管有很长的研究历史，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减缓流动的过程。特别是，我们不知道什么大小的障碍物影响最大(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大小的多个障碍物的综合影响是什么。由于难以同时测量河道地形和水流特性，这一进展受到了严重限制。但是，在现场测量、水槽技术和数值模拟方面的最新进展意味着，我们第一次能够获得在预测流动阻力和随之而来的所有变化方面至关重要的数据集。在这个项目中，我们将使用最先进的现场高分辨率测量河道地形的技术(陆地激光扫描、浅水多波束声纳)来产生第一个全面的粗糙河床地形数据集，并将使用统计方法来描述其河床和堤岸的粗糙度。我们将从这个数据集中选择具有代表性的通道，并在实验室水槽中通过缩小比例的3D球磨来复制它们。在水槽中，我们将依次添加巨石、泥沙和粗糙的堤岸，并测量每个组分对水流深度和水流阻力的影响。我们还将使用新的数值模拟方法来模拟我们所操纵的渠道中的流动特性，以便它们只包含某些地形尺度，从而使我们能够确定最重要的障碍尺寸。结合水槽和数值模拟实验，我们将能够确定渠道中不同大小和类型的障碍物如何组合在一起来设置总流阻的物理基础。根据这一理解，我们将提出新的方法，如何最好地预测水流速度、深度或流量。总体而言，该项目将从根本上改变对河流流量的理解和预测。

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.