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

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.