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.

Hundreds of millions of people live close to, and depend upon, the world's large rivers for water, food, transport and the maintenance of a thriving ecosystem. However, these rivers are increasingly vulnerable to the effects of a wide range of natural and human-induced disturbances, including climate change, construction of large dams, river engineering works, deforestation, agricultural intensification, and mining activity. Over the past 20 years, climate change and deforestation have impacted on the hydrology and sediment fluxes within the Amazon River Basin. However, the Amazon has remained one of the few large river systems that has been largely unaffected by dams. This situation is changing rapidly, because widespread hydropower dam construction in Brazil, Bolivia, Peru and Ecuador now threatens the basin, with >300 dams planned or under construction. These dams are expected to trigger severe hydro-physical and ecological disturbances throughout the basin, including massive reductions in sediment and nutrient delivery to the lowland Amazon and its floodplains, substantial degradation of river beds and banks, significant changes in river water levels and flooding, and adverse impacts on river and floodplain ecosystems, on which the human population depends. Recent high profile studies highlight the need for international action to assess and mitigate these impacts, both in the Amazon and elsewhere. However, our capacity to do this is severely restricted by an absence of quantitative models that can predict how environmental disturbances propagate through large rivers and floodplains, over continental distances, and decadal to centennial time periods. Critically, environmental disturbances driven by dams, climate and land cover change promote dynamic river responses (e.g., changes in river width, depth, slope, sediment size, degree of branching and rate of floodplain reworking), which in turn control changes in flood conveyance and downstream sediment delivery. Despite advances in modelling of river dynamics over short distances (<100 km), hydrological models that are applied to continental-scale drainage basins treat rivers and floodplains as static conduits. Consequently, such models are unable to represent or predict the future impacts of environmental change on flooding, sediment fluxes or river and floodplain functioning. This project will deliver a step-change in our ability to model, predict and understand how the world's large rivers are impacted by, and respond to, environmental change. We will achieve this by implementing a research strategy that involves six elements: First, we will develop a new multi-scale numerical modelling approach that enables the effects of river dynamics on environmental disturbance propagation through continental-scale drainage basins to be simulated. Second, we will develop a suite of environmental scenarios representing climate and land cover changes and dam construction throughout the Amazon Basin for the recent past (1985-2015) and future (up to 2200). Third, we will collect new field datasets at sites on the Amazon River that are required to test key components of the model. Fourth, we will work with an international team of project partners to assemble high-resolution field, satellite and model datasets that quantify channel and floodplain processes, and river morphology and dynamics throughout the Amazon Basin. Fifth, we will use these data to carry out rigorous testing of our new model. Sixth, we will apply the model to predict the future evolution of the Amazon River and its tributaries for a wide range of environmental change scenarios, and quantify the controls on hydro-geomorphic disturbance propagation within large drainage basins. We will work with our project partners to disseminate our model code, datasets and project outcomes to non-academic stakeholders, both nationally and internationally.

Floods are the most dangerous and costly of all natural hazards. From 1980 to 2013, floods accounted for more than $1 trillion in losses and resulted in at least 220,000 fatalities globally. More than 50% of these deaths, and a large proportion of the losses, occurred in densely populated low-lying coastal regions, especially those at the coastal-river interface. Continuing to advance our understanding of flooding is therefore of utmost importance. In coastal regions, floods are often caused by multiple factors. Floods can arise through the joint occurrence of factors such as (1) storm surges plus astronomical tides (storm-tides) and/or (2) local or remotely (swell) generated waves; but also from heavy precipitation, either through (3) increased river discharge (fluvial) and/or (4) direct runoff (pluvial). Most flood risk assessments to date have considered these four main drivers of flooding separately. However, the adverse consequences of a flood in coastal regions can be greatly exacerbated when the oceanographic (storm-tides and waves), fluvial, and/or pluvial sources of flooding occur concurrently or in close succession, a condition known as 'compound flooding', which can result in disproportionately extreme events. Despite their high impact potential, compound events remain poorly understood, in large part because of the lack of information on the inter-dependence of the driving factors, which varies considerably from place to place, and the perceived difficulty of the joint probability analysis methods required to analyse these interdepencies. This is why the World Climate Research Program Grand Challenge on Extremes has identified climatic compound events as an international research priority. A recent example of a compound event is that associated with Hurricane Harvey in 2017. Record breaking rainfall, river discharge and runoff, combined with a moderate but long-lasting storm surge, resulted in disastrous flooding in Houston. It was the second costliest natural disaster in US history. Moreover, it is recognised that, by not considering compound flooding, the risk to Houston and elsewhere had been, and still is, greatly underestimated. In CHANCE we will deliver a new integrated approach, incorporating all the spatial and temporal dependencies between the four main source drivers of flooding in coastal regions. This will allow us to make a step change in our understanding and prediction of the source mechanisms driving compound flood events in coastal areas around the North Atlantic basin. We will address the following key questions: 1. Where do (and where don't) compound events occur and which combinations of source-variables are most important in different regions? 2. Which weather types favour the occurrence of compound events and will the frequency of compound events increase/decrease in the future as weather patterns change? 3. What is the likelihood and spatial extent of compound events in different regions? 4. How do compounding effects from multiple flood sources exacerbate impacts to coastal communities? We will do this through a series of methodological innovations (e.g., novel dependence analyses and state-of-the art weather typing approaches, along with inventive multivariate extreme value analysis techniques and advanced ensemble hydrodynamic modelling) that not only have relevance to the serious issue of compound flooding, but which will also be transferable to other cascading hazards in the earth and environmental sciences, such as: heat waves, drought and bush fires; extreme rainfall, landslides and cliff falls; earthquake and tsunami; and river discharge and turbidity currents. Our new methods will enable us to fully assess and predict all the source variables associated with compound flood events and their spatial extents in coastal regions (past, present and future) and will result in a major advance in the way compound flooding is understood, quantified and managed.