River channel conveyance capacity adjusts to modes of climate variability

River networks are typically treated as conduits of fixed discharge conveyance capacity in flood models and engineering design, despite knowledge that alluvial channel networks adjust their geometry, conveyance, planform, extent and drainage density over time in response to shifts in the magnitude and frequency of streamflows and sediment supply. Consistent relationships between modes of climate variability conducive to wetter-/drier-than-average conditions and changes in channel conveyance have never been established, hindering geomorphological prediction over interannual to multidecadal timescales. This paper explores the relationship between river channel conveyance/geometry and three modes of climate variability (the El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Arctic Oscillation) using two-, five- and ten-year medians of channel measurements, streamflow, precipitation and climate indices over seven decades in 67 United States rivers. We find that in two thirds of these rivers, channel capacity undergoes coherent phases of expansion/contraction in response to shifts in catchment precipitation and streamflow, driven by climate modes with different periodicities. Understanding the sensitivity of channel conveyance to climate modes would enable better river management, engineering design, and flood predictability over interannual to multidecadal timescales

Emergency Water Information Network (EWIN)

Flooding is a global problem and as a representative example, Mexico is currently struggling to manage flood situations which are increasing in regularity and severity. Many developing countries have substandard flood monitoring infrastructure. However, in common with the UK, they have state-of-the-art cellular mobile phone systems. In this research, expertise in water engineering and radio communications from the UK and Mexico have been combined to design a cost effective flood forecasting system based on hydrology sensing and mobile networks. Recent events such as hurricane Patricia in Mexico (October 2015) has emphasised the need for systems that can predict the dynamic behaviour of large-scale water flows. Currently, management of flood situations in many developing countries is carried out through prediction of water behaviour (Hydro Meteorological Warning System). This system is based on estimates of rainfall, runoff and water levels. In Mexico two central registers and rain measuring stations are used to gather data. The data collected is compared with pre-established risk thresholds which determine whether a warning should be issued. In general, the rainy season in Mexico occurs during the summer and fall, starting in May and ending in October. Along the main waterways, the change in state is dynamic between dry and rainy both in terms of the water volume in the channels and the vegetation on the banks. Vegetation in Mexico is normally sparse but grows quickly and in abundance during the rainy season. During flood events, new rivers form along river beds that are normally empty. These conditions are typical of flooding in many countries. In order to develop a real time flood forecasting system, several areas of research need to be investigated. These include: data sensing at the appropriate location and time, wireless transmission of flood data, sensor data fusion, model generation and prediction at the remote weather station. This multidisciplinary research project is addressing each of these areas by employing UK expertise in Water Engineering and Radio Communications to complement the research base in Mexico