EGU24-19997, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

First global estimation of bankfull river discharge

Yinxue Liu1, Michel Wortmann2, Louise Slater1, Laurence Hawker3, Jeff Neal3, Jiabo Yin4, Richard Boothroyd5, Solomon Gebrechorkos1,6, Julian Leyland6, Stephen Darby6, Daniel Parsons7, Helen Griffith8, Hannah Cloke8, Ellie Vahidi9, Andrew Nicholas9, Pauline Delorme10, and Stuart McLelland10
Yinxue Liu et al.
  • 1School of Geography and the Environment, University of Oxford, Oxford, UK
  • 2European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, Reading, UK
  • 3School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, UK
  • 4State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
  • 5School of Environmental Sciences, University of Liverpool, UK
  • 6School of Geography and Environmental Science, University of Southampton, Southampton, UK
  • 7Geography and Environment, Loughborough University, Loughborough, UK
  • 8Department of Geography & Environmental Science, University of Reading, Reading, UK
  • 9University of Exeter, Faculty of Environment, Science and Economy, Geography, UK
  • 10Energy and Environment Institute, University of Hull, Hull HU6 7RX, UK

The accurate estimation of bankfull discharge (QBF) plays a central role in multiple disciplines including geomorphology, hydrology, and ecology. For example, bankfull discharge is an essential input in many large-scale flood models which are widely used in understanding flood risk across large scales. However, in the context of extremely limited bankfull discharge observations, these Global Flood Models (GFMs) typically assume that bankfull discharge has a spatially uniform recurrence interval, with a value of 1-2 years widely adopted. In reality, many studies have found that the recurrence of bankfull discharge is highly variable. Therefore, more reliable estimates of bankfull discharge that account for river variability across different regions and climate zones are vital. Here, we train a random forest model to estimate bankfull discharge from global datasets encompassing river catchment characteristics, river geometry, topography, reservoir capacity, hydrological and climate indicators, alongside a newly compiled bankfull discharge database with over two thousand observations. The trained machine learning model is then used to develop the first estimate of bankfull discharge for 22 million km of rivers globally, using a newly developed, high-resolution, multi-threaded river network, Global River Topology (GRIT, Wortmann et al., 2023). Independent testing against observed values of QBF shows that the random forest model has good performance (R2=0.79), and the estimated QBF has better accuracy compared to the use of uniform recurrence-interval flows. This is the first study to estimate bankfull discharge for rivers at the global scale. Our dataset aims to improve bankfull representation in large-scale flood modelling, and to support river and water resources research more generally.

Wortmann, M., Slater, L., Hawker, L., Liu, Y., & Neal, J. (2023). Global River Topology (GRIT) (0.4) [Data set]. Zenodo. 10.5281/zenodo.7629907

How to cite: Liu, Y., Wortmann, M., Slater, L., Hawker, L., Neal, J., Yin, J., Boothroyd, R., Gebrechorkos, S., Leyland, J., Darby, S., Parsons, D., Griffith, H., Cloke, H., Vahidi, E., Nicholas, A., Delorme, P., and McLelland, S.: First global estimation of bankfull river discharge, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19997,, 2024.