First global estimation of bankfull river discharge

The accurate estimation of bankfull discharge (Q_BF) 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 Q_BF shows that the random forest model has good performance (R²=0.79), and the estimated Q_BF 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