EGU24-12675, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Skilful probabilistic forecasts of UK floods months ahead using a hybrid approach

Simon Moulds1,2, Louise Slater2, Louise Arnal3, and Andrew Wood4
Simon Moulds et al.
  • 1University of Edinburgh, School of GeoSciences, Edinburgh, UK (
  • 2School of Geography and the Environment, University of Oxford, Oxford, UK
  • 3Ouranos, Montreal, Canada
  • 4National Center for Atmospheric Research, Climate and Global Dynamics, Boulder, CO, USA

Streamflow forecasts months ahead are an important component of flood risk management. Hybrid methods that predict seasonal streamflow quantiles using ML/AI models driven by climate model outputs are currently underexplored, yet have some important advantages over traditional approaches based on hydrological models. For example, they are computational efficient, can incorporate a wide variety of input data, and may avoid the need for spatial downscaling and/or bias correction. Here we develop a hybrid subseasonal to seasonal streamflow forecasting system to predict the monthly maximum daily streamflow up to four months ahead. We train a machine learning model on dynamical precipitation and temperature forecasts from a large ensemble from the Copernicus Climate Change Service (C3S). We show that multi-site ML models trained on pooled catchment data together with static catchment attributes are significantly more skilful compared to single-site ML models that are trained on data from each catchment individually. Overall, we find 99.8% of stations show positive skill relative to climatology in the first month after initialization, 90.7% in the second month, 57.9% in the third month and 35% in the fourth month.

How to cite: Moulds, S., Slater, L., Arnal, L., and Wood, A.: Skilful probabilistic forecasts of UK floods months ahead using a hybrid approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12675,, 2024.