Can Weather Patterns Contribute to Predicting Winter Flood Magnitudes Using Machine Learning?

Abstract. Fluvial floods pose severe socioeconomic and environmental risks globally, and are projected to change in frequency and severity in future decades. While it is crucial to understand these changes, the prediction of extreme events remains a significant challenge. Identifying predictable features driving extreme flood events provides a potential way forward with respect to improving such predictions. Weather patterns tend to be more stable and predictable than meteorological catchment-scale variables such as precipitation. However, the contribution of weather patterns to extreme flood prediction remains poorly understood. This study investigates the role of weather patterns, along with other sets of predictors, in influencing winter flood magnitudes above the 99th percentile within a large-sample machine learning framework, using natural benchmark catchments from the UK National River Flow Archive. Six generations of random forest models, each generation including additional sets of features, are explored on the national, regional, and catchment scale. Model results are interpreted using Shapley Additive Explanations (SHAP) to understand feature importance. Additionally, we analyze the conditional probabilities of the UK Met Office's MO-30 weather patterns during extreme flood events. Our findings show that weather patterns with cyclonic low pressure systems frequently co-occur with high flow magnitudes, which is also reflected in the SHAP value analysis. However, the predictive power of these weather patterns is limited and offers hardly any benefit. We also show regional nuances in the feature importance of predictors and model performance. The majority of the predictability comes from meteorological variables and antecedent precipitation. Our findings highlight the variability in model outcomes depending on the model structure and choice of predictors. This study also offers methodological guidance for developing large-sample machine learning models for flood estimation that integrate atmospheric predictors with traditional hydro-meteorological and geographical variables.