Estimating the timing of the peak snowmelt floods in unregulated boreal catchments using machine learning techniques.

The timing of peak snowmelt floods in boreal environments has undergone significant changes, characterized by nonlinear and complex patterns. This timing determines when coastal areas of boreal rivers experience the greatest inundation during the spring season. It is highly sensitive to climate change and directly influences local fauna and flora. Despite its critical role in flood risk management, the prediction of spring flood timing, along with the identification of its key drivers and most influential factors, remains insufficiently studied in boreal regions.

In this study, we investigate the potential for predicting the timing of annual maximum snowmelt floods by applying a thermal definition of the spring season, along with various climatological and hydrological indices. The analysis is based on a comprehensive daily dataset available with varying record lengths of at least 50 years, available since the early 1960s and extending to 2023 across multiple unregulated Finnish catchments. Among the most important dynamic features are daily discharge records, high-resolution gridded temperature data, and atmospheric teleconnection indices. Additionally, key static catchment characteristics, such as area, slope, and geographical position, are also incorporated into the modeling process, along with other relevant variables.

Machine learning algorithms, including Random Forest and SHAP (SHapley Additive exPlanations) values for feature importance, are applied to identify the most influential factors shaping the timing of annual maximum snowmelt floods and to assess the overall predictability of these events across multiple catchments. The study introduces a novel approach using a thermal definition of spring. The findings provide new indices and actionable thresholds that can help identify areas where adaptation measures should be prioritized.