How confident and reliable are deep learning models for streamflow prediction under flood conditions

Several deep learning (DL) based river streamflow forecasting studies have been conducted in recent years, with many producing reasonably accurate predictions. Providing uncertainty quantification (UQ) for any forecasting is important, especially in high-risk scenarios like flood forecasting. The novelty of this work lies in its focus on the early phases of flood forecasting and the assessment of uncertainties in both the timing and accuracy of predictions. This study evaluates two uncertainty quantification (UQ) techniques-Monte Carlo dropout and Ensemble methods-with deep learning (DL) models for short-term flood forecasting in the Prüm and Kyll River basins, western Germany. Three DL architectures (LSTM, GRU, and 1D-CNN) are assessed. While both the models and UQ methods perform reasonably well under normal conditions (e.g., average $$NSE> 0.85$$ N S E > 0.85 , MAE $$