Investigating Factors Influencing Upper Bound Performance of ML-Based Streamflow Simulations.

Machine learning (ML) techniques are transforming hydrological modeling, yet their ability to predict extreme streamflow events remains uncertain. Among these techniques, Long Short-Term Memory (LSTM) networks have emerged as a powerful tool for streamflow prediction, capable of capturing complex temporal dynamics and long-term dependencies inherent in hydrological data. In this study, we aim to identify the factors that influence the upper limit of discharge values simulated by LSTM models—a critical aspect for improving extreme event prediction. This limit is shaped by multiple considerations, including the diversity and quality of training data, model architecture, and optimization objectives. Data preprocessing and calibration strategies further impact performance, while challenges such as input biases and insufficient emphasis on rare events can constrain the model’s ability to capture extremes. Ultimately, predictions remain bounded by physical laws and theoretical principles, ensuring outputs are credible and consistent with real-world hydrological behavior. Understanding these factors provides valuable insights for enhancing model robustness, improving flood risk assessment, and guiding the development of scalable approaches for simulating extreme hydrological events under changing climate conditions.