Equifinality in River Discharge Prediction Revealed Through Explainable AI

River discharge prediction is critical for water resource management, yet equifinality—where multiple model configurations achieve similar accuracy—complicates process understanding. We explored this phenomenon using Long Short-Term Memory (LSTM) models trained on UK river basins, incorporating geomorphic descriptors derived from Digital Terrain Models and other environmental features from the CAMELS-GB dataset, including land cover, soil, and climate variables. Explainable AI techniques revealed that the models rely on different, yet equally effective, combinations of correlated features to achieve comparable performance. This variability underscores the complexity of hydrological systems and highlights the importance of integrating explainability and domain knowledge in machine learning to enhance model interpretability and robustness.