Leveraging Machine Learning to Uncover and Interpret Relevant Catchment Features

Recent advances in catchment hydrology [Kratzert et al., 2019–2021] demonstrate the superiority of LSTMs over traditional conceptual models for streamflow prediction in large-sample datasets. LSTMs achieve better streamflow accuracies by leveraging information from diverse hydrological behaviors. These models are enriched with static catchment attributes, which, when combined with meteorological drivers, play a critical role in streamflow formation. Augmenting LSTMs with these attributes further enhances their performance compared to vanilla LSTMs, underscoring the importance of these attributes for accurate streamflow predictions. Building on this, a recent study [Bassi et al., 2024] employed a conditional autoencoder to reveal that most of the relevant catchment information for streamflow prediction can be distilled into two features, with a third feature being beneficial for particularly challenging catchments. In this work, we directly derive a minimal set of catchment features from known attributes by passing them through the encoder and subsequently comparing streamflow predictions against state-of-the-art benchmarks [Kratzert et al., 2021]. Our findings indicate that while the intrinsic dimension of 26 commonly used attributes is four, only two features suffice for accurate streamflow prediction. This aligns closely with the findings of Bassi et al. (2024), suggesting that nearly all relevant information for streamflow prediction is encapsulated in known attributes. Finally, we provide an interpretation of these two machine-learning-derived features using information theory techniques.