Are Deep Learning Models in Hydrology Entity Aware?

Abstract

Hydrology is experiencing a shift from process-based toward deep learning (DL) models. Entity-aware (EA) DL models with static features (predominantly physiographic proxies) merged to dynamic forcing features show significant performance improvements. However, recent studies challenge the notion that combining dynamic forcings with static attributes make such models entity aware, suggesting that static features are not effectively leveraged for generalization. We examine entity awareness using state-of-the-art Long-Short Term Memory (LSTM) networks and the CAMELS-US data set. We compare EA models provided with physiographic static features to ablated variants not provided with static inputs. Findings suggest that the superior performance of EA models is primarily driven by information provided by meteorological data, with limited contributions from physiographic static features, particularly when tested out-of-sample. These results challenge previously held assumptions regarding how physiographic proxies contribute to generalization ability in EA Models, highlighting the need for new approaches for robust generalization in DL models.