Decoding LSTM to Reveal Baseflow Contributions in Fractured and Sedimentary Mountain Basins: A Case Study in the Sangre de Cristo Mountains, Southwestern United States

This study investigates how a Long Short-Term Memory (LSTM) model internally represents baseflow contributions in snowmelt-driven, semi-arid mountain basins with heterogeneous geologic characteristics. Five basins in the Sangre de Cristo Mountains of northern New Mexico, spanning fractured Precambrian bedrock and sedimentary-volcanic terrain, were used to evaluate both model performance and interpretability. Baseflow dynamics were inferred post hoc using the Baseflow Index (BFI) and a two-reservoir HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. Although baseflow components were not explicitly included in model training, internal cell state activations exhibited strong correlations with both shallow and deep baseflow components derived from the HEC-HMS model. To better understand how these relationships may change under climatic stress, BFI-based baseflow patterns were further analyzed under pre-drought and drought conditions. Results indicate that the internal LSTM states differentiated patterns consistent with short- and long-residence flow paths, reflecting physically interpretable hydrologic behavior. This work demonstrates the potential of LSTM models to provide valuable insights into baseflow generation and groundwater–surface water interactions, which is especially critical in water-scarce regions facing increasing drought frequency.