Characterizing possible failure modes: Insights from LSTM-Based Streamflow Predictions

Long Short-Term Memory (LSTM) networks have become popular for streamflow prediction in hydrological systems due to their ability to model sequential data. However, their reliance on lumped catchment representation and climate summaries often limits their capacity to capture spatial heterogeneity in rainfall patterns and evolving rainfall trends, both of which are critical for hydrological consistency. This study explores the limitations of LSTM-based streamflow predictions by employing a distributed conceptual hydrological model, SIMHYD, coupled with Muskingum-Cunge routing, to generate synthetic datasets representing diverse hydroclimatic conditions. These datasets are designed to replicate rainfall-runoff dynamics across selected catchments from all 18 ecoregions in CAMELS-US and key Indian river basins, providing a robust testbed for evaluating model performance under controlled conditions. The pre-trained LSTM model is tested against synthetic discharge data, enabling direct comparisons to assess its ability to simulate realistic hydrological responses. Performance is evaluated using multiple metrics, including Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Percent Bias (PBIAS), and mean peak timing errors, to identify systematic deviations. Results reveal that LSTM models struggle with spatially variable and temporally shifting rainfall patterns, leading to inaccuracies in peak flow timing, magnitude, and overall discharge volumes. These issues highlight vulnerabilities in current LSTM-based flood forecasting systems, particularly in their ability to generalize across diverse climatic conditions and regions. This study also characterizes specific failure pathways, such as underestimation of extreme events and poor temporal coherence in hydrographs, which are critical for operational forecasting. By diagnosing these limitations, the study provides a framework for integrating process-based hydrological knowledge with data-driven techniques to improve model robustness. The findings underscore the importance of using synthetic datasets and diverse diagnostic tools to evaluate and enhance the reliability of LSTM-based models, paving the way for hybrid approaches capable of addressing the complexities of real-world hydrological systems.