Streamflow simulations using regionalized Long Short-Term Memory (LSTM) neural network models in contrasting climatic conditions

We investigate the potential of using Long Short-Term Memory (LSTM) neural networks for estimating streamflow in (sub)tropical catchments under contrasting hydroclimatic regimes (semi-arid and humid). We have used 176 Brazilian catchments with at least 30 years of streamflow data and LSTM models with 16 static catchment attributes as input features. We tested different LSTM model configurations to assess their sensitivity to varying input sequence lengths (lookbacks). The primary objective was to explore the hydrological insights offered by LSTM-based streamflow models and compare their performance with the traditional GR4J hydrological model. With this design, we aim to address two research questions: (i) Does the performance of LSTM models depend on catchments' hydroclimatic characteristics? (ii) How effective are LSTM-based models for streamflow simulation in tropical and subtropical catchments under semi-arid and humid conditions? We adopt two modeling approaches: (1) regionalized models trained on catchments within the same hydroclimatic regime and (2) a composite model trained on a heterogeneous sample combining both arid and humid catchments. The findings reveal distinct sensitivities of LSTM models to hydroclimatic conditions. LSTM models exhibit higher sensitivity to the length of input sequences (lookbacks) in humid catchments, with longer sequences yielding better performance. This is attributed to the dominant hydrological processes in humid regions, which are influenced by long-term memory effects such as soil moisture and groundwater storage. Conversely, this sensitivity is not observed in semi-arid catchments, where streamflow dynamics are primarily driven by short-term precipitation events and exhibit less dependence on long-term hydrological processes. Furthermore, the composite model, which combines semi-arid and humid catchments, demonstrates a decrease in performance for semi-arid catchments. This suggests that adding catchments with contrasting hydroclimatic characteristics introduces heterogeneity in the dataset, potentially reducing the model's ability to capture the specific dynamics of semi-arid catchments. Overall, the regionalized LSTM models outperformed the GR4J model in both semi-arid and humid regimes, particularly in humid catchments. Approximately 87% of humid catchments and 50% of semi-arid catchments achieved Kling-Gupta Efficiency (KGE) values above 0.60 during the testing phase of the regionalized LSTM models. These results highlight the potential of LSTM networks for streamflow regionalization, especially in humid regions where long-term hydrological memory plays a critical role. The study underscores the strengths and limitations of LSTM models in tropical and subtropical catchments with contrasting hydroclimatic regimes. The findings suggest that LSTM models could serve as valuable tools for regional hydrological applications, aiding local and regional decision-making processes. Additionally, the results emphasize the importance of tailoring LSTM model configurations to the specific hydrological characteristics of catchments, particularly the choice of input sequence length, to maximize model performance.