Quantifying Post‑Wildfire Hydrologic Response Using LSTMs: Ecoregion Patterns Across the Contiguous United States

Wildfires are increasingly reshaping landscapes across the U.S., disrupting hydrogeologic processes such as runoff, infiltration, and sediment transport—posing major challenges for streamflow prediction and water resource management. Traditional conceptual and physically based hydrologic models often struggle to capture these disturbance-driven dynamics. In this study, we explore the potential of long short-term memory (LSTM) networks, a type of recurrent neural network, to simulate post-fire streamflow across 1,082 fire-affected basins spanning the contiguous U.S.—representing the first near-continental-scale application of LSTMs for wildfire-related hydrologic prediction. 

Three LSTM models were trained on different temporal splits of fifteen-year datasets containing wildfire events: one using pre-fire data, one using post-fire data, and one using the full dataset. Models were evaluated on unseen basins in both pre- and post-fire windows. Results show that the model trained on the full dataset consistently outperformed the others, underscoring the importance of temporally diverse training data that include disturbance events. Importantly, LSTMs demonstrated strong generalization across disturbed and undisturbed environments, highlighting their ability to learn hydrologic patterns beyond the constraints of traditional process-based modeling frameworks. 

Feature importance analysis revealed that topographic variables (e.g., elevation and slope) were most influential, followed by soil/geologic and vegetation characteristics, while fire-specific indicators (e.g., burn severity) ranked surprisingly low. This suggests that the LSTMs internalized key controls on streamflow response without heavy reliance on the explicit disturbance metrics included. To further isolate the model’s learned response to wildfire, simulations were performed with synthetic unburned conditions for each disturbed basin and compared against burned scenarios. Spatial analysis by EPA Level II ecoregion revealed that in the Southeastern U.S., Ozark/Appalachian Forests, and Mediterranean California, the model identified a persistent, multi-year increase in streamflow-lasting up to three years after wildfire. These regions share ecological characteristics such as high vegetation biomass, seasonal climate regimes, and terrain-driven hydrologic gradients that collectively amplify post-fire reductions in evapotranspiration and enhance runoff generation. In contrast, no significant streamflow change was detected in the Western Cordillera, South Central Prairies or Cold Desert ecoregions, where water-limited climates and lower fuel loads results in a dual-action response of hydrologic buffering and constrained post-fire increases in water yield.    

Together, these findings demonstrate that LSTMs can detect regionally coherent hydrologic responses to wildfire even in the absence of strong dependence on explicit disturbance features, highlighting the promise of AI-driven, data-centric approaches for modeling hydrologic change in an era of increasing disturbances. As wildfires and other extreme events become more frequent, integrating machine learning into hydrologic prediction frameworks offers a powerful pathway toward adaptive water resource management and improved resilience across diverse ecohydrologic settings.