Predicting Post-Wildfire Stream Temperature and Turbidity: A Machine Learning Approach in Western U.S. Watersheds

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R2 of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies.