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Examining Modeling Approaches for the Rainfall‐Runoff Process in Wildfire‐Affected Watersheds: Using San Dimas Experimental Forest

Wildfire can significantly change watershed hydrological processes resulting in increased risks for flooding, erosion, and debris flow. The goal of this study was to evaluate the predictive capability of hydrological models in estimating post‐fire runoff using data from the San Dimas Experimental Forest (SDEF), San Dimas, California. Four methods were chosen representing different types of post‐fire runoff prediction methods, including a Rule of Thumb, Modified Rational Method (MODRAT), HEC‐HMS Curve Number, and KINematic Runoff and EROSion Model 2 (KINEROS2). Results showed that simple, empirical peak flow models performed acceptably if calibrated correctly. However, these models do not reflect hydrological mechanisms and may not be applicable for predictions outside the area where they were calibrated. For pre‐fire conditions, the Curve Number approach implemented in HEC‐HMS provided more accurate results than KINEROS2, whereas for post‐fire conditions, the opposite was observed. Such a trend may imply fundamental changes from pre‐ to post‐fire hydrology. Analysis suggests that the runoff generation mechanism in the watershed may have temporarily changed due to fire effects from saturation‐excess runoff or subsurface storm dominated complex mechanisms to an infiltration‐excess dominated mechanism. Infiltration modeling using the Hydrus‐1D model supports this inference. Results of this study indicate that physically‐based approaches may better reflect this trend and have the potential to provide consistent and satisfactory prediction.