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Examining Modeling Approaches for the Rainfall‐Runoff Process in Wildfire‐Affected Watersheds: Using San Dimas Experimental Forest
Author(s) -
Chen Li,
Berli Markus,
Chief Karletta
Publication year - 2013
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/jawr.12043
Subject(s) - surface runoff , environmental science , watershed , hydrology (agriculture) , runoff curve number , infiltration (hvac) , kinematic wave , baseflow , storm , streamflow , geology , meteorology , geotechnical engineering , computer science , geography , ecology , drainage basin , cartography , machine learning , biology
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, C alifornia. 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 ( KINEROS 2). 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 KINEROS 2, 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.