Predicting Postfire Sediment Yields of Small Steep Catchments Using Airborne Lidar Differencing

Abstract

Abstract Predicting sediment yield from recently burned areas remains a challenge but is important for hazard and resource management as wildfire impacts increase. Here we use lidar‐based monitoring of two fires in southern California, USA to study the movement of sediment during pre‐rainfall periods and postfire periods of flooding and debris flows over multiple storm events. Using a data‐driven approach, we examine the relative importance of terrain, vegetation, burn severity, and rainfall amounts through time on sediment yield. We show that incipient fire‐activated dry sediment loading and pre‐fire colluvium were rapidly flushed out by debris flows and floods but continued erosion occurred later in the season from soil erosion and, in ∼9% of catchments, from shallow landslides. Based on these observations, we develop random forest regression models to predict dry ravel and incipient runoff‐driven sediment yield applicable to small steep headwater catchments in southern California.

Keywords

• dry ravel
• wildfire
• erosion
• debris flow
• lidar
• random forest