302-1 Patterns and Dynamics of In-Stream Large Wood Following Wildfire

Abstract:

The cascade of disturbances that commonly occur following wildfire often culminate in elevated erosion risks, threatening critical infrastructure and natural resources within and downstream of burned areas. In-stream large wood may have several crucial functions that serve to mitigate these hazards in post-fire settings, including increased retention of sediment and enhanced habitat resilience and recovery. However, though past work has demonstrated that large wood loading generally increases following fire, the dynamics and trajectory of wood supply and impact following fire remain poorly understood.  Quantitative field data on wood-sediment interactions following fire are similarly scarce. Here, we measure wood in nearly 400 stream segments in burned areas across the state of Utah that span gradients in topographic setting, forest characteristics, burn severity, and time since fire (1~25 years). Preliminary results suggest that fire measurably increases wood loading to streams within burned areas, with ~90% of individual wood pieces showing evidence of burning and ~95% of jams containing visibly burned wood. Similarly, wood volumes in burned reaches were statistically significantly greater than literature reported values for similar forest types.  A majority of jams within burned areas (~75%) had an observable geomorphic impact, and, of these, roughly 70% stored measurable sediment, indicating that more than half of jams within burned areas impact downstream sediment transport. Stored sediment was primarily coarse gravel, and storage volumes measured in a subset of jams ranged across five orders of magnitude (10^-2 – 10² m³), with a median of ~2 m³. Wood volumes appeared to be a function of valley morphology, burn severity, forest characteristics, and post-fire history (debris flow occurrence, time since fire). Together, results indicate that a) large wood has the potential to mitigate sediment export from burned watersheds, but storage volumes may be relatively minimal in most locations and b) topography and burn severity together control post-fire wood supply, suggesting that wood supply can be empirically modeled at the watershed scale using techniques like machine learning. Results should also help to inform targets for post-fire management actions, including erosion mitigation and stream restoration via wood addition to streams.

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