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Response of the Fluvial Wood System to Fire and Floods in Northern Yellowstone

Bibliographic Data

ID8375005
AuthorsW Andrew Marcus (University of Oregon), James Rasmussen, J L Rasmussen (University of Florida), Mark A Fonstad (0000-0001-9443-6638, Texas State University)
Year2011
Volume101
Issue1
Pages21-44
Publication date2011-01-21
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the Association of American Geographers (JOURNAL)
Journal identifiersISSN: 0004-5608 • E-ISSN: 1467-8306
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00045608.2010.539154
OpenAlexW2021508912
LanguageEN
References cited73

We map the distribution of wood continuously along 73.4 km of second- through fifth-order streams in Yellowstone National Park and develop a Monte Carlo model of wood supply, transport, and storage to investigate: (1) spatial distributions of wood in burned and unburned basins, (2) associations between geomorphology and spatial distributions of wood, and (3) effects of flood timing and sequence on wood distributions. Wood counts in 2000 and 2001 vary by over two orders of magnitude over distances of several hundred meters. Qualitative models using catchment-scale characterizations of streams (e.g., whether a stream is third or fourth order) do not explain the degree of local variability. Reach-scale wood counts are weakly related to channel width in the extensively burned Cache Creek basin and to streamside forest cover and number of channel threads in all basins. Wood counts are not related to other reach-scale variations in channel morphology or fire effects. Monte Carlo simulations indicate that postfire flood timing and sequence drive variability in wood counts, with spatial variations in dominant processes and asynchronous timing of fire and floods creating a complex cascade of disturbance and a patchwork mosaic of wood. Existing descriptive models for postfire wood response do not predict this mosaic, although they capture the central tendency of variations. The central tendency is, however, a poor predictor of postdisturbance wood accumulations, so we develop a probabilistic model that better captures the range of potential postfire responses. We make recommendations for wood monitoring and management based on the empirical data and model results

Archaeology · Fluvial · Geography · Geomorphology · Geotechnical engineering · Hydrology (agriculture) · Physical geography · Structural basin · Fire effects on ecosystems · Forestry · Geology · Landslides and related hazards · Soil erosion and sediment transport

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Citation velocityhistorical
Highly citedNo

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