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A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington

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  • T Sheehan
  • D Bachelet

Abstract

Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.

Suggested Citation

  • T Sheehan & D Bachelet, 2019. "A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-21, October.
  • Handle: RePEc:plo:pone00:0222051
    DOI: 10.1371/journal.pone.0222051
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    References listed on IDEAS

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    1. Laurie Houston & Susan Capalbo & Clark Seavert & Meghan Dalton & David Bryla & Ramesh Sagili, 2018. "Specialty fruit production in the Pacific Northwest: adaptation strategies for a changing climate," Climatic Change, Springer, vol. 146(1), pages 159-171, January.
    2. W. Matt Jolly & Mark A. Cochrane & Patrick H. Freeborn & Zachary A. Holden & Timothy J. Brown & Grant J. Williamson & David M. J. S. Bowman, 2015. "Climate-induced variations in global wildfire danger from 1979 to 2013," Nature Communications, Nature, vol. 6(1), pages 1-11, November.
    3. Laurie Houston & Susan Capalbo & Clark Seavert & Meghan Dalton & David Bryla & Ramesh Sagili, 2018. "Erratum to: Specialty fruit production in the Pacific Northwest: adaptation strategies for a changing climate," Climatic Change, Springer, vol. 146(1), pages 173-173, January.
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    Cited by:

    1. Wang, Yuhan & Lewis, David J., 2024. "Wildfires and climate change have lowered the economic value of western U.S. forests by altering risk expectations," Journal of Environmental Economics and Management, Elsevier, vol. 123(C).

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