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Karin Riley

Karin 11-2020

Research Ecologist

Address: 
5775 US Highway 10 W
Missoula, MT 59808-9361
Phone: 
406-329-4806
Fax: 
406-329-4877
Contact Karin Riley

Current Research

Much of my current research focuses on better understanding the relationship between climate and wildfire, and how this relationship might shift with climate change. In addition, I am interested in how spatial planning can be utilized to inform fire and landscape management options.

Research Interests

Much of my current research focuses on better understanding the relationship between climate and wildfire, and how this relationship might shift with climate change. I also use machine learning algorithms to create tree-level models of US forests. In addition, I am interested in how spatial planning can be utilized to inform fire and landscape management options.

Why This Research is Important

Wildland fire has a dual nature. In fire-adapated ecosystems, fire is a natural process that maintains the health of the ecosystem. However, fires may also negatively impact highly valued resources such as homes, watersheds, and habitat, even costing human lives. We can leverage tools such as simulation models, machine learning, and statistical analysis to better understand our forests and wildland fires. In so doing, we can assist land managers in using fires to restore ecosystems where the opportunity exists and help to create ecosystems that will be resilient to climate change.

Education

  • University of Montana, Phd, Geosciences, 2012
  • Humboldt State University, Master Of Science, Environmental Systems, 2001
  • Harvard University, Bachelor Of Arts, Earth and Planetary Science, 1996
  • Professional Experience

    Research Ecologist, Forestry Sciences Lab, Rocky Mountain Research Station, Missoula, Montana
    2015 to 2019

    Research Ecologist, Fire Sciences Lab, Rocky Mountain Research Station, MIssoula, Montana
    2019 to present

    Professional Organizations

    • Association for Fire Ecology, Board Member ( 2012 to present )
      Inclusivity, Journal, and Conference
    • Fire Ecology, Associate Editor ( 2015 to present )
    • Association for Fire Ecology, Vice President ( 2015 to 2019 )

    Featured Publications

    Publications

    Parisien, Marc-Andre; Ager, Alan; Barros, Ana M.; Dawe, Denyse; Erni, Sandy; Finney, Mark A.; McHugh, Charles W.; Miller, Carol L.; Parks, Sean A.; Riley, Karin; Short, Karen C.; Stockdale, Christopher A.; Wang, Xianli; Whitman, Ellen, 2020. Commentary on the article “Burn probability simulation and subsequent wildland fire activity in Alberta, Canada - Implications for risk assessment and strategic planning” by J. L. Beverly and N. McLoughlin
    Steelman, Toddi; Riley, Karin, 2018. #MeToo for the wildfire community
    Hyde, Kevin D.; Riley, Karin; Stoof, Cathelijne, 2017. Uncertainties in predicting debris flow hazards following wildfire [Chapter 19]
    Riley, Karin; Thompson, Matthew P.; Webley, Peter; Hyde, Kevin D., 2017. Uncertainty in natural hazards, modeling and decision support: An introduction to this volume [Chapter 1]
    Littell, Jeremy S.; Peterson, David L.; Riley, Karin; Liu, Yongqiang; Luce, Charles H., 2016. A review of the relationships between drought and forest fire in the United States
    Littell, Jeremy S.; Peterson, David L.; Riley, Karin; Liu, Yongqiang; Luce, Charles H., 2016. Fire and drought [Chapter 7]
    Riley, Karin; Stonesifer, Crystal S.; Calkin, Dave E.; Preisler, Haiganoush, 2015. Assessing predictive services' 7-day fire potential outlook
    Keane II, Robert E.; Jolly, William M.; Parsons, Russell A.; Riley, Karin, 2015. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT
    Riley, Karin; Grenfell, Isaac C.; Finney, Mark A.; Crookston, Nicholas L., 2014. Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses
    Woodall, Christopher W.; Domke, Grant M.; Riley, Karin; Oswalt, Christopher M.; Crocker, Susan J.; Yohe, Gary W., 2013. A framework for assessing global change risks to forest carbon stocks in the United States
    Calkin, David E.; Ager, Alan; Thompson, Matthew P.; Finney, Mark A.; Lee, Danny C.; Quigley, Thomas M.; McHugh, Charles W.; Riley, Karin; Gilbertson-Day, Julie M., 2011. A comparative risk assessment framework for wildland fire management: the 2010 cohesive strategy science report
    Finney, Mark A.; McHugh, Charles W.; Grenfell, Isaac; Riley, Karin, 2010. Continental-scale simulation of burn probabilities, flame lengths, and fire size distribution for the United States
    Forest plot data is matched to gridded landscape data from LANDFIRE using the random forests method. The output consists of a grid of the IDs for the best-matching plot for each pixel.
    https://www.treesearch.fs.fed.us/pubs/53114Maps of the number, size, and species of trees in forests across the western United States are desirable for a number of applications including estimating terrestrial carbon resources, tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies. We used a statistical method called random forests for matching forest plot data with biophysical characteristics of the landscape in order to populate entire landscapes with a limited set of forest plot inventory data. 
    Using structured decision making (SDM) can change how resource managers make decisions by separating the clinical problem analysis from the value based decision process. In a natural resource management setting, SDM necessitates making decisions based on clearly articulated objectives, recognizing scientific prediction in decisions, addressing uncertainty explicitly, and responding with transparency towards societal values in decision making. When used as an overarching framework, natural resource managers can be better equipped to identify, critique, and discuss sources and implications of uncertainty and thus improve decision-making.
    The nexus of fuels management and suppression response planning integrates pre-season actions with wildland fire incident response.
    The RMRS Wildfire Risk Management Team has been instrumental in the development and maintenance of national fire and vegetation datasets that are foundational to U.S. wildfire risk science. The assessment of contemporary wildfire hazard--a fundamental part of the risk assessment framework--is not possible without a reliable source of historical fire-occurrence data.
    Effective and efficient risk based management requires integrated knowledge, systems and planning tools that explore the interaction of the full range of land and fire management activities. The Wildfire Risk Management Team is working with managers to develop and demonstrate the power of integrating fire-risk science across the full range of fire management activities from local to national scales. Improved linkages between landscape fire potential and land management objectives will have profound effects on the efficiency of the full range of fire management activities. 
    Effective and efficient risk-based management requires integrated knowledge, systems and planning tools that explore the interaction of the full range of land and fire management activities. The Wildfire Risk Management team is working to develop and demonstrate the power of integrating fire-risk science across the full range of fire management activities. This work will include the first pilot study of changes in wildfire risk across time, using the prototype LANDFIRE time series dataset, created specifically for the study landscape.
    How is drought affecting the forests and rangelands of the United States? Dr. Karin L. Riley, Research Ecologist with the Human Dimensions program of the USDA Forest Service Rocky Mountain Research Station, participated in a recent effort to synthesize the current science on this topic, along with 76 other scientists from federal land management agencies, universities, and other research institutions.
    The Wildfire Risk Management Team is an interdisciplinary team that explores wildfire management through the lenses of risk analysis, economics, decision science, and landscape ecology to improve the scientific basis for the full range of wildfire management decisions. Primary research topics include integrated spatial risk assessment modeling and planning, econometric modeling of fire management expenditures, effectiveness of suppression resource utilization, organizational structure and managerial incentive systems, and performance measurement.