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Indicators of Climate Change in Idaho: An Assessment Framework for Coupling Biophysical Change and Social Perception

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  • 1 College of Natural Resources, University of Idaho, Moscow, Idaho
  • | 2 Department of Geography, University of Idaho, Moscow, Idaho
  • | 3 Environmental Sciences Program, University of Idaho, Moscow, Idaho
  • | 4 Department of Plant and Earth Science, University of Wisconsin–River Falls, River Falls, Wisconsin
  • | 5 Department of Conservation Social Sciences, University of Idaho, Moscow, Idaho
  • | 6 English Department, University of Idaho, Moscow, Idaho
  • | 7 Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon
  • | 8 Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho
  • | 9 Department of Fish and Wildlife Sciences, University of Idaho, Moscow, Idaho
  • | 10 Great Basin Rangelands Research Unit, USDA–Agricultural Research Service, Reno, Nevada
  • | 11 Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington
  • | 12 Department of Plant, Soil and Entomological Science, University of Idaho, Moscow, Idaho
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Abstract

Climate change is well documented at the global scale, but local and regional changes are not as well understood. Finer, local- to regional-scale information is needed for creating specific, place-based planning and adaption efforts. Here the development of an indicator-focused climate change assessment in Idaho is described. This interdisciplinary framework couples end users’ data needs with observed, biophysical changes at local to regional scales. An online statewide survey of natural resource professionals was conducted to assess the perceived impacts from climate change and determine the biophysical data needed to measure those impacts. Changes to water resources and wildfire risk were the highest areas of concern among resource professionals. Guided by the survey results, 15 biophysical indicator datasets were summarized that included direct climate metrics (e.g., air temperature) and indicators only partially influenced by climate (e.g., wildfire). Quantitative changes in indicators were determined using time series analysis from 1975 to 2010. Indicators displayed trends of varying likelihood over the analysis period, including increasing growing-season length, increasing annual temperature, increasing forest area burned, changing mountain bluebird and lilac phenology, increasing precipitation intensity, earlier center of timing of streamflow, and decreased 1 April snowpack; changes in volumetric streamflow, salmon migration dates, and stream temperature displayed the least likelihood. A final conceptual framework derived from the social and biophysical data provides an interdisciplinary case example useful for consideration by others when choosing indicators at local to regional scales for climate change assessments.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/WCAS-D-13-00070.s1.

Corresponding author address: P. Zion Klos, 875 Perimeter Drive MS 1133, Moscow, ID 83844-1133. E-mail: zion@uidaho.edu

Abstract

Climate change is well documented at the global scale, but local and regional changes are not as well understood. Finer, local- to regional-scale information is needed for creating specific, place-based planning and adaption efforts. Here the development of an indicator-focused climate change assessment in Idaho is described. This interdisciplinary framework couples end users’ data needs with observed, biophysical changes at local to regional scales. An online statewide survey of natural resource professionals was conducted to assess the perceived impacts from climate change and determine the biophysical data needed to measure those impacts. Changes to water resources and wildfire risk were the highest areas of concern among resource professionals. Guided by the survey results, 15 biophysical indicator datasets were summarized that included direct climate metrics (e.g., air temperature) and indicators only partially influenced by climate (e.g., wildfire). Quantitative changes in indicators were determined using time series analysis from 1975 to 2010. Indicators displayed trends of varying likelihood over the analysis period, including increasing growing-season length, increasing annual temperature, increasing forest area burned, changing mountain bluebird and lilac phenology, increasing precipitation intensity, earlier center of timing of streamflow, and decreased 1 April snowpack; changes in volumetric streamflow, salmon migration dates, and stream temperature displayed the least likelihood. A final conceptual framework derived from the social and biophysical data provides an interdisciplinary case example useful for consideration by others when choosing indicators at local to regional scales for climate change assessments.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/WCAS-D-13-00070.s1.

Corresponding author address: P. Zion Klos, 875 Perimeter Drive MS 1133, Moscow, ID 83844-1133. E-mail: zion@uidaho.edu

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