pp 1–17 | Cite as

Applying Climate Change Risk Management Tools to Integrate Streamflow Projections and Social Vulnerability

  • Sheila M. SaiaEmail author
  • Kelly M. Suttles
  • Bethany B. Cutts
  • Ryan E. Emanuel
  • Katherine L. Martin
  • David N. Wear
  • John W. Coulston
  • James M. Vose


Shifts in streamflow, due to future climate and land use change, may pose risks to nearby human communities. Projecting the spatial distribution and impacts of these risks requires consideration of biophysical and socioeconomic factors. Models like the Soil and Water Assessment Tool (SWAT) can project spatial distributions of hydrologic risk due to shifting biophysical factors like climate and land use, but cannot account for socioeconomic factors influencing a community’s capacity to adapt to future streamflow changes. To address this limitation, we used a risk matrix to classify subbasins in a large river basin in the southeastern USA based on (1) percent increase in SWAT simulated 10-year and extreme high flows due to climate and land use change between baseline (1982–2002) and projected (2050–2070) periods and (2) degree of community vulnerability according to a Social Vulnerability Index (SVI). We compared spatial distributions of high-risk subbasins based on SWAT results, SVI results, and the integration of SWAT and SVI results using a risk matrix. Large increases in simulated 10-year and extreme high flows occurred in middle and lower parts of the river basin, and socially vulnerable communities were distributed throughout. We identified 16, 7, and 14 unique high-risk subbasins using SWAT results, SVI results, and SWAT and SVI results, respectively. By using a risk matrix, we identified subbasins with vulnerable communities that are projected to experience future increases in streamflow due to climate and land use change. These results serve as a starting point for subsequent climate change adaptation planning.


climate change adaptation planning land use change social vulnerability soil water assessment tool water resources 



SMS and KMS were supported by an appointment to the United States Department of Agriculture Forest Service (USFS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the United States Department of Energy (DOE) and the USFS. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. All opinions expressed in this paper are the authors’ and do not necessarily reflect the policies and views of USFS. The manuscript was greatly improved by reviews from two anonymous reviewers, Dr. Louis Iverson and Dr. Travis Warzineak. All data and scripts associated with this publication are available on GitHub at and Zenodo (DOI:

Supplementary material

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Copyright information

©  Springer Science+Business Media, LLC, part of Springer Nature (This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply) 2019

Authors and Affiliations

  1. 1.Oak Ridge Institute for Science and EducationOak RidgeUSA
  2. 2.United States Department of Agriculture Forest Service Southern Research StationDurhamUSA
  3. 3.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighUSA
  4. 4.Center for Housing and Community StudiesUniversity of North Carolina at GreensboroGreensboroUSA
  5. 5.Department of Tourism, Parks and ManagementNorth Carolina State UniversityRaleighUSA
  6. 6.Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighUSA
  7. 7.United States Department of Agriculture Forest Service Southern Research StationBlacksburgUSA

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