Estuaries and Coasts

, Volume 41, Issue 2, pp 349–372 | Cite as

Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework

  • Barbara A. MuhlingEmail author
  • Carlos F. Gaitán
  • Charles A. Stock
  • Vincent S. Saba
  • Desiree Tommasi
  • Keith W. Dixon


Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.


Chesapeake Bay Statistical downscaling Spatial disaggregation Climate change 



The authors wish to acknowledge T. Miller (University of Maryland) and M. Fabrizio, R. Latour, D. Kaplan, C. Meynard, and D. Gauthier (Virginia Institute of Marine Science) for the provision of observational data in the Chesapeake Bay. H. Townsend, T. Ihde, B. Kinlan, M. Monaco, and J. Manderson contributed valuable advice and discussion on data and analyses. J. Lanzante advised on the implementation of quantile mapping methods. The manuscript was significantly improved by review from A. Muñoz, M. Lee, and two anonymous reviewers. GCMs were assessed using output from the NOAA ESRL climate change portal (J. Scott and M. Alexander). CTD data were obtained from the Chesapeake Bay Program’s water quality database, with help from M. Mallonee. Air temperature observations were obtained from the NOAA National Centers for Environmental Information and Thomas Point observations from the NOAA National Data Buoy Center. CPC US Unified Precipitation data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at Funding and support for this study were provided by the NOAA National Ocean Service (NOS) National Centers for Coastal Ocean Science (NCCOS), the NOAA National Marine Fisheries Service (NMFS) Office of Science & Technology, the NOAA Integrated Ecosystem Assessment (IEA) Program, and the NOAA office of Oceanic and Atmospheric Research (OAR).

Supplementary material

12237_2017_280_MOESM1_ESM.tif (6.4 mb)
ESM 1 Figure S1 Projected changes in surface temperature in the Chesapeake Bay during winter, spring, and fall: 1970-1999 versus 2071-2100. The two models with the strongest (top row) and weakest (bottom row) change between the two time periods are shown for each season. (TIFF 6505 kb)
12237_2017_280_MOESM2_ESM.tif (5.7 mb)
ESM 2 Figure S2 Projected changes in surface salinity in the Chesapeake Bay during spring, summer, and fall: 1970-1999 versus 2071-2100. The two models with the highest (top row) and lowest (bottom row) change between the two time periods are shown for each season. (TIFF 5843 kb)
12237_2017_280_MOESM3_ESM.tif (454 kb)
ESM 3 Figure S3 Mean 2m air temperature in the Susquehanna River watershed 2001-2100, from the four statistical downscaling methods applied to the WW model, averaged across seasons (top), and months (bottom). The difference between mean temperatures in August between the coolest (BCQM) and warmest (CFQM) methods is also shown. (TIFF 453 kb)


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Copyright information

© Coastal and Estuarine Research Federation 2017

Authors and Affiliations

  • Barbara A. Muhling
    • 1
    • 2
    Email author
  • Carlos F. Gaitán
    • 3
  • Charles A. Stock
    • 2
  • Vincent S. Saba
    • 4
  • Desiree Tommasi
    • 1
    • 2
  • Keith W. Dixon
    • 2
  1. 1.Princeton University Program in Atmospheric and Oceanic SciencePrincetonUSA
  2. 2.National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics LaboratoryPrincetonUSA
  3. 3.Arable Labs IncPrincetonUSA
  4. 4.National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Geophysical Fluid Dynamics LaboratoryPrinceton University Forrestal CampusPrincetonUSA

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