Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: A case study of the United States

Authors

  • Moetasim Ashfaq,

    1. Department of Environmental Earth System Science, Stanford University, Stanford, California, USA
    2. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
    3. Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana, USA
    Search for more papers by this author
  • Laura C. Bowling,

    1. Department of Agronomy, Purdue University, West Lafayette, Indiana, USA
    Search for more papers by this author
  • Keith Cherkauer,

    1. Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA
    Search for more papers by this author
  • Jeremy S. Pal,

    1. Department of Civil Engineering and Environmental Science, Frank R. Seaver College of Science and Engineering, Loyola Marymount University, Los Angeles, California, USA
    Search for more papers by this author
  • Noah S. Diffenbaugh

    1. Department of Environmental Earth System Science, Stanford University, Stanford, California, USA
    2. Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana, USA
    3. Woods Institute for the Environment, Stanford University, Stanford, California, USA
    Search for more papers by this author

Abstract

[1] The Intergovernmental Panel on Climate Change's Fourth Assessment Report concludes that climate change is now unequivocal, and associated increases in evaporation and atmospheric water content could intensify the hydrological cycle. However, the biases and coarse spatial resolution of global climate models limit their usefulness in hydrological impact assessment. In order to reduce these limitations, we use a high-resolution regional climate model (RegCM3) to drive a hydrological model (variable infiltration capacity) for the full contiguous United States. The simulations cover 1961–1990 in the historic period and 2071–2100 in the future (A2) period. A quantile-based bias correction technique is applied to the times series of RegCM3-simulated precipitation and temperature. Our results show that biases in the RegCM3 fields not only affect the magnitude of hydrometeorological variables in the baseline hydrological simulation, but they also affect the response of hydrological variables to projected future anthropogenic increases in greenhouse forcing. Further, we find that changes in the intensity and occurrence of severe wet and hot events are critical in determining the sign of hydrologic change. These results have important implications for the assessment of potential future hydrologic changes, as well as for developing approaches for quantitative impacts assessment.

Ancillary