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Climatic Change

, Volume 105, Issue 3–4, pp 555–576 | Cite as

Simulating the hydrological response to predicted climate change on a watershed in southern Alberta, Canada

  • Katharine A. Forbes
  • Stefan W. Kienzle
  • Craig A. Coburn
  • James M. Byrne
  • Joseph Rasmussen
Article

Abstract

The current body of research in western North America indicates that water resources in southern Alberta are vulnerable to climate change impacts. The objective of this research was to parameterize and verify the ACRU agro-hydrological modeling system for a small watershed in southern Alberta and subsequently simulate the change in future hydrological responses over 30-year simulation periods. The ACRU model successfully simulated monthly streamflow volumes (r 2 = 0.78), based on daily simulations over 27 years. The delta downscaling technique was used to perturb the 1961–1990 baseline climate record from a range of global climate model (GCM) projections to provide the input for future hydrological simulations. Five future hydrological regimes were compared to the 1961–1990 baseline conditions to determine the average net effect of change scenarios on the hydrological regime of the Beaver Creek watershed over three 30-year time periods (starting in 2010, 2040 and 2070). The annual projections of a warmer and mostly wetter climate in this region resulted in a shift of the seasonal streamflow distribution with an increase in winter and spring streamflow volumes and a reduction of summer and fall streamflow volumes over all time periods, relative to the baseline conditions (1961–1990), for four of the five scenarios. Simulations of actual evapotranspiration and mean annual runoff showed a slight increase, which was attributed to warmer winters, resulting in more winter runoff and snowmelt events.

Keywords

Streamflow Groundwater Recharge Climate Change Impact General Circulation Model Hydrological Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Katharine A. Forbes
    • 1
  • Stefan W. Kienzle
    • 1
  • Craig A. Coburn
    • 1
  • James M. Byrne
    • 1
  • Joseph Rasmussen
    • 2
  1. 1.Department of GeographyUniversity of LethbridgeLethbridgeCanada
  2. 2.Department of Biological SciencesUniversity of LethbridgeLethbridgeCanada

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