DownScaling the Great Lakes: Techniques for Adaptive Policy

Abstract

<p>Ecosystems have been profoundly shaped by unusually rapid climate change effects largely driven by human activities that release heat-trapping greenhouse gases into the atmosphere. The goal of this research is to develop a strategy to measure the direct effects of climate change on the value of natural resources, particularly Great Lakes water resources; and how humans control these resources through management decisions. This base will assist in developing and supplying the tools and information necessary for decision-making to facilitate enhancements and thus policy revision. The Canada-US Great Lakes Water Quality Agreement (GLWQA) had substantial influence on the cleanup and restoration of the region, however, threats to the Great Lakes in the face of climate change demand a renewal of program and policy approaches to the restoration of beneficial uses as identified in Annex 2. To remedy this, climate models including Statistical Downscaling (SDSM) and Artificial Neural Network (ANN) are developed to produce daily predictions of future climate variables at the regional scale. In this study, separate downscaled precipitation and temperature scenarios are generated using the SDSM and ANN with the calibrations and validations derived from CGCM and Hadley models for Canadian Areas of Concern. Then the Delphi Survey Method was designed and administered participants to verify on significant pressures associated with climate change on related beneficial uses of the Great Lakes. Collaborating both data sets allows for a thorough picture of the effects of climate change and possible adaptation strategies in the Great Lakes required to develop management and sustainable public policies</p>