A comparison of two approaches for generating spatial models of growing-season variables for Canada

Abstract

This study produced annual spatial models (or grids) of 27 growing-season variables for Canada that span two centuries (1901-2100). Temporal gaps in the availability of daily climate data-the typical and preferred source for calculating growing-season variables-necessitated the use of two approaches for generating these growing-season grids. The first approach, used only for the 1950-2010 period, employed a computer script to directly calculate the suite of growing-season variables from existing daily climate grids. Since daily grids were not available for the remaining years, a second approach, which employed a machine-learning method called boosted regression trees (BRT), was used to generate statistical models that related each growing-season variable to a suite of climate and water-related predictors. These BRT models were used to generate grids of growing-season variables for each year of the study period, including the 1950-2010 period to allow comparison between the two approaches. Mean absolute errors associated with the BRT-based grids were approximately 30% higher than those associated with the daily-based grids. The two approaches were also compared by calculating trends in growing-season length over the 1950-2010 period. Significant increases in growing-season length were obtained for nearly all ecozones across Canada, and there were no significant differences in the trends obtained from the two approaches. Although the daily-based approach tended to have lower errors, the BRT approach produced comparable map products that should be valuable for periods and regions for which daily data are not available. Show more