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.