Exploring the Influence of Source and Scale of Phenological Model Inputs at Continental Scale

Gridded time series of climatic variables are key inputs to phenological models because they allow the generation of spatially continuous products. To date, there have been few efforts to evaluate how the source and spatial resolution (i.e., scale) of the input data might affect how phenological models and associated indices track variations and shifts at continental scale. This study represents the first such assessment, based on cloud computing. We compared and validated gridded estimate of day of year (DOY) for First Leaf (FL) and First Bloom (FB) emergences in plants that were obtained using Daymet (at 1-km, 4-km, 35-km, and 100-km spatial resolution) and gridMET (at 4-km, 35-km, and 100-km) temperature data. These products were used to estimate temporal trends in DOY for first leaf and first bloom indices in the coterminous US (CONUS) from 1980 to 2016. DOYs driven from gridMET are substantially biased toward later DoYs by up to four months.