Large gridded climate datasets facilitate correlation with landscape patterns (e.g. with species distribution), but point-based ecological data are often disconnected from the spatial extent and resolution of available climate data. Consequently, data users must understand the tradeoffs between spatial and temporal resolution, as well as potential biases arising from the modeling approach when selecting data. The Global Regression Ecoregional Analysis of Temperature (GREAT) model was developed to describe monthly temperature experienced by locations along elevation and latitudinal gradients in the central Appalachians. Model performance was assessed at 30 m and 1 km spatial resolutions, with subsequent analysis of the tradeoffs between extracting temperatures from an a priori spatial grid versus modeling temperature for specific locations. Results indicate that temperatures modeled at coarser resolution (1 km) had higher correlation with observed station temperatures because models developed at finer spatial scales (30 m) over-emphasize the influence of topographic variation. Monthly temperatures from the GREAT model were compared with temperatures extracted from the established, spatially weighted gridded products PRISM and Daymet. This comparison showed greater correlation between the spatially weighted modeling approaches than with the global regression-based method. However, monthly temperatures from the GREAT model had the highest correlation with temperatures observed at 14 independent climate stations that were not used in the development of any of the 3 models. The GREAT model allows temperature to be modeled specifically for each station and results in higher model performance than can be achieved with gridded datasets.
All Science Journal Classification (ASJC) codes
- Environmental Chemistry
- Environmental Science(all)
- Atmospheric Science