A Parsimonious Downscaling Method for Global Potential Net Primary Production: From 30 arcmin to 30 arcsec Resolution

The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP consists of a comparison of actual net primary production and potential vegetation properties—i.e., vegetation properties that would prevail in the absence of land use (i.e., the NPP of the potential vegetation; NPPpot). While technical advancements in remote sensing have led to global land cover data at high spatial resolution up to 30 arcsec (i.e., 1 km at the equator) and higher, spatial explicit global NPPpot, mainly provided by Dynamic Global Vegetation Models (DGVMs), are usually generated at 30 arcmin (i.e., 55 km at the equator). This spatial mismatch causes uncertainties in HANPP assessments. We here present a workable solution for downscaling NPPpot until global, high-resolution results, for example, from DGVMs, become feasible. This method uses a simple NPPpot model with readily available auxiliary information at higher spatial resolution to generate fine-scale patterns to downscale high-quality coarse scale NPPpot. It includes a smoothing procedure to reduce downscaling artifacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling by applying it to NPPpot results from the LPJ-GUESS DGVM model for the year 2010 to downscale from 30 arcmin to 30 arcsec resolution. This approach generates fine-scaled NPPpot patterns and aligns with alternative models for smaller geographic units while computational cost is acceptable.