A Parsimonious Downscaling Method for Global Potential Net Primary
Production: From 30arcmin to 30arcsec Resolution
Abstract
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 requires integrating data
from sources such as remote sensing and census statistics, as well as
modelled data like potential Net Primary Production (NPP), which
reflects NPP without land use. Although the availability of global land
cover data at high spatial detail from remote sensing has improved, with
resolutions reaching 30 arcseconds (about 1 km) and higher, global NPP
results from Dynamic Global Vegetation Models (DGVMs) are still
unavailable at this resolution. This spatial mismatch causes
uncertainties, as simple interpolation methods fail to capture
fine-scaled productivity patterns. We here present a parsimonious method
to downscale NPP, using the Miami NPP model with temperature and
precipitation data as readily available auxiliary information at high
spatial resolution. Our method uses a moving window approach with
Gaussian convolution to minimize downscaling artefacts. We demonstrate
this Smooth Auxiliary Data (SAD) downscaling approach by downscaling
potential NPP results from the LPJ-GUESS-DGVM model for the year 2010
from 30arcmin to 30arcsec resolution. This approach, requiring low
computational cost, generates fine-scaled productivity patterns and
aligns with alternative models for smaller geographic units, offering a
solution until high-resolution DGVM results become feasible.