Effects of climate data aggregation on regional NPP modelling
Résumé
In ecosystem modelling studies, data are often collected at a small scale but are used in models to
predict ecosystem responses e.g. net primary production (NPP) at coarser scale, using data
aggregation. The data aggregation causes errors that are difficult to predict, because of model
complexity and our limited knowledge of the spatial heterogeneity underlying our aggregated input
values. Input aggregation thus adds to the uncertainty associated with model predictions. One way of
reducing uncertainty related to input aggregation error is to evaluate models at different resolutions for
areas where high-resolution input data are available. The objective of this study was the quantification
of the aggregation error on modelled NPP introduced by climate data aggregation. Therefore, climate
data at 1 km x 1 km resolution (>30 000 grid cells) were used as baseline to simulate NPP with 11
different crop and biogeochemical models for the federal state North Rhine-Westphalia (Germany).
These results, for 29 year monocultures of wheat and maize cropping systems, respectively, were
compared with simulation results using aggregated climate data for four resolutions (10, 25, 50, 100
km grid cell side length).The aggregation effect is represented by the maximum differences between
the NPP, averaged for wheat and maize cropping systems over the growing season, simulated for the
five different resolutions. Input data aggregation had little impact on NPP of 29 year averages (0.5 –
7.8 % for wheat and 0.3 – 10 % for maize), while the climate data aggregation effect was higher for
single years; up to 9 % and 13 % for wheat and maize, respectively, gradually decreasing to low
effects for averages over 10 year periods or longer. The scale effect differed among models and
shows only a minor impact (2%) for an ensemble run.
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