Impacts of reduced model complexity and driver resolution on cropland ecosystem photosynthesis estimates

Andrew Revill, Alexis Bloom, Mathew Williams

Research output: Contribution to journalArticlepeer-review


Landscape and regional estimates of crop photosynthesis are
required to support research into food security, carbon (C) cycling and
land surface processes. Quantifying C uptake by cropland ecosystems is
complicated by spatial heterogeneity. A major challenge is to upscale the
detailed understandings embodied in process models that have been
validated at specific sites with high resolution inputs. At landscape
scales the input requirements for such complex models are generally
unavailable (e.g. site specific parameters, hourly meteorological data),
and the computing demands are prohibitive. We demonstrate a simplified
crop C aggregated canopy model (ACM) predicting daily photosynthesis,
requiring minimal parameters. This simple model emulates a high
resolution model (SPAc, half-hourly time-steps; simulating leaf to canopy
processes) whilst using coarser-scale (daily) drivers. Based on the SPAc
model outputs, Bayesian inference is used to calibrate the simple
photosynthesis model scalar coefficients at eight European cereal crop
sites. We test whether a single calibration, generated from only four of
the sites (i.e. calibration sites), is effective across all sites (i.e.
including independent validation sites). We further investigate the error
introduced by using regional meteorological drivers over local
observations. We show that, compared to photosynthesis estimated from
eddy covariance at the sites, the simple model produced comparable
results to the complex model: both models explained a similar proportion
of daily variability in photosynthesis (mean R2 = 0.78 for ACM, 0.77 for
SPAc), and had similar model error (mean RMSE = 2.89 g m-2 d-1 for ACM,
3.20 g m-2 d-1 for SPAc). Thus, the simple model, which has much reduced
computational requirements, shows no reduction in model reliability and
offers a simple means to upscale a critical process. We discuss the
importance of the simple
Original languageEnglish
JournalField crops research
Early online date4 Jan 2016
Publication statusPublished - 15 Feb 2016


  • Cropland carbon cycling
  • Crop modelling
  • Markov Chain Monte
  • cropland photosynthesis modelling
  • winter wheat


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