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

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 R-2 = 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 gm(-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 model in regional to continental-scale data assimilation schemes. Crown Copyright (C) 2015 Published by Elsevier B.V.