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Comparing the performance of different stomatal conductance models using modelled and measured plant carbon isotope ratios (δ13C): implications for assessing physiological forcing

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posted on 2023-07-26, 13:26 authored by Per E. Bodin, Mary H. Gagen, Danny McCarroll, Neil J. Loader, Risto Jalkanen, Iain Robertson, V. Roy Switsur, John S. Waterhouse, Ewan J. Woodley, Giles H. F. Young, Paul B. Alton
Accurate modelling of long-term changes in plant stomatal functioning is vital to global climate change studies because changes in evapotranspiration influence temperature via physiological forcing of the climate. Various stomatal models are included in land surface schemes, but their robustness over longer timescales is difficult to validate. We compare the performance of three stomatal models, varying in their degree of complexity, and coupled to a land surface model. This is carried out by simulating the carbon isotope ratio of tree leaves (δ13Cleaf) over a period of 53 years, and comparing the results with carbon isotope ratios obtained from tree rings (δ13Cstem) measured at six sites in northern Europe. All three stomatal models fail to capture the observed interannual variability in the measured δ13Cstem time series. However, the Soil-Plant-Atmosphere (SPA) model performs significantly better than the Ball-Berry (BB) or COX models when tested for goodness-of-fit against measured δ13Cstem. The δ13Cleaf time series simulated using the SPA model are significantly positively correlated (P < 0.05) with measured results over the full time period tested, at all six sites. The SPA model underestimates interannual variability measured in δ13Cstem, but is no worse than the BB model and significantly better than the COX model. The inability of current models to adequately replicate changes in stomatal response to rising levels of CO2 concentrations, and thus to quantify the associated physiological forcing, warrants further investigation.



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Global Change Biology






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ARCHIVED Faculty of Science & Technology (until September 2018)

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