TITLE

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs

AUTHOR(S)

Wang, Fumin; Gonsamo, Alemu; Chen, Jing; Black, T.; Zhou, Bin

PUB. DATE

November 2014

SOURCE

Oecologia;Nov2014, Vol. 176 Issue 3, p703

SOURCE TYPE

Academic Journal

DOC. TYPE

Article

ABSTRACT

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C mday, and an average Nash-Sutcliffe model efficiency coefficient ( E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66 %. SADM overestimated daily GPP by about 15 % during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30 %, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

ACCESSION #

99020199

 

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