2016
DOI: 10.1590/1519-6984.14414 View full text |Buy / Rent full text
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Abstract: The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO 2 ) were performed on 11 individu… Show more

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“…The values for light response curves (A 9 Photosynthetic Active Radiation -PAR) were between 0 and 800 lmol m -2 s -1 , performed under Light Emitting Diode (LED) source light with blue = 10 % and CO 2 concentration fixed at 400 lmol mol -1 . Some tests were performed for A 9 PAR curves and we concluded that plants saturate net photosynthesis in values close to 800 lmol m -2 s -1 (instead of 1500 lmol m -2 s -1 which is the most common saturating light intensity for C 3 species) (Rezende et al 2016). We understand that even with a high amount of available light, water limitation makes this species adopt a more conservative strategy and not cope well with high radiation, especially in periods when soil moisture is not abundant.…”
Section: Field Measurementsmentioning
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“…The values for light response curves (A 9 Photosynthetic Active Radiation -PAR) were between 0 and 800 lmol m -2 s -1 , performed under Light Emitting Diode (LED) source light with blue = 10 % and CO 2 concentration fixed at 400 lmol mol -1 . Some tests were performed for A 9 PAR curves and we concluded that plants saturate net photosynthesis in values close to 800 lmol m -2 s -1 (instead of 1500 lmol m -2 s -1 which is the most common saturating light intensity for C 3 species) (Rezende et al 2016). We understand that even with a high amount of available light, water limitation makes this species adopt a more conservative strategy and not cope well with high radiation, especially in periods when soil moisture is not abundant.…”
Section: Field Measurementsmentioning
“…These sources were: (1) data observed for shrubs located in Austria and New Zealand (Bonan et al 2012;Kattge et al 2009;Kattge and Knorr 2007); (2) data for shrubs obtained in campaigns in the Brazilian semiarid biome, published here in this article and partially by Rezende et al (2016); (3) values of Vc max defined in the models. All Vc max values (from models and observed) were normalized to 25°C (Bernacchi et al 2001).…”
Section: Comparison Of Vc Max Valuesmentioning
“…The maximum rate of carboxylation Vcmax is one of the most important drivers of plant net assimilation, and hence determines the carbon available for each cohort (Dietze et al 2014;Longo et al 2019). Those two parameters are excellent candidates to calibrate the ecosystem's land fluxes and are often used to do so, including in ED2.2 (Camino et al 2019;Fer et al 2018;Rezende et al 2013;Sakschewski et al 2015;Tan et al 2010).…”
Section: Parameter Data Assimilation and Model Equifinality (Analysismentioning
“…Parameter uncertainty can be reduced by constraining the range of variation of model parameters through the assimilation of different sources of observations or via model optimization (LeBauer et al 2013). In the past, TBMs have often been calibrated with eddy covariance data (Fer et al 2018;Rezende et al 2016;Collalti et al 2016). While this approach ensures that the model correctly reproduces the short timescale (diurnal/seasonal) dynamics of land fluxes, it does not ensure an accurate representation of forest structure and carbon pools.…”
Section: Introductionmentioning
“…is an ED2 parameter that contributes a lot to the model uncertainty while being poorly constrained (Viskari et al, 2019). V cmax is a parameter that is often targeted for calibration in vegetation models, including ED2, given its broad impact on the ecosystem functioning [see for instance Camino et al (2019);Fer et al (2018); and Rezende et al (2013)]. We chose these two parameter types for this model calibration given (i) their nature and their contribution to model predictive uncertainty and (ii) the difficulty to constrain them with observational data when dealing with interspecific competition in ED2 (Meunier et al, 2020).…”
Section: Model Calibrationmentioning