P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

Benjamin D. Stocker, Han Wang, Nicholas G. Smith, Sandy P. Harrison, Trevor F. Keenan, David Sandoval, Tyler Davis, I. Colin Prentice

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for <span classCombining double low line"inline-formula">C3</span> photosynthesis with an optimality principle for the carbon assimilation-Transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and <span classCombining double low line"inline-formula">C3</span> vegetation type. The model builds on the theory developed in <span classCombining double low line"cit" idCombining double low line"xref-text.1"><a hrefCombining double low line"#bib1.bibx139">Prentice et al.</a> (<a hrefCombining double low line"#bib1.bibx139">2014</a>)</span> and <span classCombining double low line"cit" idCombining double low line"xref-text.2"><a hrefCombining double low line"#bib1.bibx193">Wang et al.</a> (<a hrefCombining double low line"#bib1.bibx193">2017</a><a hrefCombining double low line"#bib1.bibx193">a</a>)</span> and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the <span classCombining double low line"inline-formula"R/i2/span for predicted versus observed GPP based on the full model setup is 0.75 (8&thinsp;d mean, 126 sites)-similar to comparable satellite-data-driven GPP models but without predefined vegetation-Type-specific parameters. The <span classCombining double low line"inline-formula">R2</span> is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The <span classCombining double low line"inline-formula">R2</span> for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106-122&thinsp;Pg&thinsp;C&thinsp;yr<span classCombining double low line"inline-formula">-1</span> (mean of 2001-2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting-rather than prescribing-light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

Original languageEnglish
Pages (from-to)1545-1581
Number of pages37
JournalGeoscientific Model Development
Volume13
Issue number3
DOIs
StatePublished - Mar 26 2020

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