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

Abstract

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 C3 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 C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) 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 R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 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 R2 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 Pg C yr−1 (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).

When and where soil is important to modify the carbon and water economy of leaves

“…This is why it has been considered to be a value optimized by natural selection (Prentice et al ., 2014). The C i / C a ratio is also a pivotal physiological variable for the coupling of transpiration and photosynthesis from vegetated surfaces in relation to climate in Earth System Models (ESMs) (Wang et al ., 2014; Stocker et al ., 2020). The impact of soil environment on biophysical–biochemical interactions and C i / C a variation is expected to be substantial but has yet to be quantified.…”

Revisiting global vegetation controls using multi-layer soil moisture

“…Previous studies investigated dominant hydro-meteorological controls of vegetation productivity at a global scale and across different ecosystems (Nemani et al, 2003;Beer et al 2010;Jung et al 2011;Seddon et al, 2016;Madani et al, 2017;Jung et al, 2017;Walther et al, 2019;Li & Xiao, 2020). While these studies and recent gross primary production (GPP) estimates agree that vegetation in (semi-)arid area is significantly impacted by soil moisture (SM) (Stocker et al, 2018;Stocker et al, 2020), a corresponding global analysis including the impact of SM from multiple depths is lacking. Several studies have already highlighted the local relevance of multi-layer SM to ecosystems: root water uptake from deeper soil layers can help mitigate water stress and maintain plant transpiration (Schulze et al, 1996;Migliavacca et al, 2009); A et al, 2019 demonstrated varying relative importance of surface SM versus deeper SM depending on land cover types; and Schlaepfer et al, 2017 simulated an increased dryness of sub-surface SM compared to surface SM which largely impacted vegetation dynamics in temperate drylands.…”

Towards a unified theory of plant photosynthesis and hydraulics

“…Building upon this approach, Wang et al (2017) explicitly optimize photosynthetic capacity (albeit using a separate optimization criterion) and have been successful in predicting the assimilation rates and leaf-internal CO2 concentrations across climatic gradients. However, their model lacks a representation of plant hydraulics, and thus cannot predict plant responses to soil drought, especially when soil and atmospheric water deficits are decoupled (Stocker et al, 2018(Stocker et al, , 2020. Here, we extend the foundational principles of Wang et al (2017) with the principles of plant hydraulics and recast them in a profit-maximization framework.…”