EGU22-2886, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Prediction of leaf area dynamics by maximizing the Net Carbon Profit

Remko C. Nijzink and Stanislaus J. Schymanski
Remko C. Nijzink and Stanislaus J. Schymanski
  • Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg (

Leaf area dynamics are often prescribed in terrestrial biosphere models (TBMs) or based on predefined carbon allocation rules and plant functional types. However, reliance on observational data hampers predictions under future scenarios, as leaf area dynamics and allocation patterns may change due to feedbacks with soil and atmosphere. Therefore, dynamical modelling of leaf area in TBMs based on fundamental principles could greatly improve our ability to better understand and predict vegetation response to environmental change.

The Vegetation Optimality Model (VOM, Schymanski et al., 2009) uses an optimality principle based on the maximization of the Net Carbon Profit (NCP) to predict vegetation properties such as root distributions, photosynthetic capacity and vegetation cover at the daily time scale, as well as water and CO2 exchange at the hourly scale. The NCP is defined as the difference between the total CO2 assimilated by photosynthesis and the carbon costs for construction and maintenance of the light and water harvesting plant organs. In a previous study (Nijzink et al. 2021), we found that the VOM systematically overestimated wet season light absorption and CO2 uptake along the North Australian Tropical Transect (NATT), suggesting that the original big-leaf approach may be missing self-shading effects at high leaf area index (LAI) values. Therefore, we extended the VOM to explicitly consider light absorption as a function of the LAI, and dynamically optimize LAI while considering the carbon costs and benefits of maintaining leaf area. The model was extended step-wise while its predictions were compared to measurements at five flux tower sites along the NATT, with a strong precipitation gradient from north to south.

Here we present the insights gained from this process, including the importance of considering sunlit and shaded leaf area fractions, and separate optimization of photosynthetic capacity for each. In a first step, dynamical leaf area was introduced in the VOM without considering shading, which led to a relatively high CO2-assimilation. Nevertheless, including shaded and sunlit leaf fractions in the big leaf approach of the VOM was not sufficient, as in nature, shaded leaves in the lower canopy have lower photosynthetic capacities than the mostly sunlit upper canopy leaves. For this reason, a separate optimization of photosynthetic capacities, in order to maximize the NCP, was included for shaded and sunlit leaves. Eventually, we will compare the modelled leaf area dynamics and fluxes with remotely sensed LAI and locally measured fluxes at the different flux tower sites along the NATT.



Nijzink, R. C., Beringer, J., Hutley, L. B., and Schymanski, S. J.:, 2021. Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?, Hydrol. Earth Syst. Sci. Discuss. [preprint],, accepted

Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B., Beringer, J., 2009. An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resources Research 45.

How to cite: Nijzink, R. C. and Schymanski, S. J.: Prediction of leaf area dynamics by maximizing the Net Carbon Profit, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2886,, 2022.


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