EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards a land surface model based on optimality principles

Giulia Mengoli1, Sandy P. Harrison2,4, and Iain Colin Prentice1,3,4
Giulia Mengoli et al.
  • 1Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK (
  • 2Geography & Environmental Sciences, Reading University, Reading, UK
  • 3Department of Biological Sciences, Macquarie University, North Ryde, Australia
  • 4Department of Earth System Science, Tsinghua University, Beijing, China

Plants take up water from the soil via roots and release it into the atmosphere through stomata; uptake of CO2 from the atmosphere also proceeds through the stomata, implying tight coupling of transpiration and photosynthesis. We distinguish leaf-level (biochemical and stomatal) responses to external stimuli on different timescales: fast responses taking place over seconds to hours, and longer-term (acclimation) responses taking place over weeks to months. Typically, land-surface models (LSMs) have focused on the fast responses, and have not accounted for acclimation responses, although these can be different in magnitude and even in sign. We have developed a method that explicitly separates these two timescales in order to implement an existing optimality-based model, the P model, with a sub-daily timestep; and, thereby, to include acclimated responses within an LSM framework. The resulting model, compared to flux-tower gross primary production (GPP) data in five “well-watered” biomes from boreal to tropical, correctly reproduces diurnal cycles of GPP throughout the growing season. No changes of parameters are required between biomes, because optimality ensures that current parameter values are always adapted to the local environment. This is a clear practical advantage because it eliminates the need to specify different parameter values for different plant functional types. However, in areas with large seasonal variations in moisture variability, the model does not perform well. Here we address the issue of soil-moisture controls on GPP, which is a challenging issue for LSMs in general. We note two problems: an error in magnitude, and an error in shape. The model tends to overestimate GPP in dry areas because it does not consider the effect of low soil moisture (as opposed to atmospheric dryness) on photosynthesis; and it does not simulate the ‘midday depression’ that is observed under very high vapour pressure deficits. Moving beyond commonly used (empirical) water-stress formulations, we have incorporated soil moisture limitation on photosynthesis in the sub-daily P model. The main idea is to control GPP via hydraulic limitation. The revised model firstly assesses the “demand”—the transpiration that would take place under well-watered conditions—then constrains the actual transpiration at a rate that does not exceed the canopy’s estimated hydraulic capacity. This transpiration rate is then used to obtain revised rates of stomatal conductance and GPP, “corrected” for water stress. Preliminary results evaluating the revised model’s performances against flux tower measurements at dry sites are encouraging, suggesting a route towards a parameter-sparse and globally applicable LSM.

How to cite: Mengoli, G., Harrison, S. P., and Prentice, I. C.: Towards a land surface model based on optimality principles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1331,, 2022.