The influence of carbon costs and benefits on predicted vegetation behaviour along a precipitation gradient using the Vegetation Optimality Model
Authors: Nijzink Remko , Beringer Jason , Hutley Lindsay , Schymanski Stan 
Published: March 2020

&lt;p&gt;Vegetation properties such as rooting depths and vegetation cover play a key role in coupling ecological and hydrological processes. These properties are however highly variable in space and/or time and their parametrization generally poses challenges for terrestrial biosphere models (Whitley et al., 2016). Models often use static values for dynamic vegetation properties or prescribe values based on observations, such as remotely sensed leaf area index. Here, vegetation optimality provides a way forward in order to predict such vegetation properties and their response to environmental change (Schymanski et al., 2015).&lt;/p&gt;&lt;p&gt;In this study, we explore the utility of a combined water-vegetation model, the Vegetation Optimality Model (VOM, Schymanski et al., 2009), to predict vegetation properties such as rooting depths, foliage cover, photosynthetic capacity and water use strategies. The VOM schematizes perennial trees and seasonal grasses each as a single big leaf with an associated root system and optimizes leaf and root system properties in order to maximize the Net Carbon Profit, i.e. the difference between the total carbon taken up by photosynthesis and all the carbon costs related to the construction and maintenance of the plant organs involved. The VOM was applied along the North-Australian Tropical Transect, which consists of six savanna sites equipped with flux towers along a strong rainfall gradient between 500 and 1700 mm per year. The multi-annual half-hourly measurements of evaporation and CO&lt;sub&gt;2&lt;/sub&gt;-assimilation at the different sites were used here to evaluate the model.&lt;/p&gt;&lt;p&gt;The VOM produced similar or better results than more traditional models even though it requires much less information about site-specific vegetation properties. However, we found a persistent bias in the predicted vegetation cover. More detailed numerical experiments revealed a likely misrepresentation of the foliage costs in the model, which are based on a linear relation between leaf area and fractional vegetation cover. This finding, and the already favourable comparison with traditional models, implies that optimization of vegetation properties for Net Carbon Profit is a very promising approach for predicting the soil-vegetation-atmosphere exchange of water and carbon in complex ecosystems such as savannas.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;br&gt;&lt;/strong&gt;Schymanski, S.J., Roderick, M.L., Sivapalan, M., 2015. Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2 concentrations. AoB PLANTS 7, plv060. https://doi.org/10.1093/aobpla/plv060&lt;/p&gt;&lt;p&gt;Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B., Beringer, J., 2009. An optimality&amp;#8208;based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resources Research 45. https://doi.org/10.1029/2008WR006841&lt;/p&gt;&lt;p&gt;Whitley, R., Beringer, J., Hutley, L.B., Abramowitz, G., De Kauwe, M.G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., Yu, Q., 2016. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245&amp;#8211;3265. https://doi.org/10.5194/bg-13-3245-2016&lt;/p&gt;