Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-525-2022
https://doi.org/10.5194/hess-26-525-2022
Research article
 | 
01 Feb 2022
Research article |  | 01 Feb 2022

Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?

Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski

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Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration - Guidelines for computing crop water requirements, FAO – Food and Agriculture Organization of the United Nations, Rome, ISBN 92-5-104219-5, 1998. a
Asrar, G., Fuchs, M., Kanemasu, E. T., and Hatfield, J. L.: Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1, Agron. J., 76, 300, https://doi.org/10.2134/agronj1984.00021962007600020029x, 1984. a
Basler, D.: Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe, Agr. Forest Meteorol., 217, 10–21, https://doi.org/10.1016/j.agrformet.2015.11.007, 2016. a
Baudena, M., Dekker, S. C., van Bodegom, P. M., Cuesta, B., Higgins, S. I., Lehsten, V., Reick, C. H., Rietkerk, M., Scheiter, S., Yin, Z., Zavala, M. A., and Brovkin, V.: Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models, Biogeosciences, 12, 1833–1848, https://doi.org/10.5194/bg-12-1833-2015, 2015. a
Short summary
Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.