Biogeosciences, 13, 3245-3265, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
03 Jun 2016
A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas
Rhys Whitley1, Jason Beringer2, Lindsay B. Hutley3, Gab Abramowitz4, Martin G. De Kauwe1, Remko Duursma5, Bradley Evans6, Vanessa Haverd7, Longhui Li8, Youngryel Ryu9, Benjamin Smith10, Ying-Ping Wang11, Mathew Williams12, and Qiang Yu7 1Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
2School of Earth and Environment, University of Western Australia, Crawley, WA 6009, Australia
3School of Environment, Charles Darwin University, Casuarina, NT 0810, Australia
4Climate Change Research Centre, University of New South Wales, Kensington, NSW 2033, Australia
5Hawkesbury Institute for the Environment, University of Western Sydney, Penrith, New South Wales 2751, Australia
6Faculty of Agriculture and Environment, University of Sydney, Eveleigh, NSW 2015, Australia
7CSIRO Ocean and Atmosphere, Canberra 2601, Australia
8School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
9Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea
10Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
11CSIRO Ocean and Atmosphere, Aspendale, Victoria 3195, Australia
12School of GeoSciences, University of Edinburgh, Edinburgh, UK
Abstract. The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.

Citation: 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., and Yu, Q.: A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas, Biogeosciences, 13, 3245-3265,, 2016.
Publications Copernicus
Short summary
In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
In this study we assess how well terrestrial biosphere models perform at predicting water and...