Challenges and opportunities in land surface modelling of savanna ecosystems

Rhys Whitley, Jason Beringer*, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying Ping Wang, Mathew Williams, Qiang Yu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

36 Citations (Scopus)


The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.

Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.

Original languageEnglish
Pages (from-to)4711-4732
Number of pages22
Issue number20
Publication statusPublished - 24 Oct 2017

Bibliographical note

Funding Information:
Acknowledgements. This study was conducted as part of the “Australian Savanna Landscapes: Past, Present and Future” project funded by the Australian Research Council (DP130101566). The support, collection and utilisation of data was completed by the OzFlux network ( and Terrestrial Ecosystem Research Network (TERN; and funded by the ARC (DP0344744, DP0772981 and DP130101566). PALS was partly funded by the TERN ecosystem Modelling and Scaling infrAStructure (eMAST) facility under the National Collaborative Research Infrastructure Strategy (NCRIS) 2013–2014 budget initiative of the Australian Government Department of Industry. Rhys Whitley was supported through the ARC Discovery Grant (DP130101566). Jason Beringer is funded under an ARC FT (FT110100602). We acknowledge the support of the Australian Research Council Centre of Excellence for Climate System Science (CE110001028). We thank Jason Beringer, Caitlin Moore and Simon Scheiter for their permission to reproduce their results in this study.

Publisher Copyright:
© 2017 Author(s).


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