Coupling carbon allocation with leaf and root phenology predicts tree-grass partitioning along a savanna rainfall gradient

The relative complexity of the mechanisms underlying savanna ecosystem dynamics, in comparison to other biomes such as temperate and tropical forests, challenges the representation of such dynamics in ecosystem and Earth system models. A realistic representation of processes governing carbon allocation and phenology for the two defining elements of savanna vegetation (namely trees and grasses) may be a key to understanding variations in tree–grass partitioning in time and space across the savanna biome worldwide. Here we present a new approach for modelling coupled phenology and carbon allocation, applied to competing tree and grass plant functional types. The approach accounts for a temporal shift between assimilation and growth, mediated by a labile carbohydrate store. This is combined with a method to maximize long-term net primary production (NPP) by optimally partitioning plant growth between fine roots and (leaves + stem). The computational efficiency of the analytic method used here allows it to be uniquely and readily applied at regional scale, as required, for example, within the framework of a global biogeochemical model. We demonstrate the approach by encoding it in a new simple carbon–water cycle model that we call HAVANA (Hydrology and Vegetation-dynamics Algorithm for Northern Australia), coupled to the existing POP (Population Orders Physiology) model for tree demography and disturbance-mediated heterogeneity. HAVANA-POP is calibrated using monthly remotely sensed fraction of absorbed photosynthetically active radiation (fPAR) and eddy-covariance-based estimates of carbon and water fluxes at five tower sites along the North Australian Tropical Transect (NATT), which is characterized by large gradients in rainfall and wildfire disturbance. The calibrated model replicates observed gradients of fPAR, tree leaf area index, basal area, and foliage projective cover along the NATT. The model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, mediated by shifting above- versus below-ground resources, and not from imposed hypotheses about the controls on tree–grass co-existence. Results support the hypothesis that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.The contributions of V. Haverd and P. Briggs were made possible by the support of the Australian Climate Change Science Program. B. Smith acknowledges funding as an OCE Distinguished Visiting Scientist to the CSIRO Oceans & Atmosphere Flagship, Canberr

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

A test of the optimality approach to modelling canopy properties and CO 2 uptake by natural vegetation

Photosynthesis provides plants with their main building material, carbohydrates, and with the energy necessary to thrive and prosper in their environment. We expect, therefore, that natural vegetation would evolve optimally to maximize its net carbon profit (NCP), the difference between carbon acquired by photosynthesis and carbon spent on maintenance of the organs involved in its uptake. We modelled NCP for an optimal vegetation for a site in the wet-dry tropics of north Australia based on this hypothesis and on an ecophysiological gas exchange and photosynthesis model, and compared the modelled CO2 fluxes and canopy properties with observations from the site. The comparison gives insights into theoretical and real controls on gas exchange and canopy structure, and supports the optimality approach for the modelling of gas exchange of natural vegetation. The main advantage of the optimality approach we adopt is that no assumptions about the particular vegetation of a site are required, making it a very powerful tool for predicting vegetation response to long-term climate or land use change

An optimality-based model of the coupled soil moisture and root dynamics

The main processes determining soil moisture dynamics are infiltration, percolation, evaporation and root water uptake. Modelling soil moisture dynamics therefore requires an interdisciplinary approach that links hydrological, atmospheric and biological processes. Previous approaches treat either root water uptake rates or root distributions and transpiration rates as given, and calculate the soil moisture dynamics based on the theory of flow in unsaturated media. The present study introduces a different approach to linking soil water and vegetation dynamics, based on vegetation optimality. Assuming that plants have evolved mechanisms that minimise costs related to the maintenance of the root system while meeting their demand for water, we develop a model that dynamically adjusts the vertical root distribution in the soil profile to meet this objective. The model was used to compute the soil moisture dynamics, root water uptake and fine root respiration in a tropical savanna over 12 months, and the results were compared with observations at the site and with a model based on a fixed root distribution. The optimality-based model reproduced the main features of the observations such as a shift of roots from the shallow soil in the wet season to the deeper soil in the dry season and substantial root water uptake during the dry season. At the same time, simulated fine root respiration rates never exceeded the upper envelope determined by the observed soil respiration. The model based on a fixed root distribution, in contrast, failed to explain the magnitude of water use during parts of the dry season and largely over-estimated root respiration rates. The observed surface soil moisture dynamics were also better reproduced by the optimality-based model than the model based on a prescribed root distribution. The optimality-based approach has the potential to reduce the number of unknowns in a model (e.g. the vertical root distribution), which makes it a valuable alternative to more empirically-based approaches, especially for simulating possible responses to environmental change

An optimality-based model of the dynamic feedbacks between natural vegetation and the water balance

The hypothesis that vegetation adapts optimally to its environment gives rise to a novel framework for modeling the interactions between vegetation dynamics and the catchment water balance that does not rely on prior knowledge about the vegetation at a particular site. We present a new model based on this framework that includes a multilayered physically based catchment water balance model and an ecophysiological gas exchange and photosynthesis model. The model uses optimization algorithms to find those static and dynamic vegetation properties that would maximize the net carbon profit under given environmental conditions. The model was tested at a savanna site near Howard Springs (Northern Territory, Australia) by comparing the modeled fluxes and vegetation properties with long-term observations at the site. The results suggest that optimality may be a useful way of approaching the prediction and estimation of vegetation cover, rooting depth, and fluxes such as transpiration and C02 assimilation in ungauged basins without model calibration

A canopy-scale test of the optimal water-use hypothesis

Common empirical models of stomatal conductivity often incorporate a sensitivity of stomata to the rate of leaf photosynthesis. Such a sensitivity has been predicted on theoretical terms by Cowan and Farquhar, who postulated that stomata should adjust dynamically to maximize photosynthesis for a given water loss. In this study, we implemented the Cowan and Farquhar hypothesis of optimal stomatal conductivity into a canopy gas exchange model, and predicted the diurnal and daily variability of transpiration for a savanna site in the wet-dry tropics of northern Australia. The predicted transpiration dynamics were then compared with observations at the site using the eddy covariance technique. The observations were also used to evaluate two alternative approaches: constant conductivity and a tuned empirical model. The model based on the optimal water-use hypothesis performed better than the one based on constant stomatal conductivity, and at least as well as the tuned empirical model. This suggests that the optimal water-use hypothesis is useful for modelling canopy gas exchange, and that it can reduce the need for model parameterization

Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia

We developed a new algorithm for estimating monthly actual evapotranspiration (AET) based on surface reflectance from MODIS-Terra and interpolated climate data. The algorithm uses monthly values of the Enhanced Vegetation Index (EVI) and the Global Vegetation Moisture Index (GVMI) derived from the MODIS nadir bidirectional reflectance distribution function - adjusted reflectance product (MOD43B4) to scale Priestley-Taylor potential evapotranspiration derived from the climate surfaces. The EVI is associated with evapotranspiration through its relationship with leaf area index. The GVMI allows separation between surface water and bare soil when EVI is low and provides information on vegetation water content when EVI is high. The model was calibrated using observed AET data from seven sites in Australia, including two forests, two open savannas, a grassland, a floodplain and a lake. Model outputs were compared with four year average difference between precipitation and streamflow (a surrogate for mean AET) in 227 unimpaired catchments across Australia. We tested four different model configurations and found that the best results both in the calibration and evaluation datasets were obtained when a precipitation interception term (Ei) and the GVMI were incorporated into the model. The Ei term and the GVMI improved AET estimates in the forest, savanna and grassland sites and in the lake and floodplain sites respectively. The most comprehensive model estimated monthly AET at the seven calibration sites with a RMSE of 18.0 mm mo-1 (22% of the mean AET, r2 = 0.84). In the evaluation dataset, mean annual AET was estimated with a RMSE of 137.44 mm y-1 (19% of the mean AET, r2 = 0.61). The model was able to reproduce the main spatial and temporal patterns in AET across Australia. The main advantages of the proposed model are that it uses a single set of parameters (i.e. does not need an auxiliary land cover map) and that it is able to estimate AET in areas with significant direct evaporation, including lakes and floodplains. Crow

Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias

For instantaneous latent heat flux (λE) estimates from thermal remote sensing data to be useful in the hydrologic sciences, they require integration over longer time frames (e.g., months to years). This is not trivial because thermal remote sensing dat

Preface: OzFlux: a network for the study of ecosystem carbon and water dynamics across Australia and New Zealand

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