ALBERT

Description: Savanna ecosystems cover 20 % of the global land surface and account for 25 % of global terrestrial carbon uptake. They support one fifth of the world's human population and are one of the most important ecosystems on our planet. Savanna productivity is a product of the interplay between trees and grass that co-dominate savanna landscapes and are maintained through interactions with climate and disturbance (fire, land use change, herbivory). In this study, we evaluate the temporally dynamic partitioning of overstory and understory carbon dioxide fluxes in Australian tropical savanna using overstory and understory eddy covariance measurements. Over a two year period (September 2012 to October 2014) the overall net ecosystem productivity (NEP) of the savanna was 506.2 (±22 SE) g C m−2 yr−1. The total gross primary productivity (GPP) was 2267.1 (±80 SE) g C m−2 yr−1, of which the understory contributed 32 %. The understory contribution was strongly seasonal, with most GPP occurring in the wet season (40 % of total ecosystem in the wet season and 18 % in the dry). This study is the first to elucidate the temporal dynamics of savanna understory and overstory carbon flux components explicitly using observational information. Understanding grass productivity is crucial for evaluating fuel loads, as is tree productivity for quantifying the tree carbon sink. This information will contribute to a significant refinement of the representation of savannas in models, as well as improved understanding of relative tree-grass productivity and competition for resources.

Description: 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 maximise 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 5 tower sites along the Northern 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 feed-backs between the plant physiology and vegetation dynamics, mediated by shifting above- vs. 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.

Description: 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.

Description: 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.

Description: Savanna ecosystems are subject to accelerating land use change as human demand for food and forest products increases. Land use change has been shown to both increase and decrease greenhouse gas fluxes from savannas and considerable uncertainty exists about the non-CO2 fluxes from the soil. We measured methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) over a complete wet-dry seasonal cycle at three replicated sites of each of three land uses: savanna, young pasture and old pasture (converted from savanna 5–7 and 25–30 yr ago, respectively) in the Douglas Daly region of northern Australia. The effect of break of season rains at the end of the dry season was investigated with two irrigation experiments. Land use change from savanna to pasture increased net greenhouse gas fluxes from the soil. Pasture sites were a weaker sink for CH4 than savanna sites and, under wet conditions, old pastures turned from being sinks to a significant source of CH4. Nitrous oxide emissions were generally very low, in the range of 0 to 5 μg N2O-N m−2 h−1, and under dry conditions soil uptake of N2O was apparent. Break of season rains produced a small, short lived pulse of N2O up to 20 μg N2O-N m−2 h−1, most evident in pasture soil. Annual cumulative soil CO2 fluxes increased after clearing, with savanna (14.6 t CO2-C ha−1 yr−1) having the lowest fluxes compared to old pasture (18.5 t CO2-C ha−1 yr−1) and young pasture (20.0 t CO2-C ha−1 yr−1). Clearing savanna increased soil-based greenhouse gas emissions from 53 to ~70 t CO2-equivalents, a 30% increase dominated by an increase in soil CO2 emissions and shift from soil CH4 sink to source. Seasonal variation was clearly driven by soil water content, supporting the emerging view that soil water content is a more important driver of soil gas fluxes than soil temperature in tropical ecosystems where temperature varies little among seasons.

Description: A direct relationship between gross ecosystem productivity (GEP) measured by the eddy covariance (EC) method and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) has been observed in many temperate and tropical ecosystems. However, in Australian evergreen forests, and particularly sclerophyll woodlands, MODIS VIs do not capture seasonality of GEP. In this study, we re-evaluate the connection between satellite and flux tower data at four contrasting Australian ecosystems, through comparisons of ecosystem photosynthetic activity (GEP) and potential (e.g. ecosystem light use efficiency and quantum yield) with MODIS vegetation satellite products, including VIs, gross primary productivity (GPPMOD), leaf area index (LAIMOD), and fraction of photosynthetic active radiation (fPARMOD). We found that satellite derived greenness products constitute a measurement of ecosystem structure (e.g. leaf area index – quantity of leaves) and function (e.g. leaf level photosynthetic assimilation capacity – quality of leaves), rather than productivity. Our results show that in primarily meteorological-driven (e.g. photosynthetic active radiation, air temperature and/or precipitation) and relatively aseasonal vegetation photosynthetic potential ecosystems (e.g. evergreen wet sclerophyll forests), there were no statistically significant relationships between GEP and satellite derived measures of greenness. In contrast, for phenology-driven ecosystems (e.g. tropical savannas), changes in the vegetation status drove GEP, and tower-based measurements of photosynthetic activity were best represented by VIs. We observed the highest correlations between MODIS products and GEP in locations where key meteorological variables and vegetation phenology were synchronous (e.g. semi-arid Acacia woodlands) and low correlation at locations where they were asynchronous (e.g. Mediterranean ecosystems). Eddy covariance data offer much more than validation and/or calibration of satellite data and models. Knowledge of the conditions in which flux tower measurements and VIs or other remote sensing products converge greatly advances our understanding of the mechanisms driving the carbon cycle (phenology and climate drivers) and provides an ecological basis for interpretation of satellite derived measures of greenness.

Description: Savanna ecosystems are one of the most dominant and complex terrestrial biomes that derives 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 6 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 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.

Description: Savanna ecosystems are subjected to accelerating land use change as human demand for food and forest products increases. Land use change has been shown to both increase and decrease greenhouse gas fluxes from savannas and considerable uncertainty exists about the non-CO2 fluxes from the soil. We measured methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) over a complete wet-dry seasonal cycle at three replicate sites of each of three land uses: savanna, young pasture and old pasture (converted from savanna 5–7 and 25–30 yr ago, respectively) in the Douglas Daly region of Northern Australia. The effect of break of season rains at the end of the dry season was investigated with two irrigation experiments. Land use change from savanna to pasture increased net greenhouse gas fluxes from the soil. Pasture sites were a weaker sink for CH4 than savanna sites and, under wet conditions, old pastures turned from being sinks to a significant source of CH4. Nitrous oxide emissions were generally very low, in the range of 0 to 5 μg N2O-N m−2 h−1, and under dry conditions soil uptake of N2O was apparent. Break of season rains produced a small, short lived pulse of N2O up to 20 μg N2O-N m−2 h−1, most evident in pasture soil. Annual cumulative soil CO2 fluxes increased after clearing, with savanna (14.6 t CO2-C ha−1 yr−1) having the lowest fluxes compared to old pasture (18.5 t CO2-C ha−1 yr−1) and young pasture (20.0 t CO2-C ha−1 yr−1). Clearing savanna increased soil-based greenhouse gas emissions from 53 to ∼ 70 t CO2-equivalents, a 30% increase dominated by an increase in soil CO2 emissions and shift from soil CH4 sink to source. Seasonal variation was clearly driven by soil water content, supporting the emerging view that soil water content is a more important driver of soil gas fluxes than soil temperature in tropical ecosystems where temperature varies little among seasons.

Description: 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.

Description: Clearing and burning of tropical savanna leads to globally significant emissions of greenhouse gases (GHG) although there is large uncertainty relating to the magnitude of this flux. Australia’s tropical savannas occupy over 25 % of the continental land mass and have a potential to significant influence the national greenhouse gas budget, particularly because they are the focus of likely agricultural expansion. To investigate the role of deforestation on GHG emissions, a paired site approach was used. The CO2 exchange was measured from two tropical savanna woodland sites, one that was cleared, and a second analogue site that remained uncleared for a 22 month observation period. At both sites, net ecosystem exchange (NEE) was measured using the eddy covariance (EC) method. Observations at the cleared site was continuous before, during and after the clearing event, providing high resolution data that tracked CO2 emissions through multiple phases of land use change. At the cleared site, post-clearing debris was allowed to cure for 6 months and was subsequently burnt, followed by extensive soil preparation for cropping. Emissions were estimated from the debris fire by quantify the on-site biomass prior to clearing and applying savanna-specific emissions factors to estimate a fire-derived GHG emission. This was added to net CO2 fluxes as measured by the eddy covariance tower giving a total GHG emission of 154 Mg CO2-e ha−1 from a savanna woodland with a total fuel load (above- and below- ground woody debris, course woody debris, litter plus C4 grass fuel) of 40.9 Mg C ha−1. This emission was dominated by debris combustion from the fire event, which was 83 % of the total emission, the remained from soil emissions and decay of debris during the curing period prior to burning. Soil disturbance from ploughing and site preparation for cropping was responsible for almost 10 % of the total emission. Fluxes at the uncleared site were tracked using an additional flux tower for 668 days and over this period, cumulative NEE was −2.1 Mg C ha−1, a net carbon sink. Estimated emissions for this savanna type were then upscaled to provide estimates of the magnitude of emissions from any future deforestation. At current rates of deforestation, savanna burning is as significant a source of GHG emissions as deforestation, with fire emissions occurring every year across this savanna biome. However, expanded deforestation could exceed fire emissions and a clearing scenario was examined which suggested clearing over and above current rates could add up to 5 % to Australia’s national GHG account, depending on the annual rate of deforestation. This bottom-up study provides data that can reduce uncertainty associated with land use change for this extensive tropical ecosystem and provide an assessment of the relative magnitude of GHG emissions from savanna burning and deforestation as well as informing northern land use decision making processes.