Improving evapotranspiration estimation in pasture and native vegetation models using flux tower data, remote sensing and global optimisation

GRASP is a biophysical model of soil water balance, pasture growth and animal production developed for northern Australian grasses in wooded and non-wooded systems. The intention of this work is to improve predictions from the GRASP model of evapotranspiration, soil water balance and subsequent pasture biomass and cover in tree-grass systems. This work feeds into the operational modelling system of GRASP that is disseminated through the FORAGE and AussieGRASS online systems, available at the Long Paddock website (https://www.longpaddock.qld.gov.au/forage). The GRASP model operates at 3 different scales: Cedar GRASP (paddock scale), FORAGE (property scale) and AussieGRASS (continental scale for Australia). The Cedar version is used for model development and research on grazing trials in Queensland and the Northern Territory. FORAGE is an online system for Queensland that generates and distributes customised PDF reports with information for individual properties. Currently over 2000 reports are requested per month for use by extension providers (government and private), consultants (valuers, agents), researchers (universities and government) and land managers. AussieGRASS products are currently used within the Queensland government to assist with drought declaration assessments and a monthly Climate Outlook and Review delivered through https://www.usq.edu.au/research/environmental-sciences/qdmc-drought This paper documents the parameterisation and improvements to GRASP for estimating evapotranspiration in tree-grass systems. GRASP was overestimating the daily rate of evapotranspiration, particularly in wooded systems during the first days after rainfall events, with evapotranspiration often exceeding 1.3 times pan evaporation (Allen et al., 1998). Model partitioning of evapotranspiration into soil evaporation, grass and tree transpiration also needed adjustment to prevent excessive water loss. Incorporating daily measurements of evapotranspiration from TERN flux tower data provides the capacity to evaluate and improve the estimation of evapotranspiration in GRASP. Model changes include incorporation of satellite-derived fractional ground cover index for green and total cover in the understorey and persistent green for foliage projected cover to further improve the modelling by constraining estimates of evapotranspiration components. Combining field data with remotely sensed data and a global optimiser in an automated system provides the ability to inform model parameterisation and evaluation. Improving evapotranspiration modelling improves the soil water balance, pasture growth, tree-grass competition and safe carrying capacity, where animal numbers are matched to available pasture. Implications for these model changes and evaluation are significant, as this improves our capacity to model grazing land management issues such as runoff, export of sediment to the reef and sustainable long-term carrying capacity. Key learnings from the optimisation experiments revealed where the model needed improvements, along with careful consideration of trade-offs in regard to variable weighting when optimising multiple measured data groups (such as soil moisture, evapotranspiration and green cover). Model improvements removed the 'spikes' in daily evapotranspiration, compared well to measured data and reduced estimated tree transpiration. Daily estimates of surface soil moisture from remote sensing platforms can be used in model calibration but first require model processes and parameterisation to be appropriate at daily time steps. Calibration of evapotranspiration at a daily time step has not been tested before with GRASP due to the lack of high quality daily data sets, especially from mixed tree and grass systems. These results demonstrate the improvements in GRASP for estimating daily and monthly evapotranspiration in mixed tree and grass systems

Drought rapidly diminishes the large net CO2 uptake in 2011 over semi-arid Australia

Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO2 and photosynthesis and in-situ flux tower measures. We show the 2010–11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010–11 net CO2 uptake was highly transient with rapid dissipation through drought. The size of the 2010–11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011–12, and was nearly eliminated in 2012–13 (0.08 Pg). We further report evidence of an earlier 2000–01 large net CO2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle

Mulga, a major tropical dry open forest of Australia: Recent insights to carbon and water fluxes

© 2016 IOP Publishing Ltd. Mulga, comprised of a complex of closely related Acacia spp., grades from a low open forest to tall shrublands in tropical and sub-tropical arid and semi-arid regions of Australia and experiences warm-to-hot annual temperatures and a pronounced dry season. This short synthesis of current knowledge briefly outlines the causes of the extreme variability in rainfall characteristic of much of central Australia, and then discusses the patterns and drivers of variability in carbon and water fluxes of a central Australian low open Mulga forest. Variation in phenology and the impact of differences in the amount and timing of precipitation on vegetation function are then discussed. We use field observations, with particular emphasis on eddy covariance data, coupled with modelling and remote sensing products to interpret inter-seasonal and inter-annual patterns in the behaviour of this ecosystem. We show that Mulga can vary between periods of near carbon neutrality to periods of being a significant sink or source for carbon, depending on both the amount and timing of rainfall. Further, we demonstrate that Mulga contributed significantly to the 2011 global land sink anomaly, a result ascribed to the exceptional rainfall of 2010/2011. Finally, we compare and contrast the hydraulic traits of three tree species growing close to the Mulga and show how each species uses different combinations of trait strategies (for example, sapwood density, xylem vessel implosion resistance, phenological guild, access to groundwater and Huber value) to co-exist in this semi-arid environment. Understanding the inter-annual variability in functional behaviour of this important arid-zone biome and mechanisms underlying species co-existence will increase our ability to predict trajectories of carbon and water balances for future changing climates

Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of both the immediate and long-term plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e. droughts and heatwaves) and structural lags in the systems (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 12 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr-1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ-0.73, p<0.01 for NEE, ρ-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual-site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

&amp;lt;p&amp;gt;&amp;amp;#8216;Aerodynamic resistance&amp;amp;#8217; (hereafter r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt;) is a preeminent variable in the modelling of evapotranspiration (ET), and its accurate quantification plays a critical role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The present study investigates the influence of r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; and its relation to LST uncertainties on the performance of three structurally different SEB models by combining nine OzFlux eddy covariance datasets from 2011 to 2019 from sites of different aridity in Australia with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the latent heat flux (LE, energy equivalent of ET in W/m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated using observed flux data across water-limited (semi-arid and arid) and radiation-limited (mesic) ecosystems.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our results revealed that the three models tend to overestimate instantaneous LE in the water-limited shrubland, woodland and grassland ecosystems by up to 60% on average, which was caused by an underestimation of the sensible heat flux (H). LE overestimation was associated with discrepancies in r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; retrievals under conditions of high atmospheric instability, during which errors in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive bias in LST coincides with low r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; and causes slight underestimation of LE at the water-limited sites. The impact of r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; on the LE residual error was found to be of the same magnitude as the influence of errors in LST in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for mesic forest ecosystems indicated minor dependency on r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt; for modelling LE (VIP&amp;lt;0.4), which was due to a higher roughness length and lower LST resulting in dominance of mechanically generated turbulence, thereby diminishing the importance of atmospheric stability in the determination of r&amp;lt;sub&amp;gt;a&amp;lt;/sub&amp;gt;.&amp;lt;/p&amp;gt;</jats:p

The importance of interacting climate modes on Australia’s contribution to global carbon cycle extremes

The global carbon cycle is highly sensitive to climate-driven fluctuations of precipitation, especially in the Southern Hemisphere. This was clearly manifested by a 20% increase of the global terrestrial C sink in 2011 during the strongest sustained La Niña since 1917. However, inconsistencies exist between El Niño/La Niña (ENSO) cycles and precipitation in the historical record; for example, significant ENSO-precipitation correlations were present in only 31% of the last 100 years, and often absent in wet years. To resolve these inconsistencies, we used an advanced temporal scaling method for identifying interactions amongst three key climate modes (El Niño, the Indian Ocean dipole, and the southern annular mode). When these climate modes synchronised (1999-2012), drought and extreme precipitation were observed across Australia. The interaction amongst these climate modes, more than the effect of any single mode, was associated with large fluctuations in precipitation and productivity. The long-term exposure of vegetation to this arid environment has favoured a resilient flora capable of large fluctuations in photosynthetic productivity and explains why Australia was a major contributor not only to the 2011 global C sink anomaly but also to global reductions in photosynthetic C uptake during the previous decade of drought

The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.Peer reviewe