Abstract
Abstract. Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.
Highlights
The Amazon rainforest is one of the world’s most extensive natural ecosystems, influencing the Earth’s water, energy, and carbon cycles (Malhi, 2012), and is a major source of global terrestrial evapotranspiration (E) or latent heat flux (Costa et al, 2010; Harper et al, 2014)
This paper aims to leverage this emerging opportunity by exploring data from the Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA) eddy covariance (EC) observations using a novel analytical modeling technique, the Surface Temperature Initiated Closure (STIC) (Mallick et al, 2014, 2015), in order to quantify the biophysical control on λEE and λET over several representative plant functional types (PFTs) of the Amazon Basin
The residuals between gA-STIC and gA-BM13 are plotted as a function of u and u∗ in Fig. 2d with the aim of ascertaining whether significant biases are introduced by ignoring wind and shear information within STIC1.2
Summary
The Amazon rainforest is one of the world’s most extensive natural ecosystems, influencing the Earth’s water, energy, and carbon cycles (Malhi, 2012), and is a major source of global terrestrial evapotranspiration (E) or latent heat flux (λE) (Costa et al, 2010; Harper et al, 2014). Changes in land cover due to conversion of tropical forest to pastures significantly alter the energy partitioning by decreasing λE and increasing sensible heat fluxes (H ) over pasture sites (e.g., Priante Filho et al, 2004). This will lead to severe consequences for the water balance in the region, with changes to river discharge already observed in some parts of the basin (Davidson et al, 2012). Quantifying the critical role of biophysical variables on λE will add substantial insight to assessments of the resilience of the Amazon Basin under global change
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