Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.

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.

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.

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 (?ET) and evaporation (?EE) flux components of the terrestrial latent heat flux (?E), 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 ?ET and ?EE 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, ?ET, 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 ?ET during the wet (rainy) seasons where ?ET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80?% of the variances of ?ET. However, biophysical control on ?ET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65?% of the variances of ?ET, and indicates ?ET 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 ?ET 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.

Evolution of methodology for the study of adaptability and stability in cultivated species.

The GxE interaction only became widely discussed from evolutionary studies and evaluations of the causes of behavioral changes of species cultivated in environments. In the last 60 years, several methodologies for the study of adaptability and stability of genotypes in multiple environments trials were developed in order to assist the breeder's choice regarding which genotypes are more stable and which are the most suitable for the crops in the most diverse environments. The methods that use linear regression analysis were the first to be used in a general way by breeders, followed by multivariate analysis methods and mixed models. The need to identify the genetic and environmental causes that are behind the GxE interaction led to the development of new models that include the use of covariates and which can also include both multivariate methods and mixed modeling. However, further studies are needed to identify the causes of GxE interaction as well as for the more accurate measurement of its effects on phenotypic expression of varieties in competition trials carried out in genetic breeding programs.