@article {JRN54584,
	title = {Impact of land surface states within the flux footprint on daytime land-atmosphere coupling in two semiarid ecosystems of the Southwestern U.S.},
	journal = {Water Resources Research},
	volume = {52},
	year = {2016},
	abstract = {<p class="rtejustify">Land surface states play important roles in the turbulent exchanges between ecosystems and their overlying atmosphere. Field methods to estimate turbulent fluxes have time-variable source areas, while land surface observations are typically obtained at single plots with a smaller measurement scale. In this study, we characterize land-atmosphere interactions in two semiarid ecosystems in the southwestern U.S. At each study site, we combine the eddy covariance method with a distributed network of soil moisture and temperature sensors, high-resolution imagery of the spatial distribution of vegetation and soil patches, and novel spatiotemporal analyses to characterize the turbulent flux footprint analytically and identify the soil moisture, temperature, and vegetation conditions underlying the eddy covariance measurements. Four methods for aggregating the land surface observations to the scale of the daily flux footprint are tested. Our results reveal a large degree of spatial variability in the footprint, with stronger variations in soil moisture than in soil temperature. Single plot measurements are less reliable than the distributed network in capturing footprint conditions, particularly for soil moisture. Furthermore, a marked improvement is observed in the relations between turbulent fluxes and land surface states for methods capturing the footprint variability. We also identify that the composition of vegetation and soil patches in the time-variable source area affects the relative magnitudes of the turbulent fluxes and the partitioning of evapotranspiration. Our study points to the importance of monitoring the spatial distribution of land surface states (e.g., soil moisture and temperature) and vegetation and soil patches when assessing land-atmosphere interactions.</p>
},
	keywords = {eddy covariance, evapotranspiration, land surface variability, turbulent fluxes},
	doi = {10.1002/2015WR018016},
	url = {files/bibliography/JRN00650.pdf},
	author = {C.A. Anderson and Enrique Vivoni}
}
@proceedings {JRN54524,
	title = {Characterization of shrubland-atmosphere interactions through use of the eddy covariance method and distributed footprint sampling},
	volume = {p. 36},
	year = {2012},
	month = {05/2012},
	publisher = {Wildland Shrub Symposium Proceedings},
	address = {Las Cruces, NM},
	abstract = {The behavior of ecohydrological systems can be assessed from the interactions between ecosystem states and atmospheric fluxes, including water, carbon and energy dynamics. In this study, we present progress on implementing automatic and continuous measurements of soil temperature and soil moisture within the estimated footprints of eddy covariance towers at two shrubland sites in the American Southwest. The two sites are mixed shrubland in southern New Mexico at the Jornada Experimental Range and a desert savanna in southern Arizona at the Santa Rita Experimental Range. Both sites feature a high temporal variability in vegetation greenness due to rainfall seasonality induced by the North American monsoon. In addition, woody plant encroachment at each site has led to a mosaic of different herbaceous and shrub species. We expect the plant cover heterogeneity to significantly impact the eddy covariance flux measurements and that this effect cannot be adequately captured by surface measurements at the tower. Here, we present estimates of the eddy covariance footprint in each shrubland ecosystem and the sensor network design utilized for continuous measurements within each footprint. These designs relied on high--resolution elevation and vegetation classification maps derived from manned and unmanned aerial vehicles. Ultimately, this study will help characterize the ecohydrological dynamics of these shrubland systems as well as the suitability of the eddy covariance method for heterogeneous landscapes.},
	author = {C.A. Anderson and Enrique Vivoni and Nicole Pierini and Ryan Templeton and A. Rango and Andrea S. Laliberte and Srikanth Saripalli}
}
