|Norman, John - UNIVERSITY OF WISCONSIN|
|Mecikalski, John - UNIV. ALABAMA-HUNTSVILLE|
|Otkin, Jason - UNIVERSITY OF WISCONSIN|
Submitted to: Journal of Geophysical Research Atmospheres
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 5, 2007
Publication Date: May 30, 2007
Repository URL: http://handle.nal.usda.gov/10113/59922
Citation: Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A., Kustas, W.P. 2007. A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing. I. Model formulation. Journal of Geophysical Research. 112, D10117. http://dx.doi.org/10.10/29/2006JD007506. Interpretive Summary: Surface temperature, derived from thermal remote sensing, is a valuable metric for constraining models of regional evapotranspiration (ET) because varying soil moisture conditions yield a distinctive thermal signature. Moisture deficiencies in the root zone lead to vegetation stress and elevated canopy temperatures, while depletion of water from the soil surface layer causes the soil component of the scene to heat up rapidly. Several thermal-based ET models have been developed in the past, but they are generally not appropriate for operational ET monitoring at continental scales. Major stumbling blocks in prior studies include bias between surface temperature and air temperature boundary conditions and simplified land-surface representations, both of which can corrupt flux estimates particularly over partial canopy cover. Climatological studies are further limited by thermal data availability – fluxes cannot be modeled directly under cloudy conditions when the ground cannot be seen by the thermal sensor. We have developed a thermal-based ET model that directly addresses these challenges, and a technique for estimating continental-scale fluxes at 5-10km horizontal resolution under both clear and cloudy conditions using data from the NOAA Geostationary Operational Environmental Satellites (GOES). The model also estimates soil moisture conditions in the soil surface and root zone layers, providing information even under dense vegetation cover where microwave techniques typically fail. This paper describes the modeling framework, along with generation of required input data layers for applications over the continental U.S. The flux estimates and gap-filling procedures are tested with a dataset collected with a flux tower network in central Iowa during 2002. Errors in gap-filled fluxes are on the order of 20% at hourly timesteps, and 15% at daily timesteps. The data ingestion and model execution has been fully automated, and the system is in a position to become operational in collaboration with NOAA for GOES data access.
Technical Abstract: Due to the influence of evaporation on land-surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere-Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5-10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear-sky conditions when the surface is visible to the satellite sensor, leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap-filling algorithm reproduces observed fluxes with reasonable accuracy, yielding ~20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap-filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real-time. A companion paper presents a climatological evaluation of ALEXI-derived evapotranspiration and moisture stress fields for the years 2002-2004.