As our world's water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Direct validation of ET models is challenging due to lack of available observations that are sufficiently representative at the model grid scale (10-100 km). Prognostic land-surface models require accurate a priori information about precipitation patterns, soil moisture storage capacity, groundwater tables, and artificial controls on water supply (e.g., irrigation, dams and diversions, inter-basin water transfers, etc.) to reliably link rainfall to evaporative fluxes. In contrast, diagnostic estimates of ET can be generated, with no prior knowledge of the surface moisture state, by energy balance models using thermal-infrared (TIR) remote sensing of land-surface temperature (LST) as a boundary condition. The LST inputs carry valuable proxy information regarding soil moisture and its affect on soil surface evaporation and canopy stresses limiting transpiration.

This project will use an 11-year (2000 to present) ET and surface flux dataset, generated with the TIR-based Atmosphere-Land-Exchange Inverse (ALEXI) model, in an intercomparison study with various currently available water and energy cycle products over the CONUS (e.g., NLDAS2 ET estimates [from the Noah and Mosaic LSMs]; the MODIS ET dataset and other ET datasets currently used in the LandFlux-EVAL initiative). Additional analysis will focus on the ability of remotely-sensed ET estimates, especially those from ALEXI, to identify ET associated with non-precipitation water sources such as shallow ground water tables and irrigation. The ability to quantify differences in ET and ET variability in regions where these features exist has the potential to aid in the improvement of ET estimation from prognostics LSMs.