Diagnosing Soil Moisture Anomalies and Neglected Soil Moisture Source/Sink Processes via a Thermal Infrared-based Two-Source Energy Balance Model

Wednesday, 17 December 2014: 9:15 AM
Christopher Hain, University of Maryland, CMNS-Earth System Science Interdisciplinary Center, College Park, MD, United States, Wade T Crow, Hydrol and Remote Sensing Lab, Beltsville, MD, United States, Martha C. Anderson, USDA ARS, Pendleton, OR, United States and M Tugrul Yilmaz, Middle East Technical University, Civil Engineering, Ankara, Turkey
Atmospheric processes, especially those that occur in the surface and boundary layer, are significantly impacted by soil moisture (SM). Due to the observational gaps in the ground-based monitoring of SM, methodologies have been developed to monitor SM from satellite platforms. While many have focused on microwave methods, observations of thermal infrared land surface temperature (LST) also provides a means of providing SM information. One particular TIR SM method exploits surface flux predictions retrieved from the Atmosphere Land Exchange Inverse (ALEXI) model. ALEXI uses a time-differential measurement of morning LST rise to diagnose the partitioning of net radiation into surface energy fluxes. Here an analysis will be presented to study relationships between three SM products during a multi-year period (2000-2013) from an active/passive microwave dataset (ECV), a TIR-based model (ALEXI), and a land surface model (Noah) over the CONUS. Additionally, all three will be compared against in-situ SM observations from the North American Soil Moisture Database.

The second analysis will focus on the use of ALEXI towards diagnosing SM source/sink processes. Traditional soil water balance modeling is based on one-dimensional (vertical-only) water flow, free drainage at the bottom of the soil column, and neglecting ancillary inputs due to processes such as irrigation. However, recent work has highlighted the importance of secondary water source (e.g., irrigation, groundwater extraction, inland wetlands, lateral flows) and sink (e.g., tile drainage in agricultural areas) processes on the partitioning of evaporative and sensible heat fluxes. ALEXI offers a top-down approach for mapping areas where SM source/sink processes have a significant impact on the surface energy balance. Here we present an index, ASSET, that is based on comparisons between ALEXI latent heat flux (LE) estimates and LE predicted by a free-drainage prognostic LSM lacking irrigation, groundwater and tile drainage modules. ASSET is compared to existing maps of estimated groundwater depth, open water fraction, and irrigated area to provide a preliminary evaluation of the index as a diagnostic tool for evaluating the impact of SM source/sink processes not captured by traditional one-dimensional water balance models.