Wednesday, November 15, 2006 - 2:00 PM

Estimation of Effective Hydraulic Conductivity using Remotely Sensed Soil Moisture.

Binayak Mohanty, MS 2117, Texas A&M University - Rangeland Ecology & Management, TAMU Biological & Agricultural Eng, 201 Scoates Hall, College Station, TX 77843-2117 and Amor Ines, Texas A&M University, Dept. of Biol. & Agric. Eng., College Station, TX 77843-2117.

Soil hydraulic properties (hydraulic conductivity, water retention) are by far the most important land surface parameters to govern the partitioning of soil moisture between infiltration and evaporation fluxes at a range of spatial scales. However, an obstacle to their practical application in the field, catchment, watershed, or regional scale is the difficulty of quantifying the “effective” soil hydraulic functions θ(h) and K(h), where θ is the soil water, h is the pressure head and K is unsaturated hydraulic conductivity. Proper evaluation of the water balance near the land-atmosphere boundary depends strongly on appropriate characterization of soil hydraulic parameters under field conditions and at the appropriate process scale. Traditionally, process representation in the vadose zone is derived at the pore-scale and later extrapolated to larger scale without proper representation of and accounting for nonlinearity across space and time scales. With the deployment of MODIS and AQUA-E on TERRA and AQUA satellites that collect near-daily evolution of land cover parameters, states and fluxes as well as surface soil moisture on a global scale opens up a new avenue to quantify this critical land surface parameter. We will present a novel scheme to derive “effective” soil hydraulic properties at various remote sensing footprint resolutions by integrating multi-temporal remote sensing data of evapotranspiration and soil moisture, a land surface/vadose zone hydrologic model, and a suite of advanced inverse modeling algorithms such as Genetic Algorithms. We will test our proposed inverse modeling (top-down) approach at three hydro-climatic regions of Iowa, Oklahoma, and Arizona using developed upscaling (bottom-up) approach where supplemental air-borne and ground data are available during the SGP and SMEX field campaigns.