699-39 Data Source and Data Quality Effects on Vadose Zone Transport Modeling.

Wednesday, 8 October 2008
George R. Brown Convention Center, Exhibit Hall E
Marcel G. Schaap1, Mike Tzung-Mow Yao2, Aaron R. Graham3, Zhufeng Fang4, Peter Wierenga5 and Shlomo P. Neuman4, (1)Department of Soil, Water & Environmental Science, University of Arizona, Tucson, AZ
(2)GeoSystems Analysis, Tuscon, AZ
(3)University of Arizona, Tucson, AZ
(4)Hydrology and Water Resources, University of Arizona, Tucson, AZ
(5)Soil Water & Environmental Science, University of Arizona, Tucson, AZ
The ability to accurately model water flow and solute transport through soils and sediments is important for understanding, and managing many agricultural, environmental, and water resource management problems. For reliable modeling results, it is imperative that the relevant processes are implemented insufficient detail and that the boundary conditions (e.g. rainfall or irrigation, evapotranspiration rates and/or groundwater levels) and hydraulic properties and soil chemical properties are known with appropriate accuracy. An integrated field, laboratory and modeling study was conducted at a relatively large, deep vadose zone experimental site (50x50x10 meters) at the Maricopa Agricultural Center (MAC) of the University of Arizona (NSF 0737945). The objectives in this study are: 1) to use a number of different methods to determine soil hydraulic and other soil characteristics of an existing monitoring site 2) use and develop pedotransfer functions to estimate soil hydraulic properties for this site using a range of techniques that require little to a substantial amount of site-data, 3) compare hydraulic properties determined using a variety of methods (field measurement, laboratory measurements and inverse simulations) to asses the variability among these methods, 4) conduct an extensive modeling study in which we establish accurate three-dimensional site model for water and solute transport. Hydraulic properties needed for computer modeling of flow can be obtained through a variety of methods, such as expensive field and laboratory measurements, inverse modeling studies (using observed time series of hydrological variables), but also be estimated from relatively cheap proxy variables such as soil texture and density. Because these methods generally lead to different hydraulic properties, it is likely that transport simulations based on such data will yield different model outcomes.