Modeling Spatial-Temporal Soil Water and Overland Flow in a Dryland Wheat-Fallow Field using MARIA-GIS.
Timothy Green1, James Ascough2, Robert Erskine2, Bruce Vandenberg1, and Lajpat Ahuja2. (1) USDA-ARS-NPA, Great Plains Systems Research Unit, 2150 Centre Ave., Bldg. D, Suite 200, Fort Collins, CO 80526, (2) USDA, Agricultural Research Service (ARS), 2150-D Centre Avenue, #200, Fort Collins, CO 80526
Crop production and environmental fluxes vary in space and time and over a range of scales in agricultural systems. Process interactions between soil hydrology, plant growth and development, nutrient cycling and chemical transport are tightly coupled such that the soil water dynamics reflect the crop status, and vice versa. Such complex interactions are explored using a vertically complex agricultural systems model (MARIA – Management of Agricultural Resources through Integrated Assessment, based on the Root Zone Water Quality Model, and the DSSAT 3.5 crop growth model) with kinematic wave overland flow between delineated land units. A hierarchy of land units (LU's) allows simulation of runoff and run-on along flow paths. The model is applied to a dryland wheat-fallow field in eastern Colorado, USA that is strip cropped, such that a crop is growing every year on approximately half of the whole 109-ha field. Data collection since 2001 includes basic meteorological variables at one location, rainfall rate at five locations, soil and canopy-level air temperature at multiple locations, spatial crop grain yield from a calibrated yield monitor, nested spatial samples of plant emergence, development and biomass, synoptic maps of surface (top 300 mm) soil water content on several dates, 18 profiles of hourly soil water content primarily along two transects, edge-of-field surface water runoff events, and distributed soil texture. Land units of a few ha each are delineated based on 5-m elevation data (cm vertical accuracy), and spatial data within each LU are scaled up for comparison with simulation results. Issues of model calibration, parameter estimation, and uncertainty analysis will be discussed in a spatial scaling context. In addition, we examine integration of spatial data collection, scaling, and simulation – issues that have important implications for addressing soil degradation and sustainable agricultural management.