Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/15/2000
Publication Date: N/A
Technical Abstract: Applying 1-dimensional models to spatial variability problems requires some provision for accommodating the additional 2 dimensions in the horizontal plane. However, accomplishing that step illustrates the main problem, which is shortage of input data that describes the conditions at the various points in space. The experiences of the authors in extending modeling from mthe vertical to the spatial dimensions include classical data collection a multiple points in space, inference of input data from inherently spatial data collection activities (remote sensing, on-the-go sensors), inference of output data from similar sources used to adapt the state variables in the model to improve fit, and objective parameterization to maximize fit between outputs and observations. The further issue of temporal variability in the presence of spatial variability will be included. Examples of each approach will be discussed, and opinions expressed as to promising directions that might be taken to improve success of modeling under spatially variable conditions.