Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/1997
Publication Date: N/A
Citation: N/A Interpretive Summary: We are interested in predicting soil-water content on rangelands because, to a large extent, it determines plant and forage production and therefore the capacity of the land to support domestic livestock and wildlife. We currently have mathematical models that work fairly well for specific locations where lots of data are available. This is a great first step but we need to be able to extend this to the large areas that ranchers must manage before it can have a practical impact. This means modifying the models to use less and/or different kinds information and it means simulating areas of land rather than points. Since the variability of soils increases with area, a major challenge is determine how soils can be represented. We investigated how soil water varies at different scales ranging from 12 m2 to 234 km2 to determine how available spatial data could be used to provide input data for soil water models. We found significant variability even at the 12 m2 scale. However, we also found that different soil types, as mapped by soil survey, were relatively homogeneous. We also found that spatial data, such as digital elevation models and Landsat imagery, provided useful information that could be used to stratify the observed variability. Soil water variability also changed with season and we found that in late summer and early fall the entire watershed was relatively uniform. This information is being used to design a range production model that simulates plant production over large areas.
Technical Abstract: There is increasing interest in modeling soil water content over relatively large areas or scales. In general, the spatial variability of soil-water content increases with scale but it is not known how much or at which scales. High spatial variability constrains soil water models by reducing the accuracy of input parameters, calibration and verification data. It may also require representation of soil water in a spatially distributed manner. Soil-water content data were collected at the Reynolds Creek Experimental Watershed at scales ranging from 12 m2 to 2.3 108 m2 to determine how scale affects spatial variability. We found significant spatial variability at the 12 m2 scale, which could be described as random in large scale models. The increase of spatial variability with scale was controlled by deterministic "sources" such as soil series and elevation-induced climatic effects. The satellite derived, soil adjusted vegetation index, showed that spatial variability at the scale of Reynolds Creek (2.3 108 m2) is not random and may have abrupt transitions corresponding to soil series. These results suggest a modeling strategy which incorporates soil series characterized by random spatial variability nested within the larger, elevation-induced climatic gradient. The distinctions between soils and elevations is greatest early in the growing season and gradually diminish as the effects of differential precipitation and snowmelt timing are erased by evapotranspiration until late in the summer, when they virtually disappear. These conclusions are landscape dependent so that representation of spatial variability should be an explicit part of model development and application.