The Spatial Distribution of K-Factor Values Across a Toposequence and a Soil Survey Map Unit
Rivers and streams are adversely affected by an increase in sedimentation in their waters from eroding land. High sediment loads in streams can bury fish eggs and prevent hatching, increasing nutrients in the water causing algae blooms, or even contaminating the water with heavy metals carried in or...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
DigitalCommons@CalPoly
2012
|
| Subjects: | |
| Online Access: | https://digitalcommons.calpoly.edu/theses/826 https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1871&context=theses |
| Summary: | Rivers and streams are adversely affected by an increase in sedimentation in their waters from eroding land. High sediment loads in streams can bury fish eggs and prevent hatching, increasing nutrients in the water causing algae blooms, or even contaminating the water with heavy metals carried in or on the aggregates. The erodibility of soil is valuable knowledge to all land users so that we may predict soil loss and its potential to pollute streams. This is done by using the Revised Universal Soil Loss Equation (RUSLE). By predicting soil loss from a given landscape, land managers can take mitigation measures. The precision of the current scale available for soil erodibility (K-factor) by the US Department of Agriculture is not useful to small landowners or on a site-by-site basis. In California’s Central Coast, a grassland hillslope toposequence was investigated in a Los Osos-Diablo soil series complex. Geographic information systems software was used for spatial analysis of variation in the K-factor as well as interpolating areas that were not sampled. Analysis of soils’ particle size, infiltration rate, organic matter content, and structure across the toposequence allowed calculation of the soils’ K-factor values. K-factor values for the footslope, backslope, and shoulder were found to be statistically different from one another. All slope position’s average K-factor values were statistically different than the published Los Osos and Diablo series’ K-factor with the exception of the backslope which was not significantly different than Diablo’s K-factor value. The average of all K-factors was found not to be statistically different than the Los Osos’ K-factor but it was statistically different from the Diablo’s soil series K-factor. The USDA K-factors overestimated the predicted soil loss for the study site. |
|---|