Location: Watershed Management ResearchTitle: Predicting soil thickness on soil mantled hillslopes Author
|Patton, Nicholas - Idaho State University|
|Lohse, Kathleen - Idaho State University|
|Godsey, Sarah - Idaho State University|
|Crosby, Ben - Idaho State University|
Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/25/2018
Publication Date: 8/20/2018
Citation: Patton, N., Lohse, K., Godsey, S., Seyfried, M.S., Crosby, B. 2018. Predicting soil thickness on soil mantled hillslopes. Nature Communications. 9(1). doi: 10.1038/s41467-018-05743-y.
DOI: https://doi.org/10.1038/s41467-018-05743-y Interpretive Summary: Soil thickness, more often thought of as the depth of soil to bedrock, partly controls the amount of water that can be stored in soil, and is therefore extremely important in arid and semiarid regions. Unfortunately, it is also very difficult to predict on the landscape and requires considerable manual effort to determine the value at just one point. We developed a simple method that allows one to estimate soil thickness for a given, relatively large watershed, based on a few field determinations. The method is based on assumptions about the statistical distribution of soil depth relative to the curvature of the slope. We found that our assumptions were valid for a wide range of watersheds where high resolution (e.g., lidar) data are available. We expect that this new approach will produce soil depth datasets that will improve models for soil carbon, hydrology, weathering and landscape evolution.
Technical Abstract: Soil thickness is a fundamental variable in many earth science disciplines but difficult to predict. We find a strong inverse linear relationship between soil depth and hillslope curvature (r2=0.89, RMSE=0.17 m) at a field site in Idaho. Similar relationships are present across a diverse data set, although the slopes and y-intercepts vary widely. We show that the slopes of these functions vary with the standard deviations (SD) in catchment curvatures and that the mean of the curvature distributions is zero. Our simple empirical model predicts the spatial distribution of soil depth in a variety of catchments based only on high-resolution elevation data and a few soil depths. Spatially continuous soil depth datasets enable improved models for soil carbon, hydrology, weathering and landscape evolution.